Sociolinguistics

Introduction and Background

Across digital spaces, content creators strive to present themselves effectively in various contexts. Over the past decade, the rise of young female vloggers has transformed YouTube from a video-sharing platform into a complex stage for influencer culture. These creators document life. Yet, they also navigate the pressures of visibility and social norms in a highly curated world. As the YouTuber phenomenon continues to grow, the importance of understanding how people perform their online identity is ever essential.

Previous research by Abdul Razak (2025) has focused on influencers’ linguistic patterns, such as the use of the word “like.” Other studies have concentrated on influencer actions (e.g., taking selfies) (Adidin, 2016). However, there is a limited understanding of how gestures overall may change based on context for female-identifying content creators. Drawing on Goffman’s (1959) concept of impression management, this project considers how vloggers may act as performers on a digital stage. Abidin’s (2016) theory of subversive frivolity reframes seemingly lighthearted content as sites of resistance, where femininity becomes a tool for navigating digital labor. Hence, this existing literature further legitimizes the inherent merits of our research focused on shifts across content by this group of young, female YouTubers who are often dismissed.

This study examines how undergraduate female YouTubers utilize nonverbal cues in their casual versus serious videos. This examination seeks to contradict the perception of this group as one-dimensional and hypothesizes that our sample changes their gestures to fit the situation — more expressiveness (a higher amount of hand gestures and more relaxed posture, head movements, and hand openness) in casual videos and less in serious ones.

Methods

We began our study by observing five female YouTubers based on their popularity, as we were interested in analyzing videos with high admiration. We noted creators who had verified statuses, a strong following, and a high number of views. We focused mainly on those with over ten thousand subscribers, which then led to a high viewer count on the videos we chose. Our selection included twenty videos total, with two casual and two serious videos per creator. This came out to four videos for each of the five creators. Each one was determined as casual if it involved low social or personal stakes, like “a day in the life,” while serious videos included high social or academic stakes, such as career planning. We randomly selected a five-minute clip to observe from each one with an online number generator (numbergenerator.org), to remove any sort of bias. We recorded data when the YouTuber had their face visible on the screen and spoke to the audience. With each clip, we examined multiple gesture categories due to their associations with expressiveness. These gestures were adaptors, illustrators, emblems, posture, hand openness, and head movements.

We then coded each clip by counting the raw numbers of illustrators, adaptors, and emblems we observed per clip. Illustrators are gestures that send a message, like pointing or placing a hand on your chest, as shown in Figure 1.

Figure 1: Example of Illustrator from Lihn Troung’s “A week in my life: college finals & christmas in the city // vlog 007”

Adaptors, on the other hand, involve self-touching behaviors like scratching or rubbing and are typically unconscious responses to adapt to one’s environment (Kelmaganbetova et al., 2023). Figure 2 shows an example of an adaptor from Lihn Troung’s videos where she tucks her hair behind her ear.

Figure 2: Example of Adaptor from Lihn Troung’s “A week in my life: college finals & christmas in the city // vlog 007”

Emblems are substitutes for words or phrases in communication, like the peace sign shown in Figure 3.

Figure 3: Example of Emblem from Lihn Troung’s “A week in my life: college finals & christmas in the city // vlog 007”

Our next step was to further observe and calculate each video’s expressiveness. We did this in terms of the creators’ posture, hand openness, and head movements throughout the videos. To code this step, we utilized a Likert scale to determine the levels of each gesture displayed by the creator. For instance, a video with relaxed posture would have a rating of 1 on the Likert scale, while a rating of 5 would indicate formal levels of posture throughout the video. The same was for hand openness where a 1 meant closed hand positions, and a 5 meant open. High levels of head movement received a rating of 5, while still head movements received a 1. We performed interrater testing as well, in order to ensure overall consistent results. This testing measured consistency by having more than one group member collect the data.

Results and Analysis

The data set partially lined up with our prediction. Our group’s hypothesis was that young women in undergraduate programs would have a significant difference in gestures between serious and casual content. Our data demonstrated some differences between serious and casual contents’ data. However, there seemed to be more variance between the person that had uploaded the videos themselves versus their actual content type. Something we noticed was that postures would be more “correct” or upright in serious videos in general. Meanwhile, casual videos often had slouchier or relaxed posture. The frames were another factor that typically depended on the type of video that was being seen. For example, shoulders up was the frame that was seen in serious videos. On the other hand, videos that were more casual typically had a less consistent frame. Throughout the videos there was also a difference between different creators. Lihn Truong was the only creator that we viewed that fit the prediction that nine or more illustrators would be visible in casual content. Helaine Zhao and Studyquill had less gestures when filming serious content. Some of the creators kept many of their video styles pretty consistently. Mikayla Mags would be an example of a creator that had high illustrators across video types. Mags also kept a similar posture throughout her videos. Other things that we noticed among videos were that some creators had lower gestures on the Likert scale despite a difference in content type. Lillian Zhang kept a formal style of video content and personality throughout. The patterns observed demonstrate how tone and individual creators overall have more variety rather than their content type. It can be connected to sociological themes such as gender and women trying to be perceived as likable by other people due to societal expectations to be kind and nurturing. This can potentially come across differently depending on the content creator and what they perceive to be according to standard. It is also important to note that this may be a subconscious decision to do so.

Figure 4 shows the gesture counts calculated for Lihn Troung’s videos, along with the Likert scale data for each one. As shown in her casual holiday vlog, she used 10 illustrators, 5 adaptors, and 4 emblems. We can see that her serious content showed more restrained posture, fewer gestures, and closed body language. For more detailed counts and results, see the data table linked at the bottom of this post.

Figure 4: Results from Lihn Troung’s Videos (Raw Counts and Likert Scale)

Video Title	YouTuber	Vlog type	Illustrators	Adaptors	Emblems	Posture	Hand Openness	Head Movements
A week in my life: college finals & christmas in the city // vlog 007	Lihn Troung	Casual	10	5	4	5	5	5
another productive day in my life studying, coffee omakase, dance practice, & senior year memories	Lihn Troung	Casual	8	1	3	4	4	4
why you need hobbies in 2024 // rediscovering my hobbies as a burned out college student	Lihn Troung	Serious	6	1	3	2	4	5
study with me for college finals (pomodoro method)	Lihn Troung	Serious	6	4	7	2	2	1

Regarding other overall trends, illustrators were the most frequently counted gesture. Casual vlogs showed higher amounts of adaptors and emblems; however illustrators actually appeared more frequently in serious videos. Yet, serious vlogs showed fewer hand gestures as depicted in Figure 5.

Figure 5: Relationship between Gesture Counts of Casual vs. Serious Videos

X-axis: Nonverbal Gesture

Y-axis: Number of Counts

Examining the data from the Likert scale included in Figure 6, casual videos had an average score of 4.4 for head movements while serious videos scored an average of 4.8, depicted variation in relaxed and expressive behavior. However, hand openness remained consistent across topics, contradicting part of our original hypothesis which predicted that hand openness would be more prevalent in casual content.

Figure 6: Average Ratings of Expressiveness using Likert Scale (1-5)

X-axis: Expressive Gesture

Y-axis: Rating

Thus, while variation occurred between YouTubers, with some like Linh Troung adhering to our hypothesis closely while others like Makayla Mags presented some contrasting data, overall, a distinction in gestures between causal versus serious content is visible.

Discussion and Conclusion

Overall, YouTubers in casual vlogs displayed more expressive behaviors — more adaptors and emblems, relaxed posture, and head movements. In contrast, serious vlogs tended to feature more restrained gestures yet a higher number of illustrators. Although these patterns largely supported our hypothesis and the results from previous literature, individual variation between vloggers and the counts for illustrators suggests the benefits of a further study.

Our sample size of five female undergraduate YouTubers is one limitation of our study. While this small group allowed for detailed coding, it curbs the generalizability of our results. Furthermore, although the structured coding scheme and interrater testing helped mitigate bias, gesture analysis inherently involves some degree of interpretation. Distinguishing between an illustrator and an adaptor can be context-dependent. Without direct input from the creators themselves, interpretations largely remain speculative — especially regarding intentionality. It is also uncertain how gender specifically shapes these behaviors. Future research could compare how gestures differ between a sample of male versus female YouTubers.

