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Tech Bros and Tech Woes: A Perspective on Gendered Sociolinguistics in the Tech Industry

Jenny Wang, Madhavi Vivek, Rajana Chhin, Audrey Chung

In the expanding realm of technology, language serves as more than just a communication tool — it is a powerful marker of identity and belonging. Our study delves into gendered linguistic practices within the tech industry, focusing on “tech bro” culture and its impact on female experience and career advancement in this male-dominated field. Through interviews with male and female tech students and analysis on social media content, podcasts, and scholarly articles, we uncovered lexical variations and interactional patterns unique to the tech community. Utilizing a blend of qualitative and quantitative methods, we observed terms like “tech debt,” distinct pitch variations, and exaggerated “urban” accents during conversation. Our findings reveal that females in tech often adapt their language to conform to the hyper-masculine expectations of the tech workplace, further reinforcing clear gender biases and stereotypes within the industry. By highlighting these subtle linguistic barriers that perpetuate gender biases, we aim to emphasize the need for a more inclusive and supportive tech environment for all individuals.

Introduction and Background

The technology industry has long been known for its clear gender gap, given its predominantly male sector enforcing stereotypes and biases against women in the workplace. Despite advancements over the years, the tech industry still exhibits pervasive gender bias, often manifested through language. Recently, the term “tech bro” has been used to characterize the toxic masculinity prevalent in the workplace (Jones 2022). Our research aims to explore the unique lexicon and interactional patterns of this group, observing how they reinforce gender stereotypes and impact female experience in tech. In other words, how does the dense network of “tech bros” correlate to their use of vernacular variables? Does gender bias exist within language commonly used by “tech bros?”

Existing literature highlights the significant role of gendered language — the words and phrases that inherently carry gender bias—in shaping workplace dynamics. For instance, terms like “chairman” and “assertive” often assume a male subject, whereas words like “bossy” and “hysterical” are typically applied to females in leadership roles (Holmes 2008). Holmes’ study suggests that women in tech adopt more masculine speech patterns to gain respect and authority, such as using direct imperatives when commanding subordinates or purposefully disengaging from workplace arguments. Despite the growing awareness of these issues, there remains a gap in understanding the specific linguistic behaviors of “tech bros” and their effects on female colleagues.

Our hypothesis claims that the linguistic practices of “tech bros” do contribute to the exclusion of women from the tech sector. This exclusion manifests in daily communication, feedback, perception of qualifications, and performance evaluations. Our research aims to find out whether the male-dominated environment serves as a barrier to community entry and inclusion for women. Additionally, we aim to study the speech variation between genders within the community and its role in building professional relationships. By analyzing tech jargon and interactional patterns, we hope to uncover the subtle and pervasive barriers that gendered language creates in promoting workplace gender equality and diversity.

Methods

In order to conduct our research, our team relied on three main methods for data collection:

Media: We collected quotes from social media portrayals of the stereotypical “tech bro”.
Online Tech Hubs: We gathered data from popular tech communities on platforms like Discord and cross-referenced jargon with scholarly articles.
Interviews: We conducted a total of three sets of interviews with male and female identifying students in the tech industry in order to evaluate of indexical shift as well as gendered language.

Going in order, we consistently evaluated new information against previous data. Further analysis included examining the spectrograms of interview snippets for distinct phonological features.

Results and Analysis

The following section will focus on the results of our study.

1. Media

As per our methodology, one of our primary sources of information was through the portrayal of stereotypical “tech bros” on social media. While this type of content is not the most scientific, it does provide context on societal expectations placed on the average “tech bro.” The comedic video linked here demonstrates several “tech bro” characteristics while mocking common stereotypes, such as remote work culture, cost-of-living in the Bay Area, and popular hobbies and vacation spots within tech communities. Additionally, we observed that the creator of the video speaks with an exaggerated coastal urban accent, similar to those of surfers or “frat bros.” This hyper-masculine accent underscores the inherently masculine nature of “tech bro” culture and serves as a starting point for developing a hypothesis surrounding the existence of a gender gap reflected in language.

2. Online Tech Hubs

Our team visited popular internet communities within the tech industry. The following quotes were taken from a popular Discord server called cscareers.dev, which is a community for student developers seeking internship and new grad opportunities in tech with over 8000 members.

Figure 1. Pictures from a conversation on Discord server cscareers.dev.

From these quotes in Figure 1, we gather that there is a linguistic overlap between tech communities and gamer communities. This would make sense as demographically, they are both male dominated, and technology and gaming share many similarities as fields and industries. Specific jargon like “jank” to describe an interviewer of poor quality and “cracked” to describe extremely accomplished people are two examples of somewhat-expected buzzwords. According to Wiktionary, the term “jank” exists as both computing slang and video game slang, though it is rarely used in technical contexts. The usage of this term colloquially shows the adoption of computing and gamer slang in broader environments. In the same vein, “cracked,” which usually carries video gaming implications, being used to express skill levels outside of gaming also shows the adaptive nature of the jargon.

3. Interview: Indexical Shift

Our first interview was conducted with Brandon and Brian, both third-year cognitive science students at UCLA. Brandon is a product manager intern, and Brian is a software engineering intern. Because the two have an established friendship, I conducted the interview with both participants present to simulate as natural of a conversation as possible. The interview was split into first, a professional chat, and second, a casual conversation in order to evaluate for indexical shifts. Throughout the professional chat, I asked Brian and Brandon questions about their experiences in tech through their professional organizations at UCLA. The following is a transcription of a snippet of the conversation:

Brian: Ensuring like, there’s not a lot of tech debt, I think is like super important across all the projects within DevX as a whole makes everything run very smoothly and there’s no churn.

Brandon: Yeah yeah, for sure. I think that’s a huge thing too, for a lot of projects, is because people are bouncing. Um, people bounce from projects all the time, um having tech debt is just like absolutely horrendous because like people who don’t know anything about the project, for lack of a better term, like a shitload of…

For the casual conversation, I asked the participants to describe updates in their lives, as well as share gossip, as if we were catching up. This is in order to establish the most natural and comfortable environment.

Brandon: ‘Cause I remember, I had- there was something about- but anyway, I saw them together, like they were like, like homies, like they were like, headed to a party together. I was like “oh, like birds of a feather” you know? Shit, like that tracks, that hella tracks, you know? Like I’m not surprised.

Brian: That’s wild.

Generally, I noticed that participants, especially Brandon, deliberately talked slower during the professional chat. During the casual conversation, I found that Brandon tended to stutter more, as well as exhibit doubling in order to emphasize certain phrases. In the professional chat, instead of doubling phrases, he slowed down and replaced those instances with filler words.

Tech jargon was noticeably present during the professional chat, including words like:

Tech debt: shortcuts in code that achieve the goal faster, but at the cost of uglier, harder-to-maintain code.
Churn: the frequent change of resources or team members within a project.
Bounce (rate): the rate at which people leave or exit, typically used in the context of webpage visits.

The following spectrograms closely examine Brandon’s use of the word “shit” in both the casual and professional context.

Figure 2. Spectrogram of Brandon’s use of “shit” in a casual conversation.

Figure 3. Spectrogram of Brandon’s use of the word “shit” in a professional conversation.

Here, we can see that during the casual conversation, there is a significant increase in pitch towards the end of the word, indicating a high rising terminal, which can be especially common amongst young people. In the spectrogram for the same word used in a professional context, Brandon maintains the same pitch. This is likely because a high rising terminal is not situationally acceptable in all professional settings. For example, it is usually not encouraged during job interviews. We can gather that a “tech bro” like Brandon understands this and subconsciously omits high rising terminal in professional contexts.

4. Interview: Gender and Culture

Our second interview was conducted with Jiin Kim, a fourth year Linguistics and Computer Science major who has experience as a software engineer through her internship at Microsoft. As she explained tech-specific language throughout the interview, she defined them in layman’s terms, constantly checking to assure my comprehension of the terms as an outsider to the tech sector. She mentioned how it can be hard to feel included entirely when there is a linguistic barrier made of tech jargon and expressions that often feel very exclusive and not easily accessible to be understood. She herself felt intimidated as an incomer into an established tech workspace. Linguistically, she found herself assimilating into the culture by reflecting speech patterns of those around her—particularly those superior to her. If she finds them to speak more directly, using imperatives, she herself would do the same. Even through a digital space, if she noticed someone using more emoticons in their messages, she would reflect that frequency. As a woman in the tech space, she attributes her not having much experience with discrimination or feeling out of place with the changing ages. As a young college student in an internship program seeking to give more opportunities to a diverse group of tech-workers, she felt well-supported by her company and her peers.

Our last interview was conducted with Matt Nieva, a fourth year Computer Science and Engineering major with experience as the Development Team Director for ACM (Association for Computing Machinery) at UCLA and as a software engineering intern at Cisco. Throughout his interview, he went deep into tech jargon, throwing out multiple technical words while describing tech projects in–depth. When asked about why tech-bro culture revolves so heavily around a specific tech lexicon, he responded saying that tech-jargon is perceived as proof of expertise. There is a certain linguistic exclusivity that makes those who use it hold more authority and credibility. These common words in tech-space also seep into his daily vocabulary as well, signifying to others that he is a member of this tech-bro community.

These are words such as:

Bandwidth: the energy or mental capacity required to deal with a situation
Parallelize: a programming term to adapt a program for running on a parallel processing term
- This however can be used in daily life as setting two thing simultaneously in motion
Velocity: rate of progress; a metric for work that has been done

In efforts to assimilate, he, like Jiin, would mimic the linguistic patterns of those around him. He discovered the concept of downtalking versus uptalking—downtalking being lower in pitch and making a speaker sound more assertive, and uptalking being higher in pitch and making a speaker sound less credible. One reason why downtalking could be perceived as more respectable might be that it is, based on pitch, masculine indexing, while the other is more feminine indexing.

Discussion and Conclusion

As mentioned in the background, tech-bro culture is characterized by an informal, male-centric atmosphere that often perpetuates gender discriminatory behavior and aids in maintaining the gender gap in the tech industry. The establishment of Title IX in 1972 was a turning point for the number of women in STEM, but women still face the leftover effects of this gender power dynamic now in more covert ways. This inequality is visible in how more masculine indexing ways of leadership such as authoritative/assertive styles that assert hierarchical status difference is perceived as more valid of leadership than leading in what is considered more feminine indexing ways such as facilitating group discussion.

Beyond perceived leadership and professionalism, research has found that gender comes to play even within the informal discourse at the workplace. Small talk is a core strategy to foster team and build rapport; however it is culturally coded as feminine specifically when it is not about anything that is work related or distinctly masculine. Research showed that at a female workplace, these pre-meeting talks would last longer and delve into topics such as family, beauty, healthcare, while when studying three different New Zealand IT organizations, small talk was either non-agenda work related or about sports. This may sound trivial but interpersonal discourse is key in fostering work relationships and an overall culture. This difference is visible in humor as well, where a female boss might make a self-deprecating joke to reestablish good relations with her subordinates, workplaces that were more masculine in one meeting consisted of humor that 90% of was sarcastic or negative jibes intended to deflate the addresses. Gender hierarchy in the workplace, particularly within the linguistic space, is covert and embedded subtly into the ways we talk, the ways we perceive authority, etc.

Our findings indicate that the sociolinguistics of the tech industry have unique qualities, including those of which have an effect on the perception of male and female individuals that work in tech. As a male-dominated field, gender bias is highly prevalent in the industry. Our aim was to strengthen this claim in which this bias is confirmed and correlated to female “success” (defined as experience and career advancement), via the exploration of the pattern of language adaptation by females to male tech industry speech. Through this study, we found that the perpetuation of this gender bias is tied to the inherent gender gap that this industry creates in that a male-dominated environment contributes to female exclusion and strict gender-based hierarchy. Our research indicates that this exclusion and hierarchy contributes to the barrier against female success in male-dominated fields such as the tech industry. The continued observation of male-dominated power structures can aid in the effort for people of all genders in high-earning fields. There is thus a need for collective action toward increasingly inclusive, supportive, and sustainable workplace environments, especially the tech industry, for female-identifying people and other minority individuals.

References

Duchene, A., & Heller, M. (2012). Language in late capitalism: Pride and profit. Routledge.

Gleeson, P. (2017). Working with Coders. https://doi.org/10.1007/978-1-4842-2701-5.

Holmes, Janet. Gendered Discourse at Work , 2 Apr. 2008, onlinelibrary.wiley.com/doi/10.1111/j.1749-818X.2008.00063.x/abstract.

Ignatow, G. (2003). “Idea hamsters” on the “Bleeding edge.” Poetics, 31(1), 1–22. https://doi.org/10.1016/s0304-422x(02)00042-6.

Jones, H., & Sudlow, B. (2022). A contemporary history of Silicon Valley as global heterotopia: Silicon Valley metaphors in the French news media. Globalizations, 19(7), 1122–1136.

Moldovan, Andrei. “Technical Language as Evidence of Expertise.” MDPI, Multidisciplinary Digital Publishing Institute, 21 Feb. 2022, www.mdpi.com/2226-471X/7/1/41.

https://www.ucats.org/a-closer-look-at-gender-diversity-in-tech-departments-across-the-usa.html

miinii (@officialmiinii). (2024) “women in computer science stand up!” https://www.tiktok.com/@officialmiinii/video/7342179255356230955.

Tripathi, Pooja (@winnie_thepooj). (2024) “POV: date with a tech bro” https://www.tiktok.com/@winnie_thepooj/video/6962561340086996230?is_from_webapp=1&sender_device=pc&web_id=7279640957828630062.

Nasso, Austin (@austinnasso). (2024) “POV: you’re talking to a tech bro” https://youtube.com/shorts/qmGL0-U2Z58?si=DAulz7gNkYjYpRVH.

https://en.wiktionary.org/wiki/jank#Etymology.

https://en.wiktionary.org/wiki/cracked

gender, gender differences, othering, Slang, tech industry, Workplace Communication

The Power of Language in Human-Animal Relationships

Sociolinguistics

Armine Mkrtchyan

Language is not merely a tool for communication but a fundamental factor that shapes our understanding of the world around us. The words we use when speaking about our surroundings, the symbols we attach to them, and then the meanings we impart play a profound role in sculpting our perceptions. This intricate process of assigning meaning extends notably to our interactions with non-human animals where the language we use plays a significant role in how we perceive, categorize, and consequently treat them. Whether in scientific settings, on farms, or in casual conversations, our choice of language can either humanize or devalue these creatures. It is a complex procedure that bears more weight than many realize. Through the lens of symbolic interactionism, specific linguistic categorizations, and inherent anthropocentric biases, it is clear how language shapes the views of non-human animals and reinforces human superiority within societal hierarchies, ultimately leading to their exploitation. It becomes evident that language is not just a conduit for casual expression but a driving force that molds our views and actions towards the non-human inhabitants of our shared world.

Symbolic interactionism explores the idea that symbols are used to represent the world, all in order to make sense of it (Arluke and Sanders, 1996, p. 9). These symbols are not inherently fixed but are rather subjective, influenced by an individual’s cultural, racial, and experiential background. Despite their subjective nature, these symbols are often collectively agreed upon within specific cultures or societies, shaping a shared understanding of meanings and representations. This is the core idea of E.O. Wilson’s biophilia which highlights the influence of culture on our relationship with the world around us (Herzog, 2010, p. 30). Ochs and Schieffelin’s perspective on socialization adds another layer to the understanding of how our attitudes and behaviors are shaped. They argue that, through the process of language acquisition, individuals also learn cultural meanings and become socialized into a competent member of society (Ochs and Schieffelin, 1984). It is through such a process that we can explain how and why we act in certain ways toward certain things. Our actions and attitudes are driven by the meanings that we have assigned, which have been acquired through culture, but ultimately processed and altered individually.

The way that these meanings are passed around and shared in the first place is through shared symbols. One common symbolic system is language. The words that we assign to different ideas have the power to shape and/or create new meaning associations. Taking animals as an example, there are many metaphors and sayings that affect our perceptions. For example, “It is easier to order a pound of beef from the butcher than a pound of cow” (Herzog, 2010, p. 31). This juxtaposition starkly illustrates the different connotations associated with the two terms. The act of ordering a pound of cow conjures a distressing image, evoking the inhumane treatment and handling of a living being. On the other hand, ordering a pound of beef illustrates a mundane act of ordering food. This simple example demonstrates how words are not just neutral tools for communication. Instead, they are mirrors reflecting our attitudes and moral views.

Moreover, the context from which the terms originate is significant. Different contexts justify distinct language patterns when referring to animals. In biomedical research laboratories that use animals as test subjects, animals are often depersonalized, devoid of names to maintain a detached, clinical approach. This practice ensures that researchers do not form emotional attachments to the subjects of their experiments (Birke, 2007). Similarly, in the realm of factory farming, dehumanizing and objectifying terms are used to refer to animals, reducing them to mere commodities. As seen in the documentary titled Death on a Factory Farm, employees of the Wiles hog farm used degrading terms when speaking to or about the animals they were about to painfully slaughter, serving as a precursor to the violence that would soon ensue (Simon, 2009). In one incident, an employee labels one of the piglets as a “stupid little pig” moments before smashing its head against a wall to “let it bleed out” (Simon, 2009). In another instance, another employee says that he would like to, “shoot the motherfucker [because he hates] pigs” (Simon, 2009). Such derogatory terms are most often not used when speaking about household pets who have assumed the roles of family members. The specific words used to describe animals in these contexts can either evoke neutral, negative, or positive connotations, thereby shaping the societal attitudes and treatment of these beings accordingly.

By framing animals in language that carries negative connotations, an inherent anthropocentric view is constructed. “Anthropocentrism … refers to the related idea that humanity is and should be the measure of all things” (Noske, 2007, p. 78). In this perspective, humans tend to see themselves as separate from the rest of nature, often viewing other living beings and the environment primarily in relation to human interests and needs. This perspective prioritizes human desires, ambitions, and overall well-being above those of other species. Language use further reinforces this idea, perpetuating a social construct that legitimizes human dominance and control over non-human animals. In doing so, humans are unfairly placed at the top of the sociozoological scale by their own standards, relegating non-human animals to a lower status (Arluke and Sanders, 2010, p. 170). This hierarchy becomes evident in the terms and phrases commonly used, many of which carry subtle but profound negative connotations. Words and phrases like “vermin,” “pests,” or “beasts” can connote a sense of inferiority, portraying animals as nuisances or inferior creatures in comparison to humans.

By employing such demeaning language, there comes an inherent distancing or “othering” of these non-human animals, reinforcing the perception of them as entirely separate and less deserving of empathy or consideration. This process of “othering” involves the psychological, social, and linguistic mechanisms by which individuals or groups are marked as inherently different or “other” from the dominant societal group or norm. In the context of animals, this “othering” manifests in various ways, primarily through language and societal attitudes. When animals are labeled with derogatory terms or reduced to negative stereotypes, it consequently reinforces the perception that they are less deserving of moral consideration or ethical treatment. This linguistic distancing perpetuates a societal mindset that wrongfully justifies the exploitation and mistreatment of animals, as they are perceived as inferior or insignificant compared to superior humans.

As a further result of the “othering” of non-human animals comes the widespread abuse and exploitation of these creatures to fulfill human needs and desires. This distancing from animals creates a moral disconnection, allowing for their mistreatment without due consideration for their welfare. Animals are often objectified and reduced to mere commodities or resources, viewed primarily in terms of their utility to humans. This perception of animals as inherently inferior or devoid of significant moral consideration justifies their exploitation across various industries, including agriculture, entertainment, fashion, research, and more. Consequently, as seen in the film Earthlings, animals are subjected to confinement in factory farms, exploited in circuses and entertainment industries, used for their fur or skin in the fashion industry, and subjected to unethical experiments in laboratories (Monson, 2005). The process of “othering” not only justifies but also perpetuates these exploitative practices, creating a cycle where animals are marginalized, their suffering disregarded, and their rights dismissed in favor of human interests and convenience.

The “othering” of other beings solely based on their species, otherwise known as speciesism, is best understood in the context of a familiar concept: racism. Marjorie Spiegel draws an insightful comparison between racism and speciesism, identifying them as two ways of “othering” certain groups (Spiegel, 2007). Both racism and speciesism involve the judgment and discrimination of a group of animals based on perceived differences when compared with the presumed “superior” group. As such, a subjective hierarchical categorization unfolds. This classification leads to the justification of the discrimination, exploitation, and mistreatment of those deemed inferior based on arbitrary characteristics, whether that be race or species. In both ideas, the oppression of the “other” is damaging especially because there is no sound way to decide “who is better than whom” when looking through a strictly evolutionary lens (Spiegel, 2007, p. 238). This goes to show that the differences we have drawn between groups are completely fabricated. They have no sound reasoning behind them, except for made-up ways to put others down to elevate themselves. Consequently, the unwarranted utilization and exploitation of these marginalized groups, whether in industries like factory farming, fashion, or entertainment, stand as morally unwarranted actions.

Yet, the abuse and exploitation of animals continues. The reason for doing so serves one main purpose: to elevate the status of humans. Humans, who already regard themselves as the highest-ranking animals on the sociozoological scale, employ all means necessary to ensure that no other animal comes close to occupying this level (Arluke and Sanders, 2010). To do this, they put other animals down and make sure that they cannot pass them up. One way they do this is by belittling animals. Starting from degrading words such as “treacherous and cowardly” when speaking of dogs, this manifests itself in the lowering of animals’ worth through tangible actions (Spiegel, 2007, p. 235). As a result, animals are often marginalized and viewed as inferior, justifying their unfair treatment and abuse. This is evident in dog fights. In dog fights, dogs are abused to entertain a crowd and to, ideally, boost the ego of male dog owners (Evans et al., 2007). The dogs’ well-being is the last concern of the participants in this show. They can be thrown around, seriously injured, or even killed, but those issues are not weighed as important as the reputation of the dog owners amongst peers. Just this one example is strong enough to display the unfortunate carelessness of humans regarding animals, for the sake of their own status. All in all, it just shows that humans, as a whole, lack basic empathy and ethical consideration of non-human animals.

In stark contrast to the degrading language often used to describe animals, there are instances where language suggests a paradigm shift towards recognizing animals as deserving of empathy, respect, and consideration. One notable example is in the realm of pet ownership, where animals are often treated as beloved members of the family rather than mere possessions. Communities within the FluentPet project exemplify this shift in perception (Wilson, 2021). Here, pet lovers invest significant time and effort into teaching their animals to communicate using specialized soundboards. Each pet is given a name, treated as an individual with distinct preferences and personalities, and afforded the opportunity to express themselves in meaningful ways. In this way, these animals are not only regarded as companions but also as sentient beings capable of forming deep emotional bonds with their human counterparts. The language used within these communities reflects a view of animals as intelligent, deserving creatures with inherent worth. By acknowledging animals’ capacity for communication and understanding, these practices challenge traditional notions of superiority and domination, fostering a more compassionate relationship between humans and animals. Through language that elevates animals to the status of deserving individuals, we begin to reshape societal attitudes and behaviors, paving the way for more ethical treatment of all living beings. For instance, animals on farms may be treated with greater consideration and compassion, even in the processes leading to their death. Take for example Nuria, a cow who was given a name and afforded a more humane end (DW Documentary, 2023). By recognizing animals as deserving of dignity and respect, farmers may adopt practices that prioritize welfare, opting for methods of slaughter that minimize suffering. On this opposite end of the spectrum, language once more emerges as a significant shaper of perceptions.

Exploring patterns in language use toward animals makes it clear that the root of the problem comes from an anthropocentric perspective that employs specific language patterns to maintain a particular order. The meanings attached to the degrading language that is used when speaking or thinking about non-human animals open the doors to their further mistreatment. This language often carries negative connotations, depicting them as inferior or unworthy of empathy. Terms and expressions laden with derogatory or dehumanizing implications not only influence societal perceptions but also establish a framework that legitimizes the exploitation and disregard of these creatures’ well-being. Justifications for their use and abuse in various industries deeply take over. By embedding negative connotations into our language regarding animals, we inadvertently contribute to a mindset that rationalizes their mistreatment, reinforcing a cycle where their suffering is overlooked, their rights diminished, and their exploitation perpetuated, all for the greater purpose of elevating human status. Instances where this is not the case are, unfortunately, few and in between.

All in all, by examining these facets, this paper underscores the intricate relationship between language and human perceptions of animals, emphasizing the significant role of linguistic constructs. Language not only shapes our thoughts but also dictates the actions that come thereafter. Influenced so much by our thoughts and attitudes, our actions reflect the degrading perceptions we have of animals. With these in mind, the wrongful exploitation of animals unfolds with the accompanying wrongful justification of them. As a further consequence of their use and abuse, humans hold on tightly to the idea that their actions are warranted as a means of maintaining their high status on the sociozoological scale. Ultimately, these detrimental consequences stem from the immense power vested in language, underscoring its ability to shape societal attitudes and perpetuate unjust practices towards non-human animals.

References

Arluke, A. & Sanders, C.R. (1996). 9-18 in Regarding Animals (Animals, Culture and Society). Temple University Press.

Arluke, A. & Sanders, C.R. (2010) The Sociozoologic Scale. Regarding Animals (Animals, Culture and Society) (167-186). Temple University Press.

Birke, L. (2007). Into the Laboratory. In Kalof, L. & Fitzgerald, A. (Eds.), The Animals Reader (323-335). Berg Publishers.

DW Documentary. (2023). Factory farming, animal welfare and the future of modern agriculture [Video file]. Retrieved from: https://www.youtube.com/watch?v=6VOqNVt_cmM.

Evans, R., Kalich (Gauthier), D, & Forsyth, C.J. (2007). Dogfighting: Symbolic Expression and Validation of Masculinity. In Kalof, L. & Fitzgerald, A. (Eds.), The Animals Reader (209-218). Berg Publishers.

Herzog, H. (2010). Some We Love, Some We Hate, Some We Eat: Why It’s So Hard to Think Straight About Animals. New York, NY: Harper.

Monson, S. (Director). (2005). Earthlings [Film]. Nation Earth.

Noske, B. (2007). Speciesism, Anthropocentrism, and Non-Western Cultures. In Flynn, C.P. (Ed.). Social Creatures: A Human and Animal Studies Reader (77-87). Lantern Books.

Ochs, E. & Schieffelin, B. (1984). Language acquisition and socialization: Three developmental stories and their implications. In R. Shweder, & R. Levine (Eds.), Culture Theory: Essays on Mind, Self and Emotion (276-320). Cambridge University Press.

Simon, T. (Director). (2009). Death on a Factory Farm [Film]. HBO.

Spiegel, M. (2007). An Historical Understanding. In Flynn, C.P. (Ed.). Social Creatures: A Human and Animal Studies Reader (233-244). Lantern Books.

Wilson, L. (2021). From Clever Hans to Bunny the TikTok Dog: An Exploration into Animal-to-Human Communication. The Macksey Journal, 2(1).

animals, anthropocentrism, Objectification, othering

The TikTok Influencer Voice: Do Sociolinguistic Features Influence the Success of TikTok Videos?

Sociolinguistics

Natalia Adomaitis, Lam Hoang, Maryam Shama, Sydney Trieu, Kristina Zhao

TikTok is a growing social media platform that launched in 2016 and since has gained 1 billion monthly active users, 60% of which are a part of the Gen Z demographic. Many influencers have rapidly grown in popularity, leading to social changes due to trendsetting by these influencers. In our study, we analyzed linguistic features used by three TikTok influencers: Erika Titus, Katie Fang, and Alix Earle. Our goal was to analyze how the use of two linguistic features: filler words and rising intonation (uptalk) by female Gen Z TikTok influencers impact engagement. We gathered data by comparing analytics of 6 videos from each of the three influencers, 3 of which being their most popular videos and 3 being of average performance. We tracked the number of times rising intonation and filler words were used per video along with video length and amount of views and likes. We hypothesized that influencers incorporate these specific linguistic elements to better relate with their audience, which ultimately leads to an increase in video engagement.

Introduction and Background

TikTok has become one of the most popular video-sharing social media apps since its launch in 2016. Approximately 60% of the users and influencers who daily consume or create content on this app belong to Gen Z (Moshin, 2022; Rezek, 2022). In addition, it is predicted that TikTok will have 1.8 billion monthly users by the end of 2024 (Iqbal, 2024).