Although additional research may help us derive more concrete overall conclusions about our population, our study offers several benefits to the study of interpersonal communication and societal understanding of internet culture. Our research challenges stereotypes about female influencers by showcasing their context-sensitive nonverbal use. The shift in physical behavior depending on the topic can suggest acute awareness of audience expectations. These patterns contribute to our broader understanding of impression management as gestures take on a layered meaning. The openness adopted in casual vlogs reflects a negotiation of credibility. In serious videos, reducing gesturing and maintaining an upright posture aligns with traditional markers of authority, while in casual contexts, animated movement may serve to build rapport and relatability (Smith, 2017). Gestures become a coded method of exploring platform visibility, societal norms, and professional aspirations.

The findings point to a broader takeaway — nonverbal communication is a vital aspect of digital self-presentation. Although these gestures may be subtle, they wield significant implications for how we understand identity, labor, and gender in the age of influencers.

EXTENDED DATA TABLE + COUNTS

• https://docs.google.com/spreadsheets/d/1RnSxkMVnGdnex0hzXd5Kp_PWCNb36AltPVLHQpakhfs/edit?usp=sharing

RECOMMENDED READING/VIEWING (RELEVANT INFO)

• https://youtu.be/ZzEOs9bVpZQ?si=g0jPMMVomBFOFCV6
• https://languagedlife.ucla.edu/sociolinguistics/the-linguistic-switcheroo-navigating-style-shifts-in-college-discourse/
• https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1568341/full
• https://www.talkabouttalk.com/gender-differences-in-communication-ep-141/

REFERENCES

Abdul Razak, N. (2025). The use of slang by Gen Z female influencers on Instagram and Twitter (X). International Journal of Research and Innovation in Social Science, 9(4), 1910–1917. https://rsisinternational.org/journals/ijriss/articles/the-use-of-slang-by-gen-z-female-influencers-on-instagram-and-twitter-x/

Abidin, C. (2016). “Aren’t these just young, rich women doing vain things online?”: Influencer selfies as subversive frivolity. Social Media + Society, 2(2), 1–17. https://doi.org/10.1177/2056305116641342

Goffman, E. (1959). The presentation of self in everyday life. Anchor Books. Retrieved from https://archive.org/details/presentationofs00goff

Kelmaganbetova, A., Mazhitayeva, S., Ayazbayeva, B., Khamzina, G., Ramazanova, Z., Rahymberlina, S., & Kadyrov, Z. (2023). The role of gestures in communication. Theory and Practice in Language Studies, 13(10), 2506–2513. https://doi.org/10.17507/tpls.1310.09

Number Generator. (n.d.). NumberGenerator.org. Retrieved June 10, 2025, from https://numbergenerator.org (https://numbergenerator.org/)

Smith, H. J., & Neff, M. (2017). Understanding the impact of animated gesture performance on personality perceptions. ACM Transactions on Graphics, 36(4), Article 128. https://doi.org/10.1145/3072959.3073697

Introduction and Background

i) “Hey, my name’s Julian Stassi and I’m 1000% committed to UCLA! I’m from Sacramento, and majoring in Business-Economics. I love watching football, hanging with friends, and I’m a huge foodie. I’m looking for a roommate and planning to rush in the fall, so bang my line!”

These exact messages flood the UCLA Class Instagram page every single year. This is a space where thousands of recently admitted students introduce themselves to find roommates and friends, and it is a great way for students to start shaping their college identity before ever arriving on campus. These posts are more than just sharing majors or hometowns; they’re filled with unique sets of carefully chosen language that says something about how students want to be seen.

ii) These personal bios are filled with informal language, which makes them feel ​​personal and distinctive. While others can be more straightforward and formal. This made us question: Does gender influence tone or slang? How are North Campus majors different from STEM majors? What connections could be made between these new Bruins, and what does this say about them?

iii) We then analyzed the first 200 posts from the official UCLA 2029 account and closely looked at the diverse use of slang, grammar, and emoji use, and how language varies across gender, major, and location. Our project explores how even small stylistic and language choices reflect deeper identity performances and social positioning within online spaces. These posts are not just about introductions; they are shaping how students want to be perceived as they begin forming who they are on campus.

Methods

For this project, like previously mentioned, we conducted our quantitative analysis by going through the first 200 posts from the @ucla2029freshman class Instagram account – a social media account used for recruiting and engaging with incoming freshman students at UCLA – and analyzed a myriad of demographics from each post.

Our main goal from this cultivation of data was to analyze the topicof linguistic strategies used to engage prospective students and to comprehend how language choices reflect broader communicative goals in higher education and social media outreach. The method of data collection, a quantitative content analysis where we carefully recorded and studied language patterns in social media posts, focused on a 200 sample from the largerpopulation of the account and looked at gender (male/female), the opening style used (how they greet the people seeing their account, e.g., hello, hiii, hey, etc), any slang used (things like words in all caps, pls, wanna HMU, roomie, foodie, insta, etc), if there is a presence of emoji use, proper grammar and punctuation, if students listed their pronouns, if people used all lowercase or all uppercase, and their intended major (pre-med, humanities, STEM).

Since we examined different styles of speech, we also drew from the idea of language variation, which looks at how people’s language changes depending on their background, age, gender, or community involvement, as well as grammar, tone, and word choice (Bahtina, 2025).

Our data collection specifically focused on the first 200 posts chronologically available on the account at the time of data collection to ensure that we cultivated a large but manageable sample representing not only a multitude of academic disciplines but also genders, communication styles, and decisions. This approach allowed us to gather a wide variety of people at different stages of the student admission process and people from an abundance of different demographics, which helped ensure an accurate sample of the representation.

When reporting findings on gender in our study, the results were strictly based on the first 200 posts from the class account, and we did not assume a 50/50 gender split. This was because we wanted to limit any confounding variables and stay stable with our procedure and data collection, and it also revealed the prevalence of social media use and how it differs across gender.

Results and Analysis

After each group member cultivated data from the posts of the class of 2029 Instagram account we found the following findings: 43.1% of female students use emojis compared to only 32.6% of male students (Figure 2, 3), female business economic majors are 50% more likely to use slang compared to men, women are 1.8 times more likely than men to write in lowercase (Figure 1), and men in humanities adhere most strongly to formal digital norms. A key concept shown here is also the informalization of language, which posits that communication tendencies on social media tend to vary in regard to their casual and playful tone (Natsir et al., 222). This is evident in the use of emojis, lowercase letters, and slang in social media posts (Figure 1).

Figure 1

From this data we had several findings – what appears to be a casual introduction or captioning on an introductory Instagram post is often a strategic act of self-presentation that differs among each person depending on their own demographics and the image they want to illustrate to the people viewing their posts which could be future friends, roommates, classmates, or even project mates in a class setting like this. Simply put, the data shows the presence of gendered linguistic performance (Van Herk, 101). This refers to the idea that is not something we simply are but something we do, which relates to the evidence because it emphasizes the presence of linguistic performance and ways people perform. While we found relationships between gender and linguistic decisions and major and self-presentation, the relationship between gender and major was much more complex than a clear pattern.

Every individual use of emoji, lowercase letter, or slang word differs person to person (however, there are more common themes seen in some demographics than others) and demonstrates some aspect or other about a person. These posts on the Instagram account aren’t just digital information, but they have the power to reflect someone’s identity, culture, and origin. Aligning with the idea that emojis are a form of cultural expression. As Professor Junnifer Prough says, emojis clearly display layers of cultural communicative meaning, explaining that users use emojis to be understood and for belonging (Ted Talk by Freedman, 2019). When students post their introductory bios, they are engaging in a form of visual and linguistic identity making, whether they choose to or not.

Figure 2

Figure 3

These results and implications are imperative to helping illustrate how social norms shape larger themes across digital communications and reinforce the identity that language is performative even in the most casual of settings.