Because of the rapid growth of this app in recent years, in addition to its daily prevalence in our target demographic of Gen Z individuals (ranging from ages 18-24), and its continuous expected growth, we were motivated to investigate if the verbal linguistic content, or language and speech pattern used by TikTok influencers, had any role or influence in determining the success of their videos.

Our research focused on the sociolinguistic features frequently observed in the language content from popular TikTok influencers: Erika Titus, Katie Fang, and Alix Earle. Specifically, we analyzed upward intonation, filler words, “Valley Girl” speech, and conversational style.

Upward intonation- also known as “uptalk”, a speech pattern where the pitch of a voice rises, is typically used at the end of sentences to indicate a question or throughout a sentence to signal that a phrase is incomplete. This tactic can keep the viewer anticipating what will come next.
Filler words- Examples include “like, um, literally, actually”. Use of this speech pattern maintains the flow of speech, fills pauses in a conversation, thus keeping the viewer’s attention.
“Valley Girl” speech/conversational style- the previously mentioned speech patterns “uptalk” and filler words, are reminiscent of the “Valley Girl” speech and are commonly associated with being used by affluent, middle-class teenagers from California (Nycum, 2018). When used in videos, it can create a casual, one-on-one, conversational-like style that connects with Gen Z.

Erika Titus, Katie Fang, and Alix Earle all have a substantial following with Titus having 4 million followers, Fang with 5.1 million followers, and Earle at 6.8 million followers as well as belonging to the Gen Z demographic. Geographically, Titus along with Earle were born in the U.S. and currently reside in the U.S., and Fang was born in Taiwan, but resides in Canada.

These influencers mainly create lifestyle content such as daily routines, “get ready with me” videos, fashion choices, and product endorsements. Erika Titus and Katie Fang similarly produce casual, at-home, sit-down videos, whereas Alix Earle creates more spontaneous videos in addition to at-home style videos.

Our overall hypothesis is that video success (high like and view count) is attributed to influencers’ use of sociolinguistic features: upward intonation, filler words, and “Valley Girl” speech, which contribute to creating a conversation-like style that is effective at engaging and connecting with the Gen Z audience.

Methods

Our project aimed to investigate the impact of filler words and rising intonation phrases on the success of TikTok influencers’ videos. Success was measured through view count and like count, assessing whether there was a correlation between the frequency of these linguistic features and video engagement success.

To conduct this study, we analyzed six videos from each of the three selected TikTok influencers. For each influencer, three of their best-engaged videos (identified by the highest number of likes and views) were selected, along with three average-performing videos.

Several key factors were tracked for each video. We recorded the number of rising intonation phrases and counted the occurrences of filler words such as “like,” “literally,” and “um.” Additionally, we noted the number of likes and views to measure engagement success and tracked the length of each video in seconds for further analysis. This methodology enabled us to compare the frequency of linguistic features across different levels of video success.

We hypothesized that a higher frequency of filler words and rising intonation phrases would correlate with an increase in views and likes, suggesting these linguistic features enhance engagement.

Results and Analyses

Following our methods, our results are shown below:

From this data, we extract two key metrics: the average frequency of rising intonation and the average frequency of filler words. We focus on the frequency of these elements in terms of occurrences per unit time (e.g., one occurrence every x seconds) rather than their rate (e.g., x occurrences per second). This perspective allows us to contextualize sociolinguistic factors within TikTok videos, which are short-form content designed to capture attention. Following our hypothesis, influencers’ most popular videos tend to employ rising intonation or filler words frequently (e.g., every 5 seconds) to maintain audience engagement.

Based on our hypothesis, we expect that higher average frequencies of rising intonation and filler words are correlated with more likes and views on the videos. We will compare these metrics between popular and less popular videos to potentially reveal their effects on engagement. Initially, we will analyze the data for each influencer individually before aggregating and analyzing the data as a whole.

Erika Titus:

As observed in Erika Titus’s case, her popular videos exhibit an average of one rising intonation every 18 seconds, while her regular videos feature an average of one rising intonation every 9 seconds. Similarly, the frequency of filler words is lower in her popular videos compared to her regular ones. These patterns suggest that for Erika Titus, both rising intonation and filler words appear to have an inverse relationship with engagement, implying that lower frequencies of these features may be associated with higher popularity.

Katie Fang:

Moving on to Katie Fang, we observe a similar pattern to that seen in Erika Titus’s videos. Katie’s popular videos exhibit a lower frequency of rising intonation and filler words compared to her regular videos. Both factors again seem to have an inverse effect on engagement. From the line graphs depicting sociolinguistic factors versus views, it appears that these sociolinguistic features negatively correlate with views, suggesting that lower frequencies may contribute to higher popularity.

Alix Earle:

For Alix Earle, we observe the opposite pattern compared to Erika Titus and Katie Fang. Alix’s popular videos have a higher frequency of both rising intonation and filler words. These factors appear to have a positive effect on engagement. The line graphs depict a positive linear relationship between both rising intonation and filler words with views and likes, suggesting that higher frequencies of these features are associated with increased popularity.

Overall:

Looking at the data across all three influencers, we observe that popular videos generally exhibit a higher frequency of both rising intonation and filler words. These factors appear to have a positive effect on views and likes. However, the line graphs reveal scattered data, and the line of best fit does not clearly indicate a direct relationship, suggesting that other variables may also influence engagement.

Discussions and Conclusions

To summarize, Erika Titus and Katie Fang’s data do not support our hypothesis, but most of the data we collected from Alix Earle’s does support our hypothesis. Given these findings, our data is inconclusive about whether or not rising intonation and the use of filler words have a positive effect on the success of audience engagement on TikTok.

The differences in like and view count in the data between the influencers can be attributed to multiple factors that would need to be taken into consideration and further researched. Elements such as differences in influencers’ backgrounds, the racial identity and/or personality traits of the influencers, video content, and editing style may have played a role in the use of “uptalk” and filler words, which in turn would affect the success of their videos (Swerts, 1998).

Additionally, negative associations with “Valley Girl” speech could have also played a role in causing less engagement from audiences. Use of this speech by individuals who might not sociologically parallel the stereotypes accompanying the linguistic features of the “Valley Girl” accent, such as “being white, privileged”, can raise questions about how authenticity is perceived by the Gen Z audience, as well as reveal structural biases held by audiences on TikTok (Habasque, 2021). In an article by Sophia Smith Gaeler, Gaeler elaborates on the identities associated with these speech patterns and how linguistic features of female TikTok influencers can pave the way for new forms of English to evolve. (Gaeler, 2024).

Furthermore, having a substantial following and audience can potentially cause the influencer to be or appear more anxious, which in turn may lead to the use of more filler words that can also impact the success of a video (Bodie, 2010).

Video length, content, quality, and editing styles could also be investigated. We observed that Alix Earle created more spontaneous content, while Erika Titus and Katie Fang created more at-home content, which may have impacted the relevancy of the content to the viewer. The general public views spontaneous content as a reflection of the influencer’s true self. In a study done by Jacqueline Rifkin and Katherine Du, the participants rated content creators as “more sincere, genuine, and authentic.” (Du and Rikfin, 2023). Therefore, more people would be drawn to influencers that create more spontaneous content, which in turn would influence which particular linguistic features they utilize to engage their audiences.

Editing styles such as “jump cuts” which produce choppy transitions, can result in the cutting off of rising intonation at the end of a video and negatively affect the success of the video. As reported, the effect of these jump cuts can affect our brain’s perception of what we visually and audibly understand as starting or ending (McMullan, 2021). While collecting data, it was observed that Erika Titus and Katie Fang had choppier editing in their videos. Additional research would be needed to determine if content with these factors negatively impacts the quality of the video, in addition to the viewer’s experience, and therefore resulting in a lower like count.

Moreover, other aspects like trends, hashtags, and the TikTok algorithm in addition to the aforementioned factors, can inconclusively support that linguistic styles may have more of an impact on the type of audience influencers attract, rather than audience engagement or video success.

Further research can be done to analyze other linguistic styles in comparison to “Valley Girl” speech and possibly account for linguistic features like word choice and vocal fry, to determine more conclusive results about the positive effects of linguistic features on audience engagement or the prevalence of certain linguistic features across varying categories of content. For example, in an article by Alice Hall on Vice, she discusses how different types of content utilize different linguistic features to capture audiences and maintain their engagement (Hall, 2023).

Other research can be done to investigate how influencers linguistically construct their personal brands, what linguistic techniques or features influencers use to engage with their audience in comments and direct messages, or how influencers use language to build and maintain a community among their followers.

This research and further research’s findings exemplify how language adapts and evolves with the increasing use of digital media and technology. It also contributes to the construction of personal identity and community on digital platforms. Lastly, it can be used to analyze how to engage audiences and keep their engagement from a marketing standpoint.

References

Bodie, G. D. (2010). A Racing Heart, Rattling Knees, and Ruminative Thoughts: Defining, Explaining, and Treating Public Speaking Anxiety. Communication Education, 59(1), 70–105. https://doi.org/10.1080/03634520903443849.

Habasque, P. (2021, December 21). Is creaky voice a valley girl feature? Stancetaking & Evolution of … Anglophonia. French Journal of English Linguistics. https://doi.org/10.4000/anglophonia.4104.

McMullan, J. (2021b, January 21). View of the Great Jump Cut (r)evolution: A case for studying the evolution of vlogging production techniques. https://firstmonday.org/ojs/index.php/fm/article/view/10547/11266.

Mohsin, M. (2022, September 3). 10 TikTok Statistics That You Need to Know in 2019 [Infographic]. Oberlo; Oberlo Dropshipping app. https://www.oberlo.com/blog/tiktok-statistics.

Nycum, R. (2018, May). In Defense of Valley Girl English. In The Compass (Vol. 1, No. 5, p. 4).

Rezek, A. “How Brands Go Viral: An Analysis of Successful Brand Marketing on Tik Tok with Gen Z” (2022). Honors Theses. 2645. https://egrove.olemiss.edu/hon_thesis/2645.

Swerts, M. (1998). Filled pauses as markers of discourse structure. Journal of Pragmatics, 30(4), 485–496. https://doi.org/10.1016/S0378-2166(98)00014-9.

Cross-Referenced Sources

Source 1: Hall, Alice. “Why Does Everyone on TikTok Use the Same Weird Voice?” Vice, 29 Mar. 2023, www.vice.com/en/article/k7zq49/why-everyone-uses-tiktok-voice.

Source 2: Rifkin, Jacqueline, et al. “Research Identifies Why People Prefer Spontaneity in Entertainment.” Phys.org, phys.org/news/2023-06-people-spontaneity.html.

Source 3: Smith Galer, Sophia. “How TikTok Created a New Accent – and Why It Might Be the Future of English.” Www.bbc.com, 23 Jan. 2024, www.bbc.com/future/article/20240123-what-tiktok-voice-sounds-like-internet-influencer.

“Uptalk”, “Valley Girl” Speech, Conversational Style, Filler Words, Gen Z, influencers, Rising Intonation, Sociolinguistic Features, TikTok, Video Success, Viewer Engagement

In-Person vs. Digital Communication Styles Among Classmates

Sociolinguistics

Megu Kondo, Devina Harminto, Yixing Wang, Yinlin Xie, Batool Al Yousif

In the rapidly evolving landscape of communication, the distinction between in-person and digital communication has become a focal point of linguistic and sociocultural studies. This project delves into the nuanced differences in language use, expression, and understanding across these two modes of communication. The purpose of this study is to investigate how individuals adapt language styles, tones, and dialects between in-person and digital communication. Additionally, our study aims to explore these preferences specifically among classmates, shedding light on the nuances of their communication choices. By examining various linguistic features such as informality, use of emojis, turn-taking, and the adaptation to the absence of non-verbal cues in digital platforms, this study illuminates how digital communication often necessitates a shift from traditional language norms observed in face-to-face interactions. We designed a survey using Google Forms for accessibility and ease of distribution and collected data from 30 college students (18-22 years old) who engage in both in-person and digital communication.

Introduction and Background

In the evolving landscape of communication, the distinction between in-person and digital interaction has become a key area of sociolinguistic study. This project investigates how language styles, expressions, and understandings are adapted between these two modes of communication, particularly among college students. The study examines linguistic features such as informality, emoji usage, and turn-taking, shedding light on the nuances of their communication choices.

Traditionally, in-person communication has been valued for its richness and immediacy. This mode allows for a wealth of non-verbal cues such as gestures, facial expressions, and tone of voice, all of which enrich the communication experience and help in accurately conveying emotions. Face-to-face interactions also foster a sense of connection and immediacy, which are often crucial for forming strong interpersonal relationships. On the other hand, digital communication offers unparalleled convenience and flexibility. Platforms like Snapchat, Instagram, and WeChat have revolutionized how we interact, making it possible to maintain relationships over long distances with ease. The digital transformation of communication has brought about significant changes in the linguistic practices of individuals, particularly young adults. The incorporation of multimedia elements such as images, videos, and emojis attempts to bridge the gap in emotional expression that the lack of physical presence creates. However, these elements, while helpful, often fall short of fully replicating the subtleties conveyed through face-to-face interactions. The gap highlights the potential for misunderstanding and the need for enhanced digital literacy in order to navigate the complexities of modern communication.

The primary focus of this study is to assess the preferences of college students (aged 18-22) who regularly engage in both in-person and digital communication. It aims to document their perceived advantages and drawbacks of each mode, particularly in terms of emotional expressiveness and the potential for misunderstandings. Additionally, the study delves into the phenomenon of code-switching — altering one’s language, tone, or style according to the communication platform — which reflects broader cultural identities and adaptability in digital spaces.

Methodology

To examine the differences in communication styles between in-person and digital interactions among college students, we conducted a thorough survey of students aged 18-22. Our methodology ensured that we collected data from a diverse and representative sample, which is critical for drawing meaningful conclusions.

The survey was created with Google Forms, leveraging its accessibility and ease of use to reach a large audience. We divided the survey into three major sections: demographics, communication preferences, and emoji usage. The demographics section gathered basic data such as age, nationality, and primary languages spoken. The communication preferences section explored the participants’ preferred modes of communication, the differences in word and phrase usage between these modes, the ease of conveying emotions, and their awareness of changing language styles or tones. The final section focused on emoji usage, asking about frequency, meanings, and emoji-related misunderstandings. We ensured a diverse participant pool by distributing the survey through social media platforms and student groups, which reached students from a variety of cultural backgrounds and academic disciplines.

Figure 1. Example of a survey question from the section on communication preferences.

The survey responses were automatically compiled into a Google Sheets spreadsheet, allowing for efficient organization and preliminary analysis. We calculated frequencies, percentages, and averages for the quantitative data to summarize demographic information and communication preferences, giving us a clear picture of the data’s major trends and patterns. We used thematic analysis to examine qualitative data, particularly open-ended responses about emoji usage and communication preferences. This process involved coding the responses and identifying common themes, which allowed us to gain a better understanding of the participants’ experiences and perceptions. We used this methodology to gain a thorough and nuanced understanding of how college students navigate the complexities of in-person and digital communication. By analyzing both quantitative and qualitative data, we were able to identify key trends and patterns in communication styles, providing valuable insights into the changing landscape of digital communication. The use of advanced data analysis tools and rigorous thematic procedures ensured that our findings were robust and reflective of the diverse experiences within our sample. This methodology not only provided a strong framework for our research, but it also ensured that our findings were based on a diverse and representative sample, adding to the larger conversation about language use and social interaction among young adults.

Analysis and Results

We were able to attain a total of 30 participants, whose ages were between 19 to 26. Figure 2 shows that the majority of participants were Asians at 73.3%, and the other 26.7% of the participants were American. The results of the survey garnered a better understanding of how college students prefer in-person communication over online conversation.

Figure 2. Chart detailing answers to the question “What is your nationality?”

Figure 3. Chart detailing the responses to the question “What are your preferred social media platforms?”

Figure 3 shows that Instagram is the most popular social media platform among college students with 80%. 24 participants use Instagram to communicate with others since Instagram is very common among the younger generation; people can post pictures and send each other direct messages. 13.3% of the participants like to use WeChat, which is because some of them are international students who can easily communicate with their friends in China through the platform.

Figure 4. Chart displaying whether respondents prefer to talk to classmates online, in person, or either/both.

The data from Figure 4 shows a strong preference for in-person communication among participants, with 50% favoring this way over online interactions or a combination of both because they feel more productive communicating in person. On the other side, 23.3% prefer online communication, reflecting a significant proportion that values the convenience and accessibility of digital platforms. Meanwhile, 26.7% of participants are comfortable with both ways, suggesting a flexible communication preference that could be influenced by situational factors.

Figure 5. Chart displaying respondents’ feelings about how easy it is to convey emotion through text vs. in person.

In-person communication is generally considered to be more effective in conveying emotions than text-based communication, with 80% of respondents preferring face-to-face communication. Only 10% of respondents believe that online platforms are effective in conveying emotions, and online platforms are likely to use GIFs and emojis. This suggests that digital interactions are not perceived to be enough to express emotional states, which may lead to a lack of empathy in conversations. Face-to-face interactions remain crucial for a deeper emotional connection, underscoring the limitations of digital platforms in replicating the richness of in-person exchanges.

Figure 6. Chart representing whether respondents believe emojis can lead to misunderstandings.

43.3% of respondents believe that emojis can lead to misunderstandings, while the same percentage of respondents said they do not. This polarization highlights the ambiguity of emojis, which may be interpreted differently depending on individual circumstances and cultural backgrounds. The remaining 13.3% respondents were unsure of their impact.

Figure 7. Question from emoji usage portion of survey.

Based on the data collected from our survey, it is evident that emoji interpretations vary significantly among users. An analysis of the responses revealed divergent interpretations of several emojis. For instance, the smiley emoji 😃, conventionally associated with happiness and positivity, was perceived by some participants as conveying creepiness. The skull emoji 💀, which typically signifies extreme laughter, frustration, or affection according to Apple’s official description, was interpreted by respondents as representing extreme fatigue or exhaustion, akin to the expression “I am so tired, I am dying.” Moreover, it was also seen as indicating laughter, albeit more intense and devoid of any undertones, compared to 😭. Notably, the 😭emoji has lead to the most misunderstanding in their online conversation compare to others. 😭, which Apple describes as depicting a face with an open mouth, wailing, and shedding streams of heavy tears, symbolizing inconsolable grief or intense emotions such as uncontrollable laughter or overwhelming joy, was commonly misinterpreted. Participants reported using it in adverse situations, with connotations such as “yikes” or “can’t believe my bad luck,” “need help,” and some even perceived it as laughter accompanied by a sense of embarrassment. The findings from our survey underscore the complexity and variability in emoji interpretations among users, which can be quite different from what they are officially meant to represent. This can sometimes lead to confusion in online conversations. It’s important to remember these differences when using emojis, suggesting a need for greater awareness of contextual differences in emoji usage to avoid misunderstandings and make sure our messages are clear.

For additional insights and nuances, the context of the communication also appears to play a critical role in the preferred mode. For instance, for quick updates or logistical arrangements, digital communication is often favored for its efficiency. However, for more complex discussions or sensitive topics, in-person interactions are preferred due to the richer, more nuanced exchange they enable. It is also worth noting that some students reported a gradual shift in their preferences towards digital communication as they adapted to the constraints imposed by recent global events such as the pandemic. This adaptation process reflects the dynamic nature of communication preferences in response to external changes.

Based on the data, the majority of college students still prefer and find it more effective to communicate in person, particularly for emotional interactions. The findings indicate that although some students recognize the advantages of online communication, most still believe that in-person contact is more beneficial, especially when it comes to expressing emotions, and students feel more productive since people can get through everything right away. This outcome contradicts our hypothesis, highlighting the complexity of communication preferences among college students. Further analysis shows that communication preferences may vary significantly based on factors like the student’s academic major, cultural background, and prior exposure to diverse communication platforms.

Discussion

Our findings contradict the idea that generation Z is used to being on online communication platforms. In the article by Janssen (2021), generation Z is often referred to as “digital natives.” They are considered the first generation to grow up entirely in the digital age, influencing their communication habits significantly. They are described as “native speakers” of the digital language of computers, video games, and the internet. Texting and instant messaging are preferred over traditional communication methods like phone calls and emails. Generation Z uses smartphones heavily for personal communication, often choosing texting or messaging apps over voice communication. Despite their preference for texting, generation Z adapts to professional communication norms. For example, they use email extensively at work, despite rarely using it in personal contexts. This indicates flexibility in their communication styles, adapting to different linguistic expectations based on context. Surprisingly, our findings are totally opposite from this perspective of generation Z.

For the limitations of the study, while our study focused on college students aged 18-22, this demographic may not fully represent the broader population’s communication preferences. The age group and educational environment might influence communication styles more specific to this demographic, such as familiarity with digital platforms or developmental aspects related to social interactions. Additionally, the study relies heavily on self-reported data, which can introduce bias. Participants might respond in ways they perceive as socially acceptable or based on their aspirational self-image rather than their actual behavior. This can affect the accuracy of data concerning preferred communication modes and the effectiveness of emoji usage in conveying emotions.

The phenomenon of code-switching, where individuals adapt their language based on the audience’s cultural and linguistic background, further illustrates the adaptability required in digital communication and highlights the ongoing evolution of language use on these platforms. “Another study conducted by Alfaifi (2013) on intrasentential CS in Facebook comments written by 10 Saudi Arabic-English bilinguals found that intrasentential CS was used with gossip and humor, English was used for academic and technical terms, and Arabic was used for religious topics” (Elhija 357). Digital communication often sees a higher incidence of code-switching, where bilingual or multilingual speakers switch between languages or dialects depending on the audience, topic, or platform. While this can showcase linguistic flexibility, it also highlights the challenges of maintaining cultural nuances in digital communication, which may not always provide the contextual cues necessary for appropriate language use. This underscores the persistent value of traditional communication methods in an increasingly digital world and suggests that digital platforms still need to evolve to support the subtleties of human emotion and expression fully.

Conclusion

Our study began with the hypothesis that college students would prefer online communication over in-person interactions, primarily due to the flexibility and reduced social anxiety that digital platforms are presumed to offer. This hypothesis was rooted in the belief that the modern digital environment, with its asynchronous communication and absence of physical presence, might alleviate the pressures associated with face-to-face interaction. The data, however, painted a different picture. Contrary to our expectations, 50% of the participants clearly preferred to communicate with their classmates in person, appreciating the depth of emotion and clarity that comes with real-time, face-to-face interactions. Only 23.3% expressed a preference for online communication. This finding suggests that despite the convenience of digital platforms, they are insufficient for fulfilling all communicative needs, especially those that involve emotional depth and nuance.

The results of our study provide a nuanced understanding of how college students navigate the complexities of digital and in-person communication. While digital platforms offer undeniable convenience and flexibility, our findings suggest that they do not fully replicate the emotional richness and immediacy of face-to-face interactions. Half of the participants expressed a preference for in-person communication, particularly valuing its effectiveness in conveying emotions — a fundamental aspect of human interaction that digital platforms have yet to fully capture. Moreover, our investigation into emoji use revealed a balanced scenario: emojis enhance textual communication by adding emotional depth, yet they also lead to misunderstandings in nearly half of the cases reported. This indicates a significant gap in how emotional nuances are perceived and understood in digital contexts. By realistically assessing these results, we acknowledge that while digital communication technologies have transformed how we connect and interact, they are not a complete substitute for in-person interactions. Instead, they are a complementary medium that requires further refinement to meet the emotional and contextual needs of users fully. Future research should focus on developing and testing new technologies that can better accommodate the complex dynamics of human communication, bridging the gap between the efficiency of digital communication and the emotional depth of face-to-face interactions. As a result, this study not only challenges previous assumptions about digital communication preferences but also enriches our understanding of how young adults toggle between digital and real-world interactions, shaping their social identities and relationships in the process.

References

Brenda Danet, Susan C. Herring, Introduction: the Multilingual Internet, Journal of Computer-Mediated Communication, Volume 9, Issue 1, 1 November 2003, JCMC9110, https://doi.org/10.1111/j.1083-6101.2003.tb00354.x.

Defede, N., Magdaraog, N. M., Thakkar, S. C., & Bizel, G. (2021). Understanding How Social Media Is Influencing the Way People Communicate: Verbally and Written. International Journal of Marketing Studies, 13(2), 1-. https://doi.org/10.5539/ijms.v13n2p1.

Ehrensberger-Dow, M., & Granger, S. (2019). “English as a Lingua Franca in Digital Communication: Diverse Voices, Similar Scripts?” Lexis Journal in English Lexicology, 14, 193-217. https://doi.org/10.4000/lexis.1831.

Elhija, D. A. (2023). Code Switching in Digital Communication. Open Journal of Modern Linguistics, 13, 355-372. https://doi.org/10.4236/ojml.2023.133021.

Psych Minds. (n.d.). Communication: Online vs. Face-to-Face Interactions. Retrieved from https://psychminds.com/communication-online-vs-face-to-face-interactions/.

Schroeder, J. (2019). Two Social Lives: How Differences Between Online and Offline Interaction Influence Social Outcomes. https://escholarship.org/content/qt94n9w8b9/qt94n9w8b9_noSplash_293949a5e051fffc8e1fdcc9ffc168c4.pdf?t=qdtezb.

Zhao, Y. (2021). The Impact of Digital Media on Language Styles and Communication Methods Based on Text, Image, and Video Forms. ResearchGate. https://www.researchgate.net/publication/378739166_The_Impact_of_Digital_Media_on_Language_Styles_and_Communication_Methods_Based_on_Text_Image_and_Video_Forms.

D. Janssen and S. Carradini, “Generation Z Workplace Communication Habits and Expectations,” in IEEE Transactions on Professional Communication, vol. 64, no. 2, pp. 137-153, June 2021, doi: 10.1109/TPC.2021.3069288.

Appendix

Our survey link: https://docs.google.com/forms/d/e/1FAIpQLScCFoDiKMbxxYVWXmxPAEwQIKfTjg8mx1qtZFL9oA55R9WpPg/viewform?usp=sf_link

college students, emojis, Gen Z, social media, Speech Style, texting

Social Media Use Among College Students

Sociolinguistics

Jasmin Carranza, Andres Guzman, Luwuam Haile, Armine Mkrtchyan, Tzlil Pinhassi

Social media plays a significant role in the lives of college students, shaping how they connect, communicate, and express themselves. Given its pervasive influence, it is natural to assume that they would have an understanding of their own language use online. This study works to uncover just that. It specifically explores the linguistic features of grammar and vocabulary use among college students on various social media sites and examines their self-awareness of these patterns. We conducted a survey asking students about their social media usage and perceptions of their language, then analyzed their interactions through provided screenshots. Our findings confirm that students adapt their language to fit the platform’s context: Snapchat and TikTok are characterized by informal language and relaxed grammar, while LinkedIn and Facebook maintain higher formality with complex grammatical structures. Students’ perceptions of their language use closely align with their actual usage, indicating a high level of self-awareness. On platforms like Twitter and Instagram, students correctly estimate their use of informal vocabulary and abbreviations while recognizing the formality of their language on LinkedIn. This research highlights the dynamic nature of language use by college students across social media platforms, showcasing their ability to navigate different communication environments effectively. Our findings underscore students’ awareness of the distinct linguistic norms required by various social networks, adjusting their language accordingly with minimal discrepancy between self-perception and actual use.

Introduction and Background

In today’s dynamic digital age, social media is a premier medium for immediate conversation and communication, significantly influencing how individuals interact and express themselves (Merchant, 2006). Platforms like Instagram, Facebook, Snapchat, Twitter, TikTok, and LinkedIn serve as tools for social interaction and spaces where unique linguistic patterns emerge. While existing research highlights variations in language use across social media, a notable gap exists in understanding how these linguistic features manifest among college students (Kemp et al., 2021). Additionally, there is limited exploration into how these students perceive their language use across different platforms and whether their perceptions align with their usage patterns. This study addresses these gaps by examining grammar and vocabulary use among college students across various social media platforms. By analyzing formality, vocabulary, use of slang, and emoji frequency, this research aims to uncover the differences in language use. Furthermore, it investigates the self-awareness of college students regarding their language use, comparing their perceptions to their actual linguistic behaviors. Thus, our research question is: How do linguistic features, specifically grammar and vocabulary use, vary across the social media platforms of college students, and how do these students’ perceptions of their language use align with their actual usage?