These students engage in what is known as “front stage” performances where they craft online personas that balance authenticity, approachability, and cultural fluency within the norms of the current contemporary society and how Gen Z acts in the current state of the social media culture. The agentive behavior is consistent with research that shows the use of social media to develop self-presented messages tailored to specific audiences, shaping their identity even before arriving on campus (DeAndrea et al., 2011).

Discussion and Conclusion

Our analysis of the UCLA Class of 2029 Instagram posts shows the language in Instagram posts as a form of digital identity construction. While captions appear to be informal, they actually reveal a choice that every single student makes in order to express themselves. They do this in order to, in some way, fit in or stand out. The use of emojis, punctuation, and slang in order to differentiate genders, majors, and regions shows that language use is made purposefully. The pattern that was most clear to us was gender-based differences in tone and formatting of the posts. Female students, when compared to male students, overwhelmingly use an informal style of using emojis, lowercase, and slang. Males, on the other hand, were more likely to have proper grammar and tone. This is clear in the research with sociolinguistics showing women often are more linguistically innovative in language to build social standing than men, who look toward status-oriented forms of communication. The difference between majors was also an interesting point where humanities students had more flexible tones and relaxed language, while STEM students were direct. This shows how different disciplines socialize students into a variety of ways in which they communicate. These posts are not only explaining who they are or what they are excited for, but they also create a performance of sorts with the things they put within the text caption of their posts. The slang, punctuation, and emojis all say something about the way in which the students desire to be seen as they matriculate.

References

Bahtina, D. (2025). 2A Variation in language. Communication 188B.

DeAndrea, David C., et al. “Serious Social Media: On the Use of Social Media for Improving
Students’ Adjustment to College.” The Internet and Higher Education, vol. 15, no. 1,
2012, pp. 15–23. https://doi.org/10.1016/j.iheduc.2011.05.009.

Freedman, A. (2019, April 26). The culture of emoji. ??? | Alisa Freedman | TEDxUOregon. YouTube. https://www.youtube.com/watch?v=U1QpAUhnoWE

Natsir, Nur, et al. “The Impact of Language Changes Caused by Technology and Social Media.” Language Literacy: Journal of Linguistics, Literature, and Language Teaching, vol. 7, no. 1, 2023, pp. 220–28. https://pdfs.semanticscholar.org/9216/d49e850423f6d38e416694724061649445ec.pdf.

Van Herk, G. (2018). Gender. In What is sociolinguistics? (2nd ed., pp. 96–116). Wiley Blackwell.

Duggan, Maeve. (2013, September 12). It’s a woman’s (social media) world. https://www.pewresearch.org/short-reads/2013/09/12/its-a-womans-social-media-world/

Introduction and Background

Generation Z (“Gen Z”) is often seen as the generation that is constantly challenging traditional norms, especially when it comes to how they speak. Over the past couple of years, TikTok has been a cultural dominant, where slang spreads rapidly and often carries deeper meanings about identity, relationships, and culture. Prior research has explored gender differences in communication; however, they have mostly focused on face-to-face or text-based interactions. We believe that far less is known about how gendered language plays out in fast-moving, algorithm-driven spaces such as TikTok. Hence, what led us to conduct this research, our target group consists of Gen Z women and men ages 18-25 who are active users on TikTok. We specifically selected TikTok due to its platform population hosting more Gen Z individuals than others, and due to the constant exchange of slang on its platform. Common linguistic features of Gen Z include shortened words and abbreviations such as “sus”, for suspicious. Other linguistic features present include gendered slang phrases that reinforce traditional gender expectations, such as the terms “girl boss”, “I’m just a girl”, “sigma male”, etc.

Some academic sources we view to be relevant to our topic include Van Herk’s “What is Sociolinguistics?”, which focuses on differences between gender interactions and communication. Herk explains that gendered language is not natural but performed, since gender is a social construct. He also points out that women most commonly use hedges, fillers, tag questions, uptalk, etc, that reflect differences in the way women and men communicate. In comparison, men tend to be more assertive and interruptive. This source helps us understand the roots of gendered communication so we can then compare it to modern slang use and its relation to gender. Another source we are closely looking into is, “The Social Significance of Slang” by Alice Damirjian. This article argues that slang use is deeply tied to identity formation, group boundaries, and social cohesion. While it doesn’t focus on the meaning of slang words, it instead explores how words are used and in what context. This method is similar to what we intend to do in our research paper, and why we viewed this source as a vital informative piece.

Additionally, A.M. Blackstone’s “Gender Roles and Society” article explores how gender roles are learned and reinforced through socialization and institutions. This citation we included since it helps us support our hypothesis that Gen Z slang may reflect gendered communication patterns. Blackstone’s sociological framework on gender and identity is a great source that guides our research and analysis, based on empirical evidence rather than assumptions. Overall, these studies have helped us inform our hypothesis that argues that while slang is fluid and not fully tied to gender, slang use still echoes traditional gender norms.

Methods

Our study investigates how Gen Z men and women use slang on TikTok and whether this reflects broader gendered communication patterns. To explore this, we will collect slang terms through a survey and then analyze how those terms are used in TikTok videos using a fresh account to eliminate algorithmic bias. The survey will be hosted on Google Forms for ease of delivery and to ensure that we have access to necessary statistics. After this survey, we will examine the top videos for each slang word. The use of both individual and broad-scale analysis techniques allows our group to build a more nuanced understanding of the use of slang.

Once this stage of the research process has been completed, we will be aiming for 10 videos per slang term, which will offer us dozens of relevant examples through both the video itself and its comments. This number is a reasonable amount to reach, but it does not limit our understanding. We chose this two-prong data collection method to more accurately depict the results of this experiment and gain a deeper understanding of the language used.

Using thematic coding and discourse analysis, we will analyze tone, context, delivery, and nonverbal cues associated with each slang term, focusing on how language use relates to gender performance and identity. The discourse analyzed will be specifically the way that the different genders choose to interact with each other using the slang presented. Our guiding research questions include: Do men and women use the same slang terms differently? What patterns of gendered communication emerge through slang? And how do these trends reflect or challenge traditional gender norms? Based on existing sociolinguistic literature, we hypothesize that Gen Z slang use mirrors gendered communication tendencies, with women more likely to use slang that signals inclusivity and emotional connection (e.g., “slay”), and men more likely to use slang that emphasizes assertiveness or dominance (e.g., “let’s go”). However, given Gen Z’s fluid approach to identity and language, we also expect to find challenges to this norm.

Results and Analysis

Our final pool of participants for the Google survey portion of data collection was 91 responses, with 51.6% male and 48.4% female respondents. We found that “bet” and “rizz” were the two most frequently used slang terms.  The term “rizz” was the most frequently used, with it being mentioned a total of 46 times between 17 women and 29 men. While “bet” came in second, with it being mentioned a total of 23 times between 11 women and 12 men.

Figure 1. Results from the Google Form Survey: Total Number of Respondents was 91

When analyzing the 10 male-coded videos for “rizz”, 8 out of 10 videos generated by male content creators were men demonstrating their “rizz”, by approaching women with a flirtatious attitude, with the goal of receiving the woman’s contact information. While 4 out of 10 videos captured men providing advice on how to have “rizz” or “rizz up” women. This suggests that men’s use of the term  “rizz” aligns with traditional masculine traits like assertiveness and confidence in social situations. It reflects Gen Z’s tendency to blur sincerity and parody, where young men both conform to and mock traditional courtship behavior.

Figure 2. Contextual Use of “Rizz” by Gender (Male: Flirtation, Confidence)

As for the female-coded videos for “rizz,” the results demonstrated that women most often used “rizz” for humor and romantic advice, sometimes expanding it beyond male-female contexts. The data suggests they intentionally adopt masculine slang to relate to men and navigate social dynamics, which reflects how gender norms shape communication even with shared slang. In addition, the data also showed how women resisted gendered dynamics by using “rizz” in a different manner than it was intended.