Methods

To explore the linguistic features and self-awareness of language use among college students on various social media platforms, our study employed a mixed-methods approach, combining quantitative surveys with qualitative content analysis. To start, we sent out a survey to students of UCLA who self-reported their language on various social media platforms. Then, participants were asked to provide screenshots of their interactions on each of the social media sites. This allowed for a direct comparison between self-reported data and actual language use. With this information, we were able to confirm or deny each student’s self-perceptions of their online language usage. The provided screenshots were anonymized to protect participants’ privacy.

Building on previous research by Skierkowski & Wood (2012) and Kemp & Clayton (2017), we hypothesized significant variations in vocabulary use, syntax, emoticon usage, and adherence to communication norms across different social networks. By investigating aspects of text messaging, including textese density and response times, for example, we provided a comprehensive understanding of language adaptation within different social circles. Ultimately, the study contributes to the broader comprehension of communication dynamics in the digital age, offering insights into how language is utilized and adapted within college students’ social networks.

Results and Analysis

The results are important in providing insight into the nature of the linguistic choices, not only consciously but also subconsciously, by young adults in digital communication. Taking a deeper dive, we analyze the results from each platform we considered — LinkedIn, Twitter, and Instagram—and provide examples to illustrate these trends.

LinkedIn: Formal Language Use

Appearance in LinkedIn, the professional networking site, invited a thicker coat of calcified tongue for student use of semi-formal scholarly language. This meant, simply: full sentences, big words, industry talk or just a professional sheen to it overall. Analysis revealed students’ self-reports on the language they used were consistent with their actual posts.

Example: In a typical post, a student wrote, “I am thrilled to announce that I have accepted an internship position at Boeing, where I look forward to contributing to the innovative team and developing my professional skills further.”

This example underscores the formal, structured language typical of LinkedIn, reflecting the professional nature of the platform.

Twitter: Casual and Expressive Language

On Twitter, where brevity and timeliness reign, a similar but distinct trend was apparent. Students frequently used slang, abbreviations, and emoticons with whom they clearly identified, as they later recognized and reported in their questionnaires. The students were able to note the spontaneity and personal expression that helped keep their informal tone, well, informal.

Example: A tweet from one of the participants read, “Just saw the weirdest episode of my fave series ever! 😱🤣 Can’t believe what just happened… #mindblown #bingewatching.”

This tweet is representative of the casual and expressive language that defines Twitter, complete with emoticons and hashtags that add a personal touch.

Instagram: Visual and Informal Communication

Instagram, a visually rich platform, also is a part of the informal text conversation game. In sharing their pictures, students were juxtaposing the images with very informal language, often with self-deprecating serializations, and using all sorts of creative text styling. Once again, students’ perceptions of their language use and the actual content analyzed were highly correlated.

Example: An Instagram caption accompanying a beach sunset photo stated, “No filter needed for this sunset 🌅 🌊 #sunsetvibes #beachlife.”

The use of emojis and hashtags enhances the visual experience, reflecting the informal and personal communication style prevalent on Instagram.

The consistency across different platforms suggests that students possess a clear understanding of the appropriate linguistic forms for each social media context. This was particularly evident in their ability to adapt their language to match the formality of the platform, whether in professional settings like LinkedIn or more personal spaces like Twitter and Instagram.

These findings are crucial for understanding the impact of digital communication on college student’s language use and identity construction in the digital age. By demonstrating how students adeptly navigate the linguistic landscapes of various social media, this research contributes significantly to broader discussions about digital literacy and the dynamic nature of language in social media settings.

Discussion and Conclusions

The study aimed to explore the linguistic features of grammar and vocabulary use among college students on various social media platforms and examine their self-awareness of these patterns. Our findings provide valuable insights into how social media environments shape language use and how aware students are of their linguistic behaviors online. One of the key findings of our research is the adaptability of college students’ language based on the context of the platform. On platforms like LinkedIn, which are perceived as professional and formal, students consistently used structured, complex sentences and formal vocabulary. This indicates a clear understanding of the expectations and norms of professional communication. Conversely, platforms such as Twitter and Instagram, known for their casual and expressive nature, saw students employing informal language, including slang, abbreviations, emoticons, and hashtags. Snapchat and TikTok, which emphasize spontaneity and visual content, also reflected relaxed grammar and informal vocabulary. These variations in linguistic styles underline the students’ ability to navigate different communication environments effectively.

Another significant aspect of our study was the alignment between students’ perceptions of their language use and their actual usage. The survey results, paired with the analysis of online interactions, revealed that students accurately estimated their use of informal vocabulary and grammar on platforms like Twitter, Snapchat, TikTok, and Instagram. Similarly, they recognized the formality required on LinkedIn and Facebook. This high level of self-awareness suggests that students are not only aware of the different linguistic norms across social media platforms but also consciously adjust their language to fit these norms. This could be attributed to the fact that social media has a great presence in the lives of college students, making it easy to be familiar with and adapt to its expectations.

The findings of this study also help us understand how digital environments influence social interactions among young adults. The fact that college students are able to alter and modify their languages to fit several different social media platforms demonstrates a form of digital code-switching. There is a clear navigation between linguistic styles and norms, which mirrors larger societal practices of adapting communication styles in several social settings. Students are not only enhancing their digital literacy but also building their online identities that correspond to their desired social persona. As such, this adaptability in language use shows a larger phenomenon of identity formation and management in today’s digital age, where college students as well as other individuals curate their self-presentation across different platforms in online environments.

In conclusion, this research highlights the dynamic nature of language use by college students across social media platforms. Students demonstrate a keen awareness of the distinct linguistic norms required by various social networks and adjust their language accordingly. The minimal discrepancy between their self-perceptions and actual usage underscores their proficiency in navigating digital communication landscapes. These insights contribute to a broader understanding of communication dynamics in the digital age, emphasizing the importance of digital literacy. As social media continues to evolve, further research could explore how these linguistic adaptations and self-awareness develop over time and across different demographic groups. Understanding these patterns can help educators and policymakers create more effective communication skills in digital contexts, preparing students for the multifaceted nature of online interactions.

References

Kemp, N., & Clayton, J. (2017). University students vary their use of textese in digital messages to suit the recipient. Journal of Research in Reading, 40(December 2017), S141–S157. https://doi.org/10.1111/1467-9817.12074

Kemp, N., Graham, J., Grieve, R., & Beyersmann, E. (2021). The influence of textese on Adolescents’ perceptions of text message writers. Telematics and Informatics, 65, 101720. https://doi.org/10.1016/j.tele.2021.101720

Merchant, G. (2006). Identity, Social Networks, and Online Communication. E-Learning and Digital Media, 3(2), 235-244. https://doi.org/10.2304/elea.2006.3.2.235

Raccanello, Paul J. (2011) “Social networking texts among college students: identity and imagination online”. Doctoral Dissertations. 216. https://repository.usfca.edu/diss/216

Skierkowski, D., & Wood, R. M. (2012). To text or not to text? The importance of text messaging among college-aged youth. Computers in Human Behavior, 28(2), 744–756. https://doi.org/10.1016/j.chb.2011.11.023

college students, Linguistic Features, Self-awareness, social media

The Gender Playbook of Stand-Up Comedy

Bilingualism, Sociolinguistics

Shaveon Sisson, Daria Avtukh, Chelsea Garcia, Frieda Lopez Mesina

When it comes to humor, women are typically criticized for being less funny or for trying too hard to be funny. There is a sense of discrimination and inequality when it comes to comedy and gender. Asking the questions, how do sex differences have an effect on comedic styles during stand-up comedy? And, to what extent do female comedians adopt male mannerisms and verbal expressions in their performances? We believed that in order for women to be taken seriously in comedy they possibly had to take up certain character roles in order to fit into their male-dominated industry. Believing that female comedians mimic male comedians, through mimicking in body language, word choice, and pragmatics like pitch in order to be seen as monetarily successful in humor and in the industry, which then phases out as they acquire more experience on stage. Through video observations of 3 different pairs of comedians that include various ethnicities and one sample from male and female groups, we were able to make a discovery about our theory that we did not expect.

Introduction

Aayushi Sanghavi (2019) highlights that gender norms and perceptions of femininity have significantly prevented women from being recognized as “funny.” Historically, patriarchal systems have positioned men superior, including in humor. Research indicates that women’s speech is often perceived as tentative and powerless due to their use of tag questions, uptalk, and diminutive adjectives, while men are more direct and assertive, which applies to humor. This has led to the fallacy that women are not capable of exhibiting aggressive comedy. Most research in this area, conducted by men and focused on men, resulted in a bias associating masculinity with humor. In an industry where ‘funny men’ are celebrated but ‘funny women’ are merely tolerated, the question arises: Do female comedians need to adapt their communication style and stage presence to appear more masculine to be accepted and marketable? Our research focuses on comedians with less than ten years (newbies), ten to fifteen years (mid-career), and fifteen to twenty years (late-career) of professional experience. We will examine a woman and a man in each category, randomizing race, keeping track of filler words, body language, uptalk/pitch, and laughter by observing their performances and analyzing recorded material to gather data for our research.

Methods

To properly analyze comedians’ stand-up videos, we addressed the following questions: How do sex differences affect comedic styles during stand-up? To what extent do female comedians adopt male mannerisms and verbal expressions in their performances? Does this affect audience engagement? Are there differences in body language and vocal delivery between male and female comedians? Our group studied 6 comedians (3 female, 3 male), varying in experience (<10 years, 10-15 years, >20 years) collecting 1 hour of data per person. We watched two Youtube videos to study Ralph Barbosa and Andrea Jin, one Amazon Prime special and one Netflix special to study Mo’Nique, and 6 Netflix specials for Bill Burr, Taylor Tomlinson, and John Mulaney (two/each comedian)1. We analyzed vocal techniques, physical performance, joke content, structure, and timing. Inspired by Weitz’s “Sex differences in nonverbal communication” (1976), we focused on how sex roles affect each comedian’s profession. We identified differences in their rising voice intonation, fillers, body language, and laugh tracks based on their years of experience in the comedy industry. Our research indicates that the longer a comedian is in the industry, the less likely women are to adopt male mannerisms to fit into a male-dominated field, slightly contradicting our initial hypothesis.

Results

Vocal techniques.

Andrea Jin and Ralph Barbosa’s vocal shift was at a steady pace throughout the entireshow and I did not observe any shift between these two comedian styles.
Tomlinson often uses vocal fry and change in pitch to portray characters. Mulaney usesaccents and volume shifts to differentiate characters instead.
Monique uses code-switching and regional dialects from the South and East Coasts of theUnited States, while Bill employs a British accent. Both use uptalk/rising terminal, pitch,
and volume shifts to portray different characters and emphasize the punchline. Physical presence.

Jin’s stage presence was frankly odd; she stayed in one spot throughout the whole show, in which she occasionally moved and showed the same presence. I recognize a similar behavior with Barbosa in which he was very stiff throughout the whole show, stayed in one spot, and once in a while he would sway back and forth.
Tomlinson remains mostly stationary, relying heavily on facial expressions, and only moves when portraying other characters. Mulaney uses his entire body, utilizing the entire stage, even when the joke doesn’t require it.
Mo’Nique seldom remained stationary; she favored a confident stride while she paced. Additionally, she utilizes gestures with her free hand approximately 95% of the time during her spoken communication. Bill Burr uses many gestures, such as kneeling, lunging, using the mic stand as a prop, and mimicking a blow-up doll, as well as utilizing gestures like strangling and kicking to depict violence. Bill accompanies his speech with hand gestures and is in motion about 80% of the time

Jokes content

Jin’s jokes were about personal stories, gender topics, and her transition from China. Barbosa particularly displayed his jokes about drugs, smoking, personal stories, and ethnic background. I noticed that both comedians shared a lot in common with their jokes.
Tomlinson jokes about sex, failed romantic relationships, gender differences, and religious trauma. Mulaney jokes about drug addictions and tells long autobiographical stories. Neither of the two used an excessive amount of filler words.
Mo’Nique incorporates camaraderie into her jokes about her numerous husbands, weight, being Black in Caucasian spaces, her career, and her experiences as a student in special education. Burr displayed traits from the feminine speech community while discussing male feminism, cultural appropriation, cancel culture, white male privilege, and family dynamics. In two hours, he used 115 filler words, 13 discourse markers, and 82 tag questions. They both use a significant amount of expletives.

Structure

Jin’s structure of her jokes was very rushed, she didn’t give the audience a chance to soak in the jokes and moved along very quickly. I did notice that Jin had a lot of extra sentential switches which at the end of every joke she would say “uhhhuh” to let the audience know that she was done. Barbosa on the other hand was slow with his jokes but gave he the audience a chance to soak in the jokes with frequent pauses.

Tomlinson’s jokes are presented in separate sections with noticeable transitions. Mulaneytreats his performance as one long story with many sub stories.
Monique and Burr presented jokes in separate sections. Mo’Nique engages with thecrowd, while Burr uses “ahhh” and “alright” as transitions. Timing.

As I mentioned before Jin rushed her transition between topics in which the structure of her jokes didn’t follow through and there were multiple awkward pauses in between her transitions. Barbosa on the other hand had noticeable transitions but did take a long time to finish a joke.
Both comedians used more pauses later in their careers. Tomlinson would use “anyway”, “so yeah”, “and uh…” right after delivering the punchline to seem nonchalant or to possibly prepare in case the joke doesn’t “land”. Mulaney took noticeably long, dramatic pauses to let the audience laugh.
Although Burr’s timing varies with choppiness, short pauses, and speeding through the jokes, Monique, on the other hand, takes fewer pauses and delivers a slowly timed punchline.

Discussion

The observed patterns have supported our hypothesis only minimally. In early career comedians, Andrea Jin did fit the style of a hypothesis, appearing to have a masculine style. She used arm motions to dissociate body parts, joked about typical gender issues to connect with the female audience. Barbosa, however, maintained a neutral approach, which did not support our gender-based hypothesis. Mid-career comedians Taylor Tomlinson and John Mulaney showed more contrasting styles, Tomlinson being more timid and scripted, while Mulaney was more relaxed and confident. However, surprising findings include Tomlinson’s prevalence in sex-related jokes compared to Mulaney. Our findings possibly suggest that women feel more restricted in their performances, while men have greater confidence to take up more space. However, we soon found contradicting evidence, with Mo’Nique’s performance challenging this idea, since this late-career comedian embraced her feminine sexuality through her attire, confident struts on the stage, and bold delivery of jokes. Contrary to the existing literature on usage of filler words between genders (Laserna, Seih, and Pennebaker), we found that filler words were not used by women, with seasoned comedian Bill Burr using them the most, while others hardly used them. Observing both of the late-career comedians raised questions on whether Mo’Nique felt the need to compensate by her spectacular presentation since she is a double minority as a black woman, and if being a part of a majority (white male) influences the comedic style of a comedian.

Our content study challenges the notion that female comedians must adopt male mannerisms to be seen as competent in the field. While gender norms play a role, factors like parenting styles, personal choices and branding, past experiences, and social conditioning are also influential. Our hypothesis was partially supported as less experienced female comedians showed more masculine behavior, and this decreased after 10 years in the field. Females in their mid-career and late career often joked about sex and porn, unlike beginner female comedians and male comedians. Notably, Bill Burr who has been in the industry for 30 years, used many filler words, compared to beginner performers who instead used more awkward sounds. Future studies should explore larger samples, longer time frames, and in-person observations to provide deeper insight. We found that female and male comedians viewed certain topics differently with women feeling more comfortable joking about sex. While our hypothesis was minimally supported, comedy styles seem to be shaped by socio-cultural background, personal styles, and past experiences, rather than by gender mimicry. Further research is needed to assess how gender dynamics evolve in a comedian’s career trajectory.

References

Binder, M. (Director). (2022). Bill Burr: Live at Red Rocks [TV Special]. Netflix. https://www.netflix.com/

Binder, M. (Director). (2019). Bill Burr: Paper Tiger [TV Special]. Netflix. https://www.netflix.com/

Lakoff, R. (1975). Language and woman’s place. Harper And Row.

Laserna, C. M., Seih, Y.-T., & Pennebaker, J. W. (2014). Um… Who Like Says You Know: Filler Word Use as a Function of Age, Gender, and Personality. Journal of Language and Social Psychology, 33(3), 331-340.

Mercado, K. (Director). (2022). Taylor Tomlinson: Look at You [TV Special]. Netflix. https://www.netflix.com/

Mizejewski, L. (2014). Pretty/Funny. University of Texas Press.
Raboy, M. (Director). (2020). Taylor Tomlinson: Quarter-Life Crisis [TV Special]. Netflix.

https://www.netflix.com/
Ritchart, A., & Arvaniti, A. (2013). The use of high rise terminals in Southern Californian

English. The Journal of the Acoustical Society of America, 134(5), 4198–4198.

https://doi.org/10.1121/1.4831401

Frazier, L. (Director). (2023). Mo’Nique: My Name Is Mo’Nique [TV Special]. Netflix. https://www.netflix.com/

Sanghavi, A. (2019). The Effects of 21st Century Digital Media On the Changing Perceptions of Women’s Humour and Female Comedians. https://doi.org/10.33422/6th.icrbs.2019.07.430

Small, L. (Director). (2004). Mo’Nique: One Night Stand [TV Special]. Amazon. https://www.amazon.com/

Timbers, A. (Director). (2023). John Mulaney: Baby J [TV Special]. Netflix. https://www.netflix.com/

Timbers, A. (Director). (2018). John Mulaney: Kid Gorgeous at Radio City [TV Special]. Netflix.https://www.netflix.com/

Kallstig, A. (2021). Laughing in the Face of Danger: Performativity and Resistance in Zimbabwean Stand-up Comedy. Global Society : Journal of Interdisciplinary International Relations, 35(1), 45–60. https://doi.org/10.1080/13600826.2020.1828295

Weitz, S. Sex differences in nonverbal communication. Sex Roles 2, 175–184 (1976). https://doi.org/10.1007/BF00287250

Appendix A.

“Andrea Jin: Comedy Central Stand up” (2024).” Youtube. “Bill Burr: Live at Red Rocks” (2022). Netflix Special. “Bill Burr: Paper Tiger” (2019). Netflix Special.
“John Mulaney: Baby J” (2023). Netflix Special.

“John Mulaney: Kid Gorgeous at Radio City” (2018). Netflix Special. “Mo’Nique: My Name is Mo’Nique” (2023). Netflix Special. “Mo’Nique: One Night Stand” (2004). Amazon Special.
“Ralph Barbosa: Comedy Central Stand up” (2023). Youtube.

“Taylor Tomlinson: Look At You” (2022). Netflix Special. “Taylor Tomlinson: Quarter-Life Crisis” (2020). Netflix Special.

Gender Inequality, Humor Perception, Language Variety, Speech Communities, Stand-up Comedy

Differences in Gender Expressions Online

Bilingualism, Sociolinguistics

Antoinette Woodson, Sydney Hesel, Paolo Barrientos, Amar Ebrahim, Nada Gad

Our research project functions to explore the different communication patterns and tendencies between males and females on social media. More specifically how these gender dynamics influence the communication styles. Our motivations for the research stem from our own personal experiences with gender stereotypes on social media. Additionally, we wanted to understand the potential sources of miscommunication between generations to further shed light on the different ways individuals express themselves on social media.

Existing studies have provided insight into how gender dynamics influence communication behavior. Differences in language use, emotional expression, and interaction frequency between genders have been shown to be factors influencing communication preference and understanding. To address these questions, the research team collected data from social media posts and comments made by Gen-Z individuals, both celebrities and non-celebrities, across different gender combinations. To further address these areas, our research group collected data from social media posts including comments, gender of commenters, and gender of posters for both celebrities and non-celebrities and across different gender combinations. We analyzed language choice, emoji usage, and patterns in interactions to identify common trends and tendencies within online communication.

Our results revealed definite communication patterns among gendered groups. Females were more likely to use affectionate/emotionally expressive language and frequently compliment physical appearance or express admiration. Males were commonly more rational and material in their communication style as they focused on achievements and tangible qualities. The red heart and fire emojis were the most commonly used among all groups in the study.

Introduction

This project researches differences in how people express themselves online depending on their gender, specifically through examining lexical variation on the social media platform Instagram. The population we are targeting are young adults, male and female ages 18-25, who are active users on the social media platform, Instagram. Our research will focus on studying the communication pattern differences between men and women on Instagram. Many sources and research findings show that men and women express themselves differently online, specifically in lexical variation. In online communication, lexical variation refers to word choice, emoticon use, hashtags, and phrases in interactions with people. We will observe the word choice and variation in expressions depending on the gender of the user, and also observe how it can change depending on what gender they are interacting with. We seek to analyze the differences in the context of comments on self-presentation posts through the comments on these posts, excluding business or advertisement posts. Our central focus is to study the relationship between gender and lexical variation on people’s Instagram. The goal of this study is to determine the difference in how males and females interact and express themselves online.

Background

The target population of the research belongs to the older segment of Generation Z. People belonging to this generation grew up with the internet, constantly communicating through consuming and creating digital media. Research indicates that Gen Zs are more comfortable using technology to communicate, even over face-to-face communication (Bredbenner, 2020). The most commonly used form of communication for this generation is through social media, which influences social norms between the genders. (Ridgeway, 1999). Our research looks at the relationship between gender and language variation, specifically on social media. Literature exploring the language variations in Instagram captions suggests that women were more likely to use polite phrases while men used more assertive language (Sari et al, 2020). The lexical variation between men and women suggests that women use more pronounced, emotional and expressive terms, kinship terms, and hesitation words, while men use more swear and taboo words, and friendship terms (Bamman, 2012). The differences between how men and women communicate online can even be seen through their use of emoticons, with women using emoticons to express feeling and support, while men used them primarily for teasing and sarcasm when in their own gender groups (Wolf, 2004). When both genders were interacting with each other in the same group, the men adopted the female standard of expressing more emoticons (Wolf, 2004). Furthermore, gender differences in lexical variation, more specifically in hashtag selection are mainly in part due to women’s higher levels of self-expression and more emotional interpersonal communication (Ye et al., 2017). Contrarily, men are more oriented toward goals and more inclined to share rational and objective information (Ye et al., 2017).

Methods

In approaching the study we first identified the most efficient way to collect data without skewing the results, while also collecting as much data as possible in order to decrease the likelihood of the results being distorted by a small sample size. To do so, we tasked each member working on the study to find a set of 20 posts from a pool of Gen-Z male and female celebrities, and Gen-Z male and female colleagues/friends. After each member found the posts, they were then tasked with identifying the top 5 comments and the gender of the user posting said comment, totaling to 500 data points. Those data points were then entered into a document, which allowed for the facilitation of isolating groups by gender of the poster and the commenter. With the data now isolated, the team identified patterns that could be used to prompt Chat-GPT-4 to search through the data. The data was then inserted into the model which calculated the quantities of the common keywords and emojis. The organized data provided allowed for convenient and efficient analysis based on the number of tendencies within each demographic. We double-checked 10% of the data to ensure accuracy in counting, and to ensure misspellings or slang versions of words were accounted for.

Results

After completing our data collection and graph, we found that both genders commented more on posts made by their same gender. We also found that females commented the most out of all the user groups with 44% of the data being women commenting the most on other posts done by women. The graph shows the total number of male-on-male comments was 168 with 110 emojis used. The female-on-female comments had a total of 220 and 176 emojis were used. We can see that females use more emojis than males, but males spread their emojis out more throughout comments, whereas women typically stack their emojis more in one comment. The male-on-female comments showed much less interaction with only 38 comments in total but 54 emojis were used. Lastly, the female on male also contained much less engagement with 71 comments and 53 emojis used. The results show that females still engage and interact more with both males and females in online comments on Instagram. We also looked at the top three emojis used by males and females. The most commonly used emojis by females were the heart eyes, heart, and fire emoji, and the male’s most common emojis were heart, laughing, and fire emoji when commenting on males, and heart eyes, heart, and fire emoji when commenting on females. Males tended to adopt the heart-eyes emoji when talking to females.

One of the main subjects we decided to observe and quantify in the data was word choice, and in what contexts words were used most frequently. When females commented on other female’s posts, the most commonly used words were emotional compliments. For example, words expressing feelings of admiration and compliments, such as “beautiful,” “pretty,” “gorgeous,” “stunning,” and, “cute”, appeared in 33% of comments. Additionally, “love” and “lovely” appeared in 22% of comments. The use of “love” only appeared 5 other times in another category, females commenting on male posts. When females commented on male posts, their compliments began to exclude words such as “beautiful,” “pretty,” “gorgeous,” and “stunning,” and were more likely to use “cute” as an emotional compliment. While we cannot prove the cause of why this would happen, we can infer that the level of emotional expression is being limited. Other commonly used words were “ate,” “best,” and “serving,” all of which lean towards a more material expression of admiration, used to express that someone is stylish or confident. Additionally, a tendency that stood out among female commenters was their tendency to add letters onto words. We infer that this is done to add emphasis, for example, “Elllie_rose” comments ”the cat pic you are kiddinggggggg” on a post by user “Rubylyn”, having the same effect as drawn-out words in verbal dialogue. This was done in 8% of all comments made by female users, and only 2% by male users.

When males commented on other males’ posts, they used adjectives that were less emotional, being more material, and more rational. For example, some of their top used words were “fit” and “fitted”, which express praise for the poster’s outfit, which is inherently material.

Additionally, words such as “winning” and “mid”, a slang term expressing mediocracy, exemplify compliments utilizing rational words, rather than emotional words. These comments are still supportive, however lean towards observations suggesting achievement rather than forward admiration. However, this changed when males commented on female’s posts, with their top words changing to “gorgeous,” “cute,” and, “queen”. These words were not used at all on other males’ posts, so male users are more overtly changing their lexical choices when commenting on female posts. This is a contrast to the female commentators, who seem to have dialed back their compliments but did not change their word choice as overtly.

Discussion

Our findings indicate some lexical variation dependent on gender on social media. Females utilize a more expressive and emotional communication style, especially when engaging with other females. Their comments consist of affectionate compliments that boost confidence. This behavior shows the supportive nature of female interactions on digital platforms. Their style changes slightly when commenting on male posts, dialing back the emotional strength of the compliments. Additionally, the language and emojis used by women tend to amplify the emotional intensity of their interaction. Males tend to imply that something is cool or impressive, using more material terms. However, when interacting with women’s posts, men use more affectionate communication. Our findings could be interpreted to reveal how current gender norms and stereotypes influence digital communication. Males’ interactions often center around achievements, while females’ interactions are rich in compliments. Despite our findings, we find it difficult to make concrete conclusions given the limited size of our data. The data could have been skewed by unknown factors, thus any concrete conclusions from this data require further studies of Gen-Z’s tendencies within comment sections on Instagram. Our research into

Instagram comments adds to the understanding of how males and females communicate online, and what changes occur during cross-gender interactions. That being said, we hope our findings and study are able to set the stage for future research to help understand gender norms.

References

Bamman, D., Eisenstein, J., & Schnoebelen, T. (2012). Gender identity and lexical variation in social media. Journal of Sociolinguistics, 18(2), 135–160. https://doi.org/10.1111/josl.12080

Bredbenner, Jamie, and Lisa M. Parcell. “Generation Z: A Study of Its Workplace

Communication Behaviors and Future Preferences.” Generation z: A Study of Its Workplace Communication Behaviors and Future Preferences, Wichita State University, 2020.

Ridgeway, C. L., & Smith-Lovin, L. (1999). The gender system and interaction. Annual Review of Sociology, 25(1), 191–216. https://doi.org/10.1146/annurev.soc.25.1.191

Sari, I. P., Gunawan, W., & Sudana, D. (2020). Language variations in Instagram captions.