Figure 3: Contextual Use of “Rizz” (Female: Romantic Advice)

For the 10 male-coded videos with the slang term “bet,” we found that men most frequently demonstrated or recorded their use of “bet” to express agreement, acceptance, or commitment to a plan, e.g., responding “bet” when a friend proposes hanging out or making a wager. 5/10 videos analyzed showcased scenarios where “bet” was used to confirm plans or signal confidence in accepting challenges. This result suggests that men tend to use “bet” as a casual, assertive way, aligning with traditional masculine traits of confidence and decisiveness. It also reflects Gen Z’s conversational style, which blends casual affirmation with a sense of underlying confidence.

Figure 4. Contextual Use of “Bet” by Gender (Male: Agreement, Commitment, Confidence)

As for the 10 female-coded videos for the term “bet,” Our results showed that women most often used “bet” to express agreement, excitement, or playful sarcasm in casual conversations with friends. 3/10 videos analyzed women using “bet” in a lighthearted or humorous way. Showing enthusiasm or signaling they were on board with a plan.

Figure 5. Contextual Use of “Bet” by Gender (Female: Sarcasm, Excitement, Agreement)

Discussion and Conclusion

Our results show that there are distinct gendered patterns in the use of slang terms like “rizz” and “bet.” For the slang term “rizz,” men were more likely to demonstrate or embody the term, while women often used it mockingly or humorously. In the case of “bet,” men tended to use the term to affirm plans or accept challenges, reflecting direct engagement, whereas women used it more playfully or sarcastically. For both slang terms, men typically expressed traditionally masculine traits such as confidence, assertiveness, and decisiveness. In contrast, women exhibited traits like humor and irony as a way to challenge or resist the gender dynamics ingrained in the slang terms. These insights are particularly useful for scholars in sociolinguistics, digital media, and gender studies, as they highlight how informal language can reinforce, challenge, or reshape traditional gender roles. By looking at how terms like “rizz” and “bet” are used, we can gain a better understanding of how digital media reflects and reinforces ideas about traditional gender roles and communication. Our results could benefit researchers, educators, and media literacy advocates by shedding light on gendered communication patterns embedded in Gen Z slang. This insight can contribute to a broader conversation about how digital culture affects youth identity formation, peer dynamics, and societal expectations. Our research helps unpack the cultural significance of everyday language, positioning it as a key site for both reproducing and potentially resisting dominant gender ideologies.

References

Bibian Ugoala. “GENERATION Z’S LINGOS on TIKTOK: ANALYSIS of EMERGING LINGUISTIC STRUCTURES.” Journal of Language and Communication, vol. 11, no. 2, 30 Sept. 2024, pp. 211–224, www.researchgate.net/profile/Bibian-Ugoala/publication/384965246_GENERATION_Z, https://doi.org/10.47836/jlc.11.02.08.

Blackstone, A. (2003). Gender Roles and Society. https://digitalcommons.library.umaine.edu/cgi/viewcontent.cgi?params=/context/soc_facpub/article/1000/&path_info=Blackstone___Gender_Roles_and_Society.pdf

Damirjian, A. (2024). The social significance of slang. Mind & Language. https://doi.org/10.1111/mila.12530

Herk, G, V. (2018). What Is Sociolinguistics? (Linguistics in the World) 2nd Edition (1). India New Delhi: Wiley Blackwell.

Jahan, I. (2021). The impact of gendered language on our communication and perception across contexts and domains. Journal of Language and Linguistic Studies, 17(4), 3523-3534. https://www.jlls.org/index.php/jlls/article/view/5449

Introduction and Background

Stereotypes associated with gossip divert from its basic communicative, relational, and cultural functions. Gossip is constantly misunderstood as idle chatter in scholarly as well as popular discourse. Recent research in psychology, communication science, and organizational behavior has reframed gossip as a socially strategic action. Gossip has been shown to be critical to social coordination, identification, and norm enforcement as a primary way in which individuals acquire knowledge about both others and themselves (Dores Cruz et al., 2021). Through our study, we examined these ideas from the specific perspective regarding how gendered depictions of gossip have evolved across generations within popular media, namely television dramas.

Before looking into this, we were already aware of previous information regarding gendered patterns of gossip, i.e., the idea that women and men gossip differently. Past research characterized women as gossiping primarily about relationships and personal matters so as to participate in social bonding, whereas men gossip to acquire humor or attain status (Levin & Arluke, 1985). Current research maintains this trend, with women participating in emotion-focused, relation-directed means of gossip in the context of coalition-building (Davis et al., 2018). We also know that men, on the other hand, gossip far less overall and use gossip in instrumental functions as a way to navigate social positioning. Such behaviors cohere with Deborah Tannen’s theoretical framework, distinguishing “rapport talk”, a related, connection-directed characteristic of women, and “report talk,” an informational, hierarchical characteristic of men (Van Herk, 2018).

Despite the fact that such patterns have long since taken root, there was no framework to comprehend the way in which media representations reinforce or challenge these gendered communication norms in the long run–the leading question guiding our own research. While prior research has explored gendered patterns in gossip, few studies have examined whether and how gossip has evolved across generations or been portrayed differently over time in television. This gap in the literature motivated our study, as we aimed to investigate whether shifts in gender norms were mirrored in media discourse. With continued influence from television to the public eye regarding social conduct, the issue we examined involved whether gender representations in gossip have shifted in sync with gender norms. With this, our research documents the construction of gossip across two periods (1990s–2000s, 2009–2022) in U.S. television with attention to gender. By combining empirical content analysis with sociolinguistic theory, our analyses help demonstrate the idea that gossip is neither fixed nor gendered; it is an evolving social practice.

Methods

In terms of gossip across different generations and genders, we were more interested in how TV shows categorized gossip across two distinct periods and how female-identifying individuals differed in their communication features from male-identifying individuals. First and foremost, we coded for gender identity based on the pronouns that were used in the show. We analyzed different TV shows representing two generational periods. The first period involved shows that were released between 1997-2009, including Friends (Seasons 5 and 8) and Survivor (Season 6). The second period involved TV shows released between 2009-2022, including Love Island (Seasons 1 and 2), Outer Banks, and The White Lotus. All of these episodes were accessed through streaming platforms, including Netflix and Hulu. Because television remains a powerful force in shaping public understandings of identity and interaction, analyzing TV dialogue allows us to observe how gossip reflects or resists evolving social norms.

For each of these shows, we chose approximately three scenes, each around 2 minutes in duration, that featured confrontation, gossip, or situations that all displayed differences between how the two identities communicated with one another. A total of 15 scenes were analyzed and transcribed across all shows. By observing the contrast between individuals, there was an exposure to how gossip can be challenged, and it allowed us to gain insight into each generational period. So, we assessed the prevalence of communication features in the scenes in the two periods of time.

*See Figure 1 below for an example of our transcriptions.*

Figure 1. Transcription from Friends (Season 5, Episode 9), showing a gossip scene used to analyze gendered communication styles like humor and confrontation.

Each television scene was analyzed using six communication features, each previously associated with gendered communication and gossip behavior: indirectness, report, rapport, humor as a shield, status assertion, and meta-commentary. For example, we see indirectness in Love Island when Caro and Cashel are talking about other couples as well as themselves. Caro, who identifies as female, says “ °hh I just (0.3) feel like (0.4) you’re not being ↑honest with me.” Use of the words “I just feel like” indicates hedging, suggesting Caro’s unwillingness to be straightforward due to nerves about how her words may be perceived. In response, Cashel, who identifies as male, says “ <<laughing> I guess I’m just the worst boyfriend ever.>” He uses humor as a shield to avoid taking ownership of his behavior. We also observed a report talk in Friends, when Ross asks, “What’s going on?” to get a factual explanation of the situation, contrasting with rapport talk, like Harper in The White Lotus confiding in Ethan to build emotional alignment over shared social discomfort. Status assertion occurred in Survivor, where one contestant claimed, “They’re playing us,” in an attempt to establish control over the narrative. Lastly, meta-commentary was seen in Outer Banks, when a character prefaced gossip with, “I know we’re being shady, but hear me out…” acknowledging the act of gossiping itself.

Figure 2. Bar chart comparing six gossip features across male and female characters from two TV generations (1997–2009 and 2009–2022), based on average frequency.

Figure 2, a grouped bar chart, compares the previously mentioned communication features across male and female characters in TV shows from two time periods: 1997–2009 and 2009–2022. The X-axis contains the coded communicative traits, while the Y‑axis shows their average frequency on the 0–4 scale. The pattern is striking: use of gossip communicational features by female characters in TV shows remained relatively consistent across the periods, while portrayals of male gossip noticeably shift, especially in regards to a reduced use of humor and indirectness. This suggests a slow blending of gendered language styles, even as clear differences persist.

In addition to data, we analyzed specific TV scenes to demonstrate these trends across contexts. Figure 3 features a moment from the White Lotus, between husband and wife, Ethan and Harper. In one scene, Harper attempts to gossip with Ethan about the other couple they are traveling with, whom she sees as fake, snobby, and arrogant (White, 2022). She uses an indirect style of gossip to assert her status as well as gain rapport from her husband, exemplifying the modern portrayal of female gossip both strategically and socially.

Figure 3. Scene from The White Lotus (Season 2, Episode 2), showing indirect and strategic gossip between characters Harper and Ethan.

To situate our project within real-world discourse, we included several informal references. A TEDx Talk titled “Why gossip is good” by Assem Bisenbay breaks down gossip’s benefits in modern society (Bisenbay, 2019). Another TEDx video, “The virtues of gossip” by Richard Weiner, echoes these ideas by arguing that gossip can foster social cohesion (TEDx, 2013). Additionally, a Reddit thread asking “Why do we love gossip?” offers everyday perspectives that parallel our findings about social bonding and status (CommonRash, 2022). Taking this media into account, our project was structured around five key ideas: gendered gossip, generational communication, TV media discourse, sociolinguistic method, and report vs. rapport talk. These pinpoint our population (TV characters), method (scene-based communication coding), and theoretical orientation. All TV show clips analyzed were publicly available; no private data or human subjects were involved. Our research is the collaborative work of Abigail Garza, Julian Goldman, Faye Turcotte, Madeline Doring, and Dylan Sherr, conducted under the mentorship of Professor Bahtina.

Discussion and Conclusion

Our findings across various scenes in TV shows contribute to the idea of gossip as a whole and what it truly means to privately discuss personal information with others through means of conversing. Gossip itself provides key insights into the ways in which individuals interact on a broader scale, both culturally and socially. In terms of gender and generational differences, studying gossip across all individuals can be used to highlight key aspects of the progression that can be seen in conversational analysis across genders. Through studying numerous television dramas, it can be seen that the ideas of report and rapport amongst male-identifying and female-identifying individuals remain true no matter the time period. This report vs. rapport stereotype can be generally accepted as being a ‘mainstay’ in the scene of gossip itself. So, although there are constant changes occurring in the world, there will still be aspects of gendered gossip that remain constant no matter where we are or what time it is.

References

Bisenbay, A. (2019, September). Why gossip is good [Video]. TED Talks. https://www.ted.com/talks/assem_bisenbay_why_gossip_is_good?utm_campaign=tedspread&utm_medium=referral&utm_source=tedcomshare

Burnett, M. (Executive Producer). (2003, April 3). Girls vs. boys (Season 6, Episode 8) [TV series episode]. In Burnett, M. (Executive producer), Survivor. CBS.

CommonRash. (2022). Why do we love gossip? Reddit. https://www.reddit.com/r/CasualConversation/comments/v9dyen/why_do_we_love_gossip/

Davis, A. C., Arnocky, S., & Vaillancourt, T. (2018). Sex differences, initiating gossip. In T. K. Shackelford & V. A. Weekes-Shackelford (Eds.), Encyclopedia of evolutionary psychological science (pp. 1–8). Springer. https://doi.org/10.1007/978-3-319-16999-6_190-1

Dores Cruz, T. D., Nieper, A. S., Testori, M., Martinescu, E., & Beersma, B. (2021). An integrative definition and framework to study gossip. Group & Organization Management, 46(2), 252–285. https://doi.org/10.1177/1059601121992887

Jonas, J., & Pate, S. (Writers), & Pate, J. (Director). (2020, April 15). The runaway (Season 1, Episode 8) [TV series episode]. In Jonas, J., Pate, J., & Pate, S. (Executive producers), Outer Banks. Netflix.

Kauffman, M., & Crane, D. (Writers), & Bright, K. S. (Director). (1998, December 17). The one with Ross’s sandwich(Season 5, Episode 9) [TV series episode]. In Kauffman, M., & Crane, D. (Executive producers), Friends. NBC.

Kauffman, M., & Crane, D. (Writers), & Bright, K. S. (Director). (1999, February 11). The one where everybody finds out (Season 5, Episode 14) [TV series episode]. In Kauffman, M., & Crane, D. (Executive producers), Friends. NBC.

Kauffman, M., & Crane, D. (Writers), & Bright, K. S. (Director). (2001, November 15). The one with the rumor (Season 8, Episode 9) [TV series episode]. In Kauffman, M., & Crane, D. (Executive producers), Friends. NBC.

Levin, J., & Arluke, A. (1985). An exploratory analysis of sex differences in gossip. Sex Roles, 12(3–4), 281–286. https://doi.org/10.1007/BF00287594

Macleod, C. (Director). (2015, June 7). Episode 6 (Season 1, Episode 6) [TV series episode]. In Richard Cowles et al. (Executive producers), Love Island. ITV2.

Macleod, C. (Director). (2016, June 10). Episode 9 (Season 2, Episode 9) [TV series episode]. In Richard Cowles et al. (Executive producers), Love Island. ITV2.

TEDx Talks. (2013, November 19). The virtues of gossip: Richard Weiner at TEDxMiami 2013 [Video]. YouTube. https://www.youtube.com/watch?v=WpiTp2COVZk

Van Herk, G. (2018). Gender. In What is sociolinguistics? (2nd ed., pp. 122–141). Wiley Blackwell. White, M. (Writer & Director). (2022, November 6). Italian dream (Season 2, Episode 2) [TV series episode]. In White, M. (Executive producer), The White Lotus. HBO.

Introduction and Background

When deciding which videos to use for our analysis, there was an initial limitation to our study related to cropping longer videos for brevity and how that would affect audience perceptions of the apologies. With Colleen’s video, for example, we cropped her 10-minute video into a 4-minute video and decided that the middle of the video was a good way to gauge the overall language use. This decision aligns with Ambady and Rosenthal’s concept of thin slices, which suggests that people can accurately judge complex traits such as sincerity or emotional state from brief snippets of behavior. By focusing on a representative portion of the video, we aimed to preserve the essence of the apology while still enabling meaningful analysis grounded in the reliability of these short behavioral samples. (Ambady & Rosenthal, 1992)

Our contribution lies in bridging rhetorical analysis with audience perception, using YouTube apology videos as a case study to explore how sincerity is communicated and interpreted in digital spaces. By analyzing apology videos from these “influencers,” we aim to investigate not only what these public figures say, but how they say it (visually, emotionally, and linguistically). Our thesis is that public apologies are performative acts in which specific communicative choices (like a tearful tone, minimal makeup, or carefully crafted phrasing/scripting) shape whether an audience sees the apology as genuine or manipulative. This project sheds light on how the intersection of image, tone, and language affects credibility in the age of viral accountability and offers a nuanced perspective on the performance of remorse in online culture.

Methods

To examine how public figures convey sincerity in their apology videos, we selected three widely circulated YouTube apologies from the previously mentioned figures. We then distributed these clips to 30+ college students via an open-ended and multiple-choice Google form survey. Participants were asked to reflect on their impressions of the apologies’ sincerity, whether they believed the public figure should be forgiven, and what verbal or nonverbal elements shaped those judgments. Our analysis was guided by two core theoretical frameworks: interpersonal apology theory and Benoit’s image repair discourse (Sandlin, J. K. et al., 2018). Interpersonal apology theory, rooted in communication studies, outlines five key components of a sincere apology: Admitting fault – clearly acknowledging responsibility for the offense, Acknowledging harm – recognizing the damage done to others, Expressing remorse – showing emotional regret and guilt, Asking for forgiveness – signaling a desire to make amends, and Offering compensation or corrective action – providing a tangible or verbal commitment to change.