Proceedings of the 4th International Conference on Language, Literature, Culture, and Education (ICOLLITE 2020). https://doi.org/10.2991/assehr.k.201215.053

Wolf, A. (2004). Emotional expression online: Gender differences in emoticon use. CyberPsychology & Behavior, 3(5), 827–833. https://doi.org/10.1089/10949310050191809

Ye, Z., Hashim, N. H., Baghirov, F., & Murphy, J. (2017). Gender differences in Instagram hashtag use. Journal of Hospitality Marketing & Management, 27(4), 386–404. https://doi.org/10.1080/19368623.2018.1382415

An Examination of Code-Switching Patterns: Who is More Prone to Code-Switching, Males or Females?

Bilingualism, Sociolinguistics

Jessica Chou, Kelly Yatsko and Alexandra Flores

Every Black, Indigenous and person of color (BIPOC) in America has likely code-switched in social interactions, either consciously or unconsciously. Code-switching, or the practice of alternating between two languages or varieties of language, is common amongst people of color when speaking to other BIPOC individuals as opposed to white Americans in order to reduce stereotypes. Motivated by this notion as well as previous studies on code-switching in young adults, we sought to discover the effects of gender on code-switching frequency in young adults. We examined YouTube videos from channels such as “Cut” and “Jubilee” to examine how often young adults from diverse backgrounds code-switch when discussing a variety of topics, both serious and lighthearted. Our project initially attempted to confirm previous findings which indicate that young women tend to code-switch more frequently than their male counterparts. However, our data revealed that the young men in these videos code-switched as often and even more frequently than young women in certain videos. Our findings reflect the idea that all BIPOC individuals feel the need to code-switch regardless of gender. Acknowledging this finding can aid in breaking down racial and gender stereotypes and improve communication among young adults of different identities.

Introduction and background

Our research project explores the frequency of code-switching amongst young people in an informal setting based on two aspects of their identity. Code-switching is defined as a practice where people alternate between two languages or varieties of language in a given conversation. Our team was motivated by the notion that people of color tend to code-switch between more informal language when speaking to other people of color, otherwise known as their in-group, versus the use of more formal and standardized language with white Americans, or their out-group. We then decided to go further and examine the frequency of code-switching based on gender. Similarly to racial stereotypes, people of different genders often speak differently in accordance with the demeanor they wish to present to others. Through this study, we hope to better understand the effects of both racial and gender stereotypes that can affect young people’s communication styles and the variety of language they choose to use amongst their peers.

It has been previously discovered that people of color tend to alter their language patterns when speaking to white people as a form of social preservation (Baugh 2002). This is part of the problem that motivated our team to examine code-switching in informal social situations amongst peers. In a previous study, it has also been found that code-switching amongst young people is a way to facilitate conversation, switch from topic to topic, and build rapport (Muthusamy 2010). Code-switching was also found on social media when young people are unable to translate between languages, strengthening the idea that code-switching is common amongst young people on informal channels, even those that are written rather than verbal (Almoaily 2023). Additionally, we hoped to study the differences in code-switching between men and women. In a previous study on gender differences in code-switching, it was discovered that while both genders frequently code-switch when speaking to their peers, women generally tended to use code-switched forms of language 5% more frequently than their male counterparts (Kane 2020). Kane also defines two different forms of code-switching: inter-sentential, which is code-switching between sentences, and intra-sentential, which is code-switching within a sentence. These are the two forms of code-switching we decided to search for when gathering data. In line with these studies, we initially sought to prove that when speaking to other young adults about a variety of topics, women code-switch more frequently than their male counterparts.

Methodology

Our team analyzed code-switching among BIPOC young adults communicating with one another in YouTube videos, examining if males or females engage in code-switching more than the other. We hypothesize that females will code-switch more than males due to what we have learned in Communication 188B, such as how Tannen (1990) found that women are more agreeable and potentially more sub-consciously adaptable when communicating due to “rapport”. Additionally, studies have pointed to women being more susceptible to standardizing their speech such as one conducted in America and Britain by Eckert (2012). Our hypothesis is also based on our shared experience of a “white/valley girl” voice many young adults adopt in Los Angeles as college students at UCLA.

Our data comes from YouTube channels, “Jubilee” and “Cut”. These YouTube channels produce content that often examines and confronts identity-based differences between opposing social groups. We have selected these videos because they have high production value, allowing them to source their participants from a broad range of backgrounds. Their videos focus on asking thought-provoking questions that appropriately juxtapose the behaviors of people with different backgrounds and address hard-hitting topics. We analyzed the language and communication styles that young adults participating in the videos use, particularly observing how BIPOC communication style changes when they are speaking with members of their ingroup. In attempts to reduce or weaken stereotypes against themselves, BIPOC individuals will often utilize more formal language and speaking patterns, such as a more assertive/skillful tone, in conversation with their White counterparts, rather than speaking colloquially, or using non-standard grammar patterns. We specifically looked for aspects of language including formality of speech, usage of slang words, and tone.

Results and analysis

In one video we found that 4 men code-switched whereas only 2 women code-switched when interacting with BIPOC. In another video, 4 men code-switched intra-sentially and 2 women code-switched inter-sentially. In two other videos where participants were blindfolded and in a group of their gender only on camera, men still code-switched more than women. In the video, 6 Black women and 1 Asian woman, the women shared having to speak in a “customer service voice” which supports how women are more susceptible to standardization but may maintain their standardized language on camera when communicating with other BIPOC. The video of 6 Black men and 1 White man showed the men voting off the only openly gay man as the secret white man, this could be due to how although he used Black Vernacular English (BVE), he spoke in a softer tone and pitch overall. The differences in code-switching exhibited by the men and women in these communities are partially due to the demeanor they wish to present; men are more likely to use these forms of code-switching to stimulate more of a camaraderie-like environment, while women foster more of a connecting or empathetic one. Our findings did not support our hypothesis and concluded that males code-switch more than females. However, we must acknowledge that the unique setting of the videos may have impacted our conclusion. Further research may clarify other factors in code-switching, including sexuality and gender expression or diverse settings.

Discussion and conclusions

Our findings lead us to suggest that men will code-switch just as much if not more than women definitely reaffirms the presence of this negative stereotype against women suggesting that they are the leaders in exhibiting this behavior. Particularly, we found that men are more likely to use intrasentential forms of code-switching, while women will gravitate more towards intersentential code-switching. Some of the differences in code-switching exhibited by the men and women in these communities is partially due to the demeanor they wish to present; men are more likely to use these forms of code-switching in order to stimulate more of a camaraderie-like environment, while women foster more of a connecting or empathetic one. Understanding that these behaviors are associated with specific genders provides us with some further insight on how language is used to navigate the social world and belonging, in addition to negotiating identity and power dynamics.

In understanding this behavior in both genders, and being mindful of this information, one can supplement it as the call to action by altering their actions in a positive light. This can help break misconceptions about gendered communication behaviors and reduce the stigma associated with code-switching, especially for women who have been unfairly criticized for it in the past. By refuting this stereotype, and refuting the connection between code-switching and femininity or masculinity, we can help promote gender equality. If we are more aware of this, we can foster stronger interpersonal relationships in various social settings, like work, school, etc., because they are operating off less fear of judgment, and more on trust/empathy.

Confidence and authenticity in expression and identity can lead to inclusive communication patterns and practices where people feel validated in being themselves. In attempts to reduce or weaken stereotypes against themselves, BIPOC individuals will often utilize more formal language and speaking patterns, such as a more assertive/skillful tone in conversation with their White counterparts, rather than speaking colloquially or using varying grammatical patterns, such as African American Vernacular English. Since code-switching for BIPOC can be considered a negative stereotype because of the cultural identity conflict and linguistic insecurities that come with it, especially in more marginalized areas, embracing this idea also validates BIPOC individuals; changing this concept into a positive one can be beneficial for them to navigate different linguistic and cultural arenas.

References

Almoaily, M. (2023). Code-switching functions in online advertisements on Snapchat. PLOS ONE, 18(7). https://doi.org/10.1371/journal.pone.0287478

Baugh, J. (2002). Black Linguistics: Language, Society, and Politics in Africa and the Americas. Routledge; 1st edition, 8, 155-168.

Eckert, P. (2012). Three Waves of Variation Study: The Emergence of Meaning in the Study of Sociolinguistic Variation. The Annual Review of Anthropology

Kane, H. (2020). Language Variation: A Case Study of Gender Differences in Wolof-French Codeswitching. International Journal of Language and Linguistics, 8(4), 122-127. https://doi.org/10.11648/j.ijll.20200804.11

Muthusamy, P. (2010). Codeswitching in Communication: A Sociolinguistic Study of Malaysian Secondary School Students. Pertanika Journal of Social Sciences and Humanities, 18(2), 407-415.

Tannen, D. (1990). You Just Don’t Understand: Women and Men in Conversation. Social Interaction in Everyday Life Contemporary.

Lights, Camera, Flirtation: An Analysis of Male and Female Verbal Flirting Techniques, As Represented in 5 Romantic Comedies from 1989–2023

Bilingualism, Sociolinguistics

Sherry Zhou, Amo O’Neil, Jared Ramil, Juliana Rodas, Thalia Rothman

Our paper seeks to analyze the ways in which flirting and romantic communication has changed, in regards to both gender and societal norms. In doing so, we collected and analyzed data regarding the frequencies and distribution of flirting between main characters of five different romantic comedy movies. We collected data pertaining to four variables: frequency of compliments, pitch change from, sexual jokes, and meaningful questions. Our analysis of the data revealed a number of observations indicative of s in present day society. Most significantly, we observed an increase of female or more female presenting characters initiating flirtation over time, a reflection of changing gender norms in society. With the rise of digital media and the Internet, online content such as movies have become more impactful to the way society processes social norms. Our study calls for continued analysis of the reflections of media representations and narratives onto society, and vice versa.

Introduction and Background

The societal obsession with flirtation and romance permeates modern life at every level. It is the subject of conversations with friends, podcasts, television, books and social media posts. When it comes to how exactly to flirt, most resort to clichés like batting eyelashes and using bad pickup lines. Our research aims to analyze the ways in which the media portrays successful romance, specifically in romantic-comedy movies. Through study, we sought to understand societal shifts in romantic dynamics as portrayed in popular media and their potential implications on real-life interactions.

Flirting is a broad category of physical and verbal communication, and men and women often favor very different strategies. Gender divides in courtship can be traced through time — a study of 19th century love letters showed men expressing their passion through poetic language and elaborate vocabulary while women were expected to be reserved and polite (Wyss 2008). Modern studies showed women tend to have a more reserved flirting style, and men tend to be more playful (Hall and Xing 2015). Women were also found to be more polite with the opposite gender (Cabrera 2022), and had more interest in men’s personality traits, while men focused on women’s physical appearance (Apostolou and Christoforou 2020). Finally, gender roles were found to be strongly predictive of flirtation styles among men and women, though men were also strongly influenced by sexual orientation (Clark, Oswald, and Pedersen 2021).

Our research delves into the evolution of verbal flirting styles between male and female characters depicted in enemies-to-lovers romantic comedies from 1989 to 2023. We hypothesized that time would reveal a less strict gender divide in flirting methods; in particular, we expected female leads to be more direct with sexual comments and male leads to give more personality-related compliments and ask more questions.

Methods

Our research seeks to answer the following question: How have male and female verbal flirting styles portrayed in romantic comedies changed over time? In doing so, we watched and analyzed five different romantic comedies that spanned several decades ranging from the 1980s to the 2020s: When Harry Met Sally (1989), 10 Things I Hate About You (1999), The Proposal (2009), Set It Up (2018), and Red, White, and Royal Blue (2023) (LGBTQ+ case study).

While watching these films individuals, we noted the frequencies and distribution of four specific verbal flirting approaches, our variables, expressed by the romantic leads:

Compliments: Film scenes containing romantic leads complimenting each other based on physical attractiveness or personality.
Voice Pitch Changes: Noticeable pitch changes among the romantic interests were documented (i.e., whether a character’s voice went lower or higher), excluding natural vocal changes.
Sexual Comments: Scenes where characters exchanged sexual remarks to each other. (e.g. lewd remarks, provocations)
Questions: Questions that were asked, as a means for the romantic leads to familiarize each other (e.g. discussions of one’s past life, interests)

Our study we utilized the content analysis research method to quantify the occurrence of these four variables between the main characters of each film. Using the criteria above, we documented the frequency of each variable, and labeled which characters expressed them. Next, we determined which flirting style was mostly preferred by either men or women, as well as examining how these patterns have evolved over time. In the case study of “Red, White, and Royal Blue,” the romantic lead, Prince Henry, is portrayed as a “bottom,” indicating his flirting techniques as more feminine. The data from this film was analyzed to determine whether flirting patterns translated to heterosexual relationships.

To visualize our results, we formulated our data into two types of pie charts, both in percentages. As can be seen in Figure 2, the first chart documented gender based distribution in flirting: how frequently each character utilized all flirting styles throughout the film. The second chart, Figure 3, depicted variable distribution, showing how regularly each of the four flirting techniques that we observed was used in each film.

Results and Analysis

The results of our study on the evolution of verbal flirting styles in romantic comedies from 1989 to 2023 reveal significant trends that reflect broader societal shifts. The total instances of verbal flirting varied significantly across the movies, with “Red, White, and Royal Blue” (2023) having the highest number of recorded instances (56), followed by “When Harry Met Sally” (1989) with 42.

Figure 1: Rates of Female Leads Flirting Over Time

As can bee seen in Figure 2, Men consistently had higher instances of flirting than women in heterosexual relationships, indicating a persistent gender disparity. For example, Harry had 30 recorded instances compared to Sally’s 12, and Andrew had 17 instances versus Margaret’s 14. Although there was a slight increase in the ratio of women’s flirting over time, as can be seen in Figure 1, men still dominated the number of recorded instances. “Red, White, and Royal Blue” (2023) stands out as an exception, with a nearly equal distribution of flirting instances between the male leads, Alex and Henry, who had 29 and 27 instances, respectively.

Figure 2: Gender Based Distribution of Flirting

Figure 3: Variable Distribution of Flirting

Our study also revealed trends in the types of flirting used. As can be seen in both figures 2 and 3, meaningful questions (MQ) showed fluctuating popularity, with men generally asking more questions than women. Harry asked the most questions (18), while Andrew asked the fewest (1). Among women, Sally stood out with 11 MQs, the highest recorded for female characters. The use of sexual comments increased over time, becoming more prevalent in films like “The Proposal” and “Red, White, and Royal Blue.” Physical compliments saw a decline, with women rarely complimenting men’s appearances. Sally was the only woman who did so, once, while men also showed a decreasing trend. Conversely, personal compliments became more popular, with “Set It Up” having the highest number of personal compliments among the movies analyzed. The use of pitch changes as a flirting method varied, showing no clear trend over the years.