These components emphasize the relational and emotional dynamics between the offender and the audience, underscoring that sincerity is not just about what is said, but how it’s expressed. To evaluate rhetorical strategies, we applied Benoit’s image repair discourse theory(Sandlin, J. K. et al., 2018). This framework categorizes how individuals and organizations respond to crises through specific strategies, including: Denial (refusing responsibility or shifting blame), Evasion of responsibility (claiming lack of intent or knowledge), Reducing offensiveness (minimizing the severity or attacking accusers), Corrective action (promising to fix the issue), and Mortification (openly admitting guilt and asking for forgiveness).

These strategies are often used in combination and serve to repair the speaker’s public image. By identifying which of these strategies were present in the videos, we could compare them with how participants responded in terms of perceived sincerity and forgiveness.

We also paid attention to nonverbal and contextual cues, such as facial expressions, vocal tone, emotional displays (e.g., crying), clothing, setting, and even the presence or absence of makeup. These elements were analyzed to understand how visual and tonal choices contributed to the perception of sincerity, whether, for instance, a more casual appearance or a somber tone enhanced credibility. We used a dual-method approach to analyze how public figures use specific linguistic and visual strategies in their public apology videos on YouTube to influence the audience’s perception of sincerity, accountability, and forgiveness.

Results and Analysis

Drawing on Benoit’s image repair discourse theory and interpersonal apology theory, we performed a content analysis of the three YouTube videos, focusing on four specific aspects: the influencers repetition of performative verbs (“sorry” or “apologize”), their emotional appearance, denial, and personal presentation to help us better visualize and interpret the survey results (Sandlin, J. K. et al., 2018). We then used a Likert-type scale of 1-10 to measure our perceived perceptions of these four variables (see Table 1), rating 10 as the highest level perceived and 1 as the lowest level perceived. For example, in Table 1, Laura Lee received a 10 for Repetition Level of performative verbs, meaning she repeated the verbs “sorry” and/or “apologize” over ten times. 

Table 1

Perceived level of linguistic and visual features in influencer apology videos (1 = low,10 = high). Levels rated are based on content analysis of all three apology videos.

	Logan Paul	Colleen Ballinger	Laura Lee
Repetition Level of Performative Verbs	6	1	10
Visually Emotional Level	5	2	9
Denial Level	1	8	1
Personal Presentation Level	1	3	1

Survey Results

The other method that we used in our dual method approach was an open-ended online survey. To better visualize how the audience rated the sincerity/accountability of the video, we included one of the responses to our question asking the audience “On a scale from 1 to 7, how sincere/accountable do you find the video?” (1 = not sincere at all, 7 = took full accountability). Highlighting the most significant findings from this question, we found that 60% of surveyors did not find Colleen Ballinger to be sincere/accountable (see Pie Chart 1). We then found that 50% of surveyors found that Logan Paul was somewhat sincere/accountable (see Pie Chart 2). Finally finding that 100% of surveyors did not find Laura Lee sincere/accountable (see Pie Chart 3).

Pie Chart 1

Measuring audiences’ perception of the sincerity of Colleen Ballinger’s apology video (respondents either choose 1 or 3).

Pie Chart 2

Measuring audiences’ perception of the sincerity of Logan Paul’s apology video (here we see a bit more variation in responses).

Pie Chart 3

Measuring audiences’ perception of the sincerity of Laura Lee’s apology video (here we see a unanimous decision).

Measures of Audience Perception:

To better understand the significance of the survey responses and to have clear numerical data, we then used a Likert-type scale of 1-10 to quantify the audience’s free response answers (see Table 2).

Table 2

Audiences’ perceived level of sincerity, forgiveness, accountability, and their changed perception (1 = low, 10 = high). Levels rated are based on free-response answers from open-ended surveys.

	Logan Pual	Colleen Ballinger	Laura Lee
Perceived Sincerity Level	5	2	3
Changed Perception Level	2	1	1
Forgiveness Level	3	1	2
Perceived Accountability level	7	5	2

We, from quantifying audiences free-response questions into a Likert-type scale, were able to see a clear trend. In the videos where the influencers received higher numbers on our content analysis scale, audience members were more likely to rate sincerity levels lower. Meaning, influencers who were visually more emotional, used more repetition of words such as “sorry”, and who expressed more denial of wrongdoing, were found by the audience to have less sincere apologies. The audience was also likely to rate the accountability level lower (see Table 2). For Logan Paul, responses were more mixed, though they mostly leaned negative or unchanged. For Laura Lee, the majority of participants reacted very negatively, describing the apology as overly emotional, manipulative, or performative. Finally, for Colleen Ballinger, we found that the apology reinforced or worsened existing negative opinions, even if they had already stopped watching her (likely due to the use of a guitar and breaking out into song, which can come off as dismissive and playful during a serious situation). Overall, we were able to see that Logan and Laura took more of an interpersonal route of apology (5/5 components of IAT), whereas Collen was geared more towards image repair (4/5 components of IRDT), which heavily affected audience perception of sincerity.

Some things to note when looking at such apologies are that “The medium is the message”. This reminds us that how an apology is delivered (through a platform like YouTube) shapes audience perception just as much as the content of the apology itself. (McLuhan, 1964)

Discussion and Conclusion

In conclusion, we considered audience commentary on YouTube as part of the broader discursive context. Comment sections often revealed whether viewers thought the apology was believable or performative, and frequently referenced the public figure’s prior reputation. This ties into the two-step flow model, which suggests that public opinion is shaped not just by media content but by influential opinion leaders (for example; top commenters or media coverage) who interpret and amplify messages for others. We also acknowledged the mass-personal nature of YouTube apologies (where personal disclosures are made in mass communication formats) and how this shapes expectations of authenticity and vulnerability. (Lazarsfeld, Berelson, & Gaudet, 1944)

References

Choi, G. Y., & Mitchell, A. M. (2022). So sorry, now please watch: Identifying image repair strategies, sincerity and forgiveness in YouTubers’ apology videos. Public Relations Review, 48(4), 102226. https://doi.org/10.1016/j.pubrev.2022.102226.

Holmbom, M. (2015). The YouTuber : A Qualitative Study of Popular Content Creators (Dissertation). https://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-105388.

Ohbuchi, K.-I., Watanabe, N., & Narisawa, K. (2013). Effects of timing and sincerity of an apology on satisfaction and changes in negative feelings during conflicts. Communication Reports, 26(1), 2–11. https://doi.org/10.1080/10570314.2013.770160.

Uoti, K. (2022). Apology as a Speech Act Set: Apology strategies of social media influencers in the context of the Covid-19 Pandemic (Doctoral dissertation, Master’s Thesis, University of Turku]. UTUPub. https://urn.fi/URN:NBN: fi-fe2022042530307).

Matheson, B. (2023). Fame and redemption: On the moral dangers of celebrity apologies. Social Philosophy. https://epub.ub.uni-muenchen.de/108820/1/Journal_of_Social_Philosophy_-_2023_-_Matheson_-_Fame_and_redemption__On_the_moral_dangers_of_celebrity_apologies.pdf

Sandlin, J. K., & Gracyalny, M. L. (2018). Seeking sincerity, finding forgiveness: YouTube apologies as image repair. Public Relations Review, 44(3), 393–406. https://doi.org/10.1016/j.pubrev.2018.04.007.

Introduction and Background

Our project examines how responses to cancel culture operate within and reinforce gender norms. Male misconduct is downplayed through humor and trivialization, and women are held to a higher standard of morality. Further, we argue that cancel culture perpetuates existing power imbalances between men and women. Current research examines cancel culture from moral and emotional lenses, but fails to consider gender’s role in shaping public responses. Our project bridges this gap by analyzing qualitative thematic and linguistic data from social media comments and interpreting how gender influences the reactions.Cancel culture is the widespread phenomenon of cancelling, the public shaming and withdrawal of support from figures engaged in unacceptable behavior (Minkkinen, 2024). Reactions range from moral activism to cyberbullying, and support to moral outrage (Forster & Spitz, 2015). Research shows that cancel culture reflects gender norms. Sailofsky (2021) and Bouvier (2020) found that men’s misconduct is mitigated through “redemption talk.” Ochs & Taylor (1996) show men use narrative control to preserve authority. Dubrofsky (2011), Marwick & Caplan (2018), Cohen (2010), Aprill (2022), Banet-Weiser (2018), and Sobande (2021) show us that women face strict media surveillance around their physical appearance, emotional expression, and perceived morality. Cameron (2005), Ng (2020), and Kosnik & Feldman (2019) demonstrate how gendered expectations shape language and digital expression.

Methods

Our study applies qualitative and thematic discourse analysis to public commentary on four canceled public figures. James Charles, Dr. Disrespect, and Colleen Ballinger were all canceled for inappropriate communication with minors, while Chrissy Teigen was canceled for online harassment and bullying. We collected nondiscriminatory data from TikTok and Reddit, then randomly selected six comments per public figure from the collected data. Our analysis focused on the emerging patterns found in themes, tone, and sentiment. We then noted how the patterns we identified reflect gendered expectations and reactions in common culture. Thematic analysis was established by coding each comment for language, tone, and sentiment patterns. These codes were grouped into themes such as character attack, moral absolutism, victim reframing, body shaming, suspicion of feminine personas, redemption, minimization, believability, audience support, and jokes about appearance. We illustrated our findings with four graphs that were created by counting the occurrences of each theme per person and gender across the sample. Our graphs visually represent and compare the most common reaction themes and how they vary by case and gender.

Results and Analysis

Our findings revealed clear patterns of gendered responses in cancel culture discourse, supported by our thematic and linguistic data.

Male Figures – The responses to James Charles and Dr Disrespect were centered around humor and redemption. Charles and Disrespect received seven instances of redemption talk, five of minimization, and four of audience support. Commenters joked about the men nine times, using humor to deflect accountability, aligning with Sailofsky’s (2021) theory of minimizing male misconduct. Our findings suggest a pattern of forgiving and rationalizing men’s misconduct.

Female Figures – Chrissy Teigen and Colleen Ballinger were subjected to severe judgments. They faced seven attacks on their character, four of moral absolutism, and four of body shaming. The responses focused on the women’s appearance and morality, reflecting the severity of emotional surveillance of women in media (Dubrofsky, 2011). The data also revealed only three instances of jokes in the responses. Call-outs were also much more common for women than men, who had nine occurrences compared to two, highlighting a harsher scrutiny and lower tolerance for women and their misconduct.

Public defenses also suggested a gendered pattern. Men were defended through the minimization and trivialization of their actions and allowances for redemption. Supporting the theory of a double standard in forgiveness and trust, the women did not receive public support or defenses in the responses.

The findings support our thesis by demonstrating how responses to cancel culture are shaped by gender norms. Women are evaluated by what they do and who they are perceived to be. On the other hand, men are more likely to be separated from their actions and allowed to return to their careers. The unequal responses found in the cancellation discourse limit women’s ability to save their reputations from permanent ruin. This suggests that cancel culture does not function as a neutral or impartial form of accountability. Instead, cancel culture is shaped by gender norms and expectations. Rather than responding based on the severity of the misconduct, the public’s responses were contingent on gendered norms and assumptions that reflect who is viewed as redeemable or not.

Discussion and Conclusion

Our findings illustrate how cancel culture functions as a way to police morality, identity, and credibility in a gendered way. Male public figures like James Charles and Dr Disrespect were discussed using themes of understanding, minimization of their misconduct, and the allowance for their redemption. In contrast, responses to Chrissy Teigen and Colleen Ballinger centered on moral absolutism, character attacks, and body shaming. Our analysis suggests men are judged by their perceived intent or potential for change, while women are evaluated based on who people think they ought to be. Further, our findings suggest the personas of the influencers we examined add context to gendered standards. This is particularly important with a figure like James Charles. Charles is a male influencer who goes against traditional masculinity norms. Perhaps due to his perceived femininity and sexual orientation, he did not receive the same protection from appearance-based jokes as men like Dr Disrespect, who personifies stereotypical masculinity. However, his status as a male may have shielded him from the level of cruelty directed at women, particularly Chrissy Teigen, whose appearances were targeted ruthlessly.

The patterns we identified support existing literature on gendered surveillance, digital public shaming, and current understandings of respectability politics. The consistency in findings across public figures also supports the idea that cancel culture is not the equalizing accountability that people understand it to be. Instead, it reinforces double standards across genders. Women are more likely to face moral scrutiny tied to their appearance and character, while men are afforded redemption. Our research expands the understanding of online accountability and public discourse by exposing how cultural scripts of gendered forgiveness, credibility, and judgment persist even in places meant to challenge norms. As participants in online culture, we play a role in the narratives that shape the perception of public figures. Being mindful of how we respond to public figures’ misconduct, through the language we use and standards we uphold, can help challenge and reform gender bias in media scrutiny. Holding people accountable is important, but we must do so objectively and with self-awareness in order to foster an equitable digital society.

Figure 1: Thematic Findings in Cancelled Women’s Discourse

Figure 2: Thematic Findings in Cancelled Men’s Discourse

X-axis: Categories Identified in Comment Sections  Y-axis:  Number of Occurrences

Figure 3: Linguistic Findings in Cancelled Women’s Discourse           

Figure 4: Linguistic Findings in Cancelled Men’s Discourse

X-axis: Linguistic Categories Identified in Comment Sections Y-axis:  Number of Occurrences

YouTube Videos on Cancel Culture

1. James Charles Misconduct

James Charles Career Is Basically Dead. Why?

2. Discussion Surrounding Dr Disrespect’s Misconduct Dr Disrespect Situation Just Got Worse
3. Chrissy Teigen Misconduct

The rise and fall of ‘undercover bully’ Chrissy Teigen

4. Colleen Ballinger Misconduct Colleen Ballinger Controversy: The FULL Story Break Down | E! News
5. Problems in Cancel Culture TED Talk

Cancel Culture: The Decline and Disconnect Within Society  | Jasmine Iacullo | TEDxYouth@NBPS

Keywords

1. Gendered forgiveness: the observed phenomena of the act or severity of forgiveness being correlated to the presented gender of the accused or wrongdoer
2. Asymmetric accountability: Individuals or groups are held responsible for their actions asymmetrically. The consequences, repercussions, or scrutiny they face are not equal to those of other individuals or groups.
3. Misconduct minimization: downplaying the significance or severity of one’s wrongdoing through various methods to reduce guilt, shame, or accountability
4. Aesthetic and emotional surveillance: The act of monitoring and analyzing a public figure’s appearance and emotions, especially women’s, to control, influence, or make judgments comparative to societal and gender norms
5. Redemption: the successful offset or compensation for a defect, misconduct, or failure

References

Aprill, Julie, “The Effects of Moral Violations on Parasocial Relationships with Influencers” (2024). Electronic Theses and Dissertations. 3981. https://digitalcommons.library.umaine.edu/etd/3981

Banet-Weiser, S. (2018). Empowered: Popular Feminism and Popular Misogyny. Duke University Press. https://doi.org/10.2307/j.ctv11316rx

Bouvier, G. (2020). Racist call-outs and cancel culture on Twitter: The limitations of the platform’s mechanisms for accountability. Discourse, Context & Media, 38, 100431. https://doi.org/10.1016/j.dcm.2020.100431

Cameron, D. (2005). Language, Gender, and Sexuality: Current issues and new directions. Applied Linguistics, 26(4), 482–502. https://doi.org/10.1093/applin/ami027

​​Cohen, E. L. (2010). Expectancy violations in relationships with friends and media figures. Communication Research Reports, 27(2), 97–111. https://doi.org/10.1080/08824091003737836