A closer examination of the data reveals some nuanced patterns. For instance, in “10 Things I Hate About You” (1999), Patrick had 27 total recorded instances of verbal flirting, while Kat had 9. Patrick’s flirting included a mix of MQs, sexual comments, physical and personal compliments, and pitch changes. Kat’s flirting style included MQs and pitch changes, with no recorded sexual comments or physical compliments. In “The Proposal,” Andrew had 17 instances of verbal flirting, with a significant use of sexual comments and pitch changes, while Margaret had 14 instances, using more MQs and pitch changes.

In “Set It Up” (2018), Charlie had 23 recorded instances of verbal flirting, using MQs, sexual comments, and a significant number of personal compliments. Harper had 12 instances, also using MQs and pitch changes, with a balanced mix of flirting styles. “Red, White, and Royal Blue” showed a unique pattern with Alex and Henry almost equally splitting their flirting instances. Both characters used MQs, sexual comments, physical and personal compliments, and pitch changes, reflecting a more balanced and modern portrayal of romantic interactions.

Overall, the study indicates a shift towards more balanced and diverse representations of flirting styles in romantic comedies. While traditional gender roles are still evident, the increasing representation of women engaging in flirting and the balanced portrayal in “Red, White, and Royal Blue” suggest a trend towards more equal interactions. The rise in sexual comments and personal compliments points to a trend towards more open communication in romantic relationships. These results largely reflect the shift in societal gender norms, which have, in recent years, changed to emphasize female empowerment and gender equality. Our results also reflect the ways in which modern day society has become more accepting of previously frowned upon topics, such as homosexuality and sexual intercourse.

Discussion and Conclusion

Our research delves into the evolution of verbal flirting styles in romantic comedies from 1989-2023, revealing significant trends and changes that reflect broader societal shifts. Understanding these changes is crucial because media representations influence real-life perceptions and behaviors. Relationships are fundamental to our lives— they shape our experiences, influence our well-being, and give us a sense of connection and belonging. As media consumers, it is essential to be vigilant about the content we consume and the messages they convey. By critically engaging with media, we can protect our relationships from being impacted by unrealistic or harmful portrayals.

Additionally, these findings can benefit various groups. In the entertainment industry, writers, directors, and producers can use our insights to create more authentic romantic interactions that resonate with modern audiences. By understanding these evolving trends, they can develop characters and storylines that better reflect contemporary relationships. To improve communication and foster healthier romantic interactions, media professionals should strive for authenticity and diversity in portraying relationships— avoid falling back on outdated gender stereotypes and instead, reflect on the complex, evolving nature of modern romance. Educators in gender studies, communication, and media studies can build on our research to explore further how media influences societal perceptions of gender roles and relationships. They should integrate discussions of media representation into curricula on gender and communication, using film examples to illustrate the impact of societal changes on interpersonal dynamics. Our findings provide a foundation for examining the interplay between media representation and real-life communication styles. Individuals can benefit from this research by reflecting on their own communication styles and the societal norms they perpetuate. Increased awareness can foster more inclusive and egalitarian interactions in personal relationships. Additionally, recognizing and challenging the stereotypes depicted in films can help audiences consider how stereotypes might influence their own perceptions and behaviors in relationships.

By leveraging these insights, we hope to contribute to a more nuanced understanding of romantic communication and support the ongoing evolution towards more inclusive and representative portrayals of relationships in media. This not only enriches the storytelling in romantic comedies, but also promotes healthier perceptions of romance and interpersonal relationships in real life.

References

Apostolou, M., & Christoforou, C. (2020). The art of flirting: What are the traits that make it effective?. Personality and Individual Differences, 158, 109866. https://doi.org/10.1016/j.paid.2020.109866

Cabrera, L. (2022). Quantitative and qualitative analysis of politeness and gender effects in romantic comedies [Doctoral dissertation, Trinity College Dublin]. Trinity’s Acess to Research Archive, Centre for Language and Communication Studies (Theses and Dissertations).

Clark, J., Oswald, F., & Pedersen, C. L. (2021). Flirting with gender: The complexity of gender in flirting behavior. Sexuality & Culture, 25(5), 1690-1706. https://doi.org/10.1007/s12119-021-09843-8

Hall, J.A., Xing, C. (2014). The verbal and nonverbal correlates of the five flirting styles. Journal of Nonverbal Behavior, 39(1), 41–68. https://doi.org/10.1007/s10919-014-0199-8

Wyss, Eva. (2008). From the bridal letter to online flirting: Changes in text type from the nineteenth century to the internet era. Journal of Historical Pragmatics, 9(2), 225-254. https://doi.org/10.1075/jhp.9.1.04wys.

Film, flirting, gender, Romantic Comedies

Assessing Gender Bias in Praise Language on UCLA BruinWalk

Bilingualism, Sociolinguistics

Lane Dreslin, Alison Johnson, Kyra Magda, Emily Posner, Eliana Shyley Simhai

UCLA’s student review website, BruinWalk, has approximately 18,800 users and 85,000-course reviews (Danesh & Danesh, 2021). Student evaluations are a pivotal research for several students, however, a 2021 study found that “student evaluations of teaching seem to measure conformity with gendered expectations, rather than teaching quality…” (Adams et al., 2021). In American culture, gender expectations for men include strength, assertiveness, & drive, while gender expectations for women include niceness, sociability, & interpersonal sensitivity (Prentice & Carranza, 2002). Instructors seem to receive praise for conforming to gender expectations – women as nurturers and men as leaders in the classroom (Adams et al., 2022). Similarly, a 2023 study on gender bias in student evaluations found that the most common praise terms used for female professors revolved around kindness and support, while males were most frequently praised for intelligence and knowledge (Zheng et al., 2023). Therefore, praise words may function as rewards for conforming to gender norms. Our research project focuses on UCLA BruinWalk reviews and the possible presence of gender bias in student word choice.

Introduction

As students navigate through the difficult times of college, having access to reviews on professors may put them at ease when selecting their classes. Students at the University of California, Los Angeles, can utilize a website called BruinWalk to review their professors. On the website, they can compare and contrast each professor that teaches a specific course. Students can give a rating based on five stars and share their opinion on a class they took in an optional text-based response column. Our background research suggests that students may share certain expectations and values on adherence to gender expectations and reflect this bias in their reviews. In our research, our goal was to determine if there is a pattern of gender bias in how professors are praised in reviews on BruinWalk. Additionally, we assessed whether there is a higher frequency of gender bias in praise toward female professors in STEM based classes versus those within the Social Sciences department. We decided to incorporate this question into our research because women are currently underrepresented in STEM fields (Nimmesgern, 2016). Additionally, a study of an American university found that when STEM classes moved from online to in person, female professors received lower student ratings (Babin and Hussey, 2023). The researchers attributed this to students’ increased ability to perceive gender expression in an in-person setting.

Methods

Our research question asks: “Do UCLA students assign different praise words to male and female professors?” We hypothesize that we will find a difference in praise that reflects gender bias in student reviews on BruinWalk. In other words, when it comes to male professors, we expect to see words that describe and allude to intelligence, demonstrating how men are often viewed as more knowledgeable in their field. However, when it comes to women, we anticipate words such as “nice” or “approachable” to be used, suggesting that women are valued more for their personality and embodying stereotypically “feminine” characteristics. We tested our hypothesis by analyzing the reviews of UCLA professors from Bruinwalk. The two areas of focus were STEM related classes and the Social Science department. We selected three courses from each department and identified two male and two female professors from each of those courses. The selection of the courses was simply based on finding a course that had at least two male and female professors with a significant number of reviews, as it was somewhat difficult to find reviews of female professors in the STEM department. Our chosen courses from the social science department were COMM 1, PSYCH 10, and POL SCI 10. For STEM related classes, we selected ASTR 3, COMPTING 10A, and LIFESCI 30B.

Figure 1: Columns of BruinWalk reviews of UCLA professors by gender, with praise words highlighted.

After finding classes with enough reviews, we sorted them by “most recent” and inputted praise terms from the five reviews that were most recent. For the purposes of our research, sentences with negation words were excluded from our data. After finding reviews that adhered to our guidelines, we copied and pasted them into a document and highlighted the specific praise words. We also ensured that our praise words reflected the quality of the professor as opposed to the course as a whole. Following our collection, we compared the praise words used to describe the male and female professors by counting them. Next, we determined students’ most common praise words and their frequency by inserting our data into the databasic.io website, which determined the most commonly used descriptors. Lastly, we inputted the data into the Free Word Cloud Generator website to create a word cloud that served as a visual aid to support our hypothesis.

Results

After conducting our research, we were able to determine the most frequent words used to describe female professors and male professors on BruinWalk. The most common terms used to describe male professors overall were engaging (11.9%), helpful (10.6% ), funny (7.6%), and nice (7.6%). The most common terms used to describe female professors overall were helpful (12.3%), cares (7.5%), nice (6.6%), and sweet (6.6%).

Figure 2: Visual representation of the most common praise words used for UCLA professors on BruinWalk, by gender.

Both male and female professors were frequently praised for being helpful and nice, indicating a universal appreciation for these traits regardless of gender. However, female professors were far more likely to be described as “sweet” and valued for their “nurturing” qualities. On the other hand, male professors were more likely to be described as “funny” and valued for being engaging as an instructor. While the overall results indicated patterns of gender bias, No significant differences were observed between student evaluations in STEM classes and the Social Sciences. For female STEM professors, the most common terms used were clear (9.6%), cares (9.6%), and nice (7.7%). For female Social Science professors, the most common terms used were helpful (18.5%), clear (9.3%), and sweet (9.3%).

Figure 3: Frequency of praise words female professors received at higher counts than male professors

Figure 4: Female Professor Word Cloud

Figure 5: Male Professor Word Cloud

Analysis

The results of our data reveal identifiable patterns in how female and male professors are perceived by their students. There were notable differences in the descriptive terms used that reflect broader social stereotypes and gender expectations. The frequency of the terms “cares” and “sweet’ ‘ highlights the importance of emotional investment by female professors and the broader “nurturing” role. Furthermore, it aligns with traditional gender roles that expect women to exhibit nurturing and empathetic behaviors.

Conversely, male professors were more likely to be described as “funny” and “engaging.” In the male professors evaluations, 11.9 % used the word “engaging,” and 7.6% of evaluations used the word “funny.” These terms suggest that male professors are often perceived as bringing humor and more dynamic interaction into the classroom, highlighting societal expectations that men should be entertaining and charismatic.

Overall, it appears that the more male professors exhibit engaging and humorous behaviors, the more they are praised, and the more female professors exhibit nurturing characteristics, the more they are praised. Therefore, we could ask, “do violations of gender expectations earn less praise, and, therefore, lower instructor ratings?” For example, our results indicate that women are praised more than men for being sweet and supportive. No female professor was praised for being assertive, engaging, or humorous. Therefore, further research into what response these more masculine stereotypes receive when they are performed by women would be an intriguing study.

Interestingly, no significant differences were observed between student evaluations in STEM and social sciences, suggesting that the gendered perceptions of professors remain consistent across disciplines. However, a closer look at the data does reveal subtle distinctions. Results from female professor evaluations in STEM show that the descriptive term “clear” (9.6%) is the most common, suggesting that clarity is particularly valued. This is likely because STEM fields contain complex material that requires clear communication and instruction. On the other hand, female professor evaluations in social sciences most commonly use the term “helpful” (18.6%). The higher frequency of “helpful” in Social Sciences might reflect the field’s emphasis on student support and guidance. Therefore, complying with gender expectations of clarity or supportiveness may earn professors more praise and possibly higher ratings in each field. Conversely, the difference could also indicate that performing both attributes in both STEM and non STEM fields could result in higher praise. Future research into how female professors are rated in STEM and non STEM areas of study could provide informative results.

Conclusion and discussion

The findings of our research show that gender expectations can influence the words students use when evaluating their professors. The larger phenomenon within the boundaries of gender communication shows how student perceptions of their professors can differ. A student reviewing a male professor may assess the quality of his instruction from the perspective of how he adhered to traditionally masculine stereotypes. On the other hand, a student reviewing a female professor may assess the quality of her teaching based on how she adhered to traditionally feminine expectations. Our results suggest a clear pattern in how students communicate about their professors of different genders. Conclusively, we have collected data that supports these claims concerning our original research question.

References

Adams, S., Bekker, S., Fan, Y., Gordon, T., Shepherd, L. J., Slavich, E., & Waters, D. (2021,

March 16). Gender bias in student evaluations of teaching: “punish[ing] those who fail to do their gender right” – higher education. SpringerLink. https://link.springer.com/article/10.1007/s10734-021-00704-9

Babin, J. J., & Hussey, A. (2023). Gender penalties and solidarity — Teaching evaluation differentials in and out of STEM. Economics Letters, 226, 111083. https://doi.org/10.1016/j.econlet.2023.111083

Danesh, N., & Danesh, E. (2021, April 12). Behind the bruin: Taking an inside look at UCLA

review website Bruinwalk. Daily Bruin.

https://dailybruin.com/2021/04/12/behind-the-bruin-taking-an-inside-look-at-ucla-review-website-bruinwalk

Nimmesgern, H. (2016). Why are women underrepresented in STEM fields? Chemistry, 22(11), 3529–3530. https://doi.org/10.1002/chem.201600035

Prentice, D. A., & Carranza, E. (2002). What Women and Men Should Be, Shouldn’t be, are Allowed to be, and don’t Have to Be: The Contents of Prescriptive Gender Stereotypes. Psychology of Women Quarterly, 26(4), 269–281. https://doi.org/10.1111/1471-6402.t01-1-00066

Zheng, X., Vastrad, S., He, J., & Ni, C. (2023). Contextualizing gender disparities in online

teaching evaluations for professors. PloS One, 18(3), e0282704. https://doi.org/10.1371/journal.pone.0282704