De Kosnik, A., & Feldman, K. (Eds.). (2019). #identity: Hashtagging race, gender, sexuality, and nation. University of Michigan Press https://library.oapen.org/bitstream/handle/20.500.12657/24497/9780472901098.pdf

Dubrofsky, R. E. (2011). The Surveillance of Women on Reality TV: Watching The Bachelor and The Bachelorette. Lexington Books https://digitalcommons.usf.edu/spe_facpub/646

Marwick, A. E., & Caplan, R. (2018). Drinking male tears: Language, the manosphere, and networked harassment. Feminist Media Studies, 18(4), 543–559. https://doi.org/10.1080/14680777.2018.1450568

Minkkinen, L. (2024b). “Girl, you’re still cancelled”: analysis of public ‘cancel culture’ Apologies and responses. https://jyx.jyu.fi/jyx/Record/jyx_123456789_95151#description

Ng, E. (2020). No grand pronouncements here. . .: Reflections on cancel culture and digital media participation. Television & New Media, 21(6), 621–627. https://doi.org/10.1177/1527476420918828

Ochs, E. and Taylor, C.” (1996) The Father Knows Best:’ Dynamic in Family Dinner Narratives.” In Gender Articulated: Language and the Socially Constructed Self. K.Hall (ed). Routledge. Pp.97-121.

Sailofsky, D. (2021). Masculinity, cancel culture and woke capitalism: Exploring Twitter response to Brendan Leipsic’s leaked conversation. International Review for the Sociology of Sport, 57(5), 734-757. https://doi.org/10.1177/10126902211039768 (Original work published 2022)

Sobande, F. (2021). Spectacularized and branded digital (re)presentations of Black women on YouTube. Television & New Media, 22(2), 131–146. https://doi.org/10.1177/1527476420983745

Tukachinsky Forster, Rebecca & Spitz, Daniel. (2025). I Love You, but I Have Got to Cancel You: Psychological Consequences of Participation in Cancel Culture. Psychology of Popular Media. 10.1037/ppm0000598.

Research Question: LOL vs 😂: How Digital Laughter Varies Across Generations

Introduction and Background            

For this project, the target population will be Generation Z and Generation Alpha. Generation Z refers to those born in 1997 up until those born in 2012. Generation Alpha refers to those born in 2010 up until those born in 2024. With digital communication being one of the main sources of communication in today’s world, we want to explore how the two most recent generations, more specifically the younger generations, have become linguistic innovators. In these more recent generations, it has become evident that people now create their own rules for punctuation and have normalized not using punctuation (Guo, 2016). Both Generation Z and Generation Alpha have grown up using technology. From smartphones to social media to messaging platforms, both generations have been surrounded by technology as a main source of communication. In typical face-to-face communication, people physically show their emotions, whether they are happy, sad, excited, shocked, angry, humorous, etc. However, in today’s digital world, those non-verbal cues like facial expressions and vocal intonation are replaced by the use of emojis and expressions of amusement. The specific expressions we are going to focus on for this project are “LOL, LMAO, and HAHA,” and when they are being used by Gen Z and Gen Alpha, as well as certain emojis that pertain to humor. It is made clear that age is something that significantly impacts how emojis are classified (Chen et al., 2024). It is well established that textese are commonly used (Sánchez-Moya & Cruz-Moya, 2015). Both generations tend to use these phrases digitally quite often. “LOL” is an acronym that means laugh out loud, and over time, it has been used as a tone marker instead of an actual sign of laughter. “LMAO” is a more bold and exaggerated phrase to signal laughter, and “HAHA” is also used to express genuine laughter. Understanding the reasoning behind these linguistic patterns will allow for more expansive knowledge on how digital communication norms evolve and continue to grow amongst the new generations and today’s youth.

Methods

Our approach was to collect data using our two population groups. These participants were chosen due to their generational ages and the majority of individuals had relationships to the researchers. These relationships consisted of group members and included siblings, roommates, friends, classmates and cousins.

Next, the materials used throughout our research were observable data through screenshots of text messages and google form surveys to collect individual data. We first collected the observable data using real life conversations within text messages and organized these into the two generational groups. After collecting observable data we created google form surveys to either discredit or prove our origin hypothesis. The survey used the same questions for both generations and included 10 responses from Gen Z and 10 responses from Gen A participants. This survey began by asking those engaging in the study to indicate whether they are Gen Z or Gen A. Then, the succeeding question followed with a “Select All That Apply” to indicate which styles (i.e. lol/LOL, haha/HAHA, lmao/LMAO, 😭, 😂, 💀) of digital laughter they use. Additionally, the next questions after those included Likert scales ranging from (1) Not at All to,  (5) Frequently. This scale determined the likelihood and frequency of each form of digital laughter and allowed us to measure the different forms of usage between each generation. The survey questions are specific to ensure detailed conclusions of responses, which changes in polarity to see visible opposing viewpoints. Although, due to using the survey method, the participants must have personal accountability to respond truthfully. All responses collected were anonymous and included all participants to agree on responses being used publicly.

Furthermore, the data collected within this methodological procedure was coded using all participants’ responses to understand the opposition among generations. Using the survey responses we divided each individual reply into Gen Z and Gen A categories. We then analyzed each form, marking down each response from all 13 questions. This is how we gathered the differences between frequency and style of digital laughter used in online communication styles between Gen Z and Gen A. This procedure allowed us to determine statistical data in which we could affirm or negate the hypothesis from our observable data.

Results and Analysis

Through the collection of our data, we were able to confirm details within our hypothesis as well as gather new information that supported our research study. Our results suggested that while both generations profusely engage in online laughter, there is a difference in how they use it and in what forms. For example, results from our survey collection indicated higher use of overall online laughter particularly verbal laughter amongst Gen Z with phrases such as “lol/LOL” and “lmao/LMAO” receiving 70-80% usage compared to that of Gen A’s (55% and 11%). Gen A on the other hand relied on emoji usage more so than Gen Z. However as per survey data, Gen A participants were less likely to use digital laughter than their counterparts. These results were portrayed through our bar graph which compared digital laughter use among the generations with Gen Z represented in teal color and Gen A through blue. When comparing verbal laughter and emoji usage, our graph depicted patterns of Gen Z participants selecting verbal responses with ranging levels of 2-4 while Gen A participants selected fewer verbal responses and drifted to emoji usage at higher levels. As stated previously, these results aided in identifying patterns between the generations and noting the differences in expression through online laughter.

Based on the results, we see that while there are some differences between the two generations, there are no big differences. Usually when comparing 2 different generations, there are clear and distinct differences, often big ones. However, we found that the differences are hardly any and relatively small. The small differences can show how similar and closely related these 2 generations are in regards to digital communication and emoji usage. The gap between generations is not large, they have some nuances but it doesn’t stop them from effectively communicating with each other. Our findings show that both Gen Z and Gen Alpha frequently use digital laughter, but slightly differ in how and with what tone they express it. Emojis allow us to speak in ‘gestures’ through digital platforms and let conversation flow more freely (McCulloch, 2019). It is almost as if texting is the new speaking in-person in today’s day and age. While texting using digital laughter, in a study done on Whatsapp, researchers found that this turn-taking was similar to face-to-face conversations (Petitjean & Morel, 2017).

Discussion and Conclusion

Based on the results, we see that while there are some differences between the two generations, there are no big differences. Usually when comparing 2 different generations, there are clear and distinct differences, often big ones. However, we found that the differences are hardly any and relatively small. The small differences can show how similar and closely related these 2 generations are in regards to digital communication and emoji usage. The gap between generations is not large, they have some nuances but it doesn’t stop them from effectively communicating with each other. Our findings show that both Gen Z and Gen Alpha frequently use digital laughter, but slightly differ in how and with what tone they express it. Emojis allow us to speak in ‘gestures’ through digital platforms and let conversation flow more freely (McCulloch, 2019). It is almost as if texting is the new speaking in-person in today’s day and age. While texting using digital laughter, in a study done on WhatsApp, researchers found that this turn-taking was similar to face-to-face conversations (Petitjean & Morel, 2017).

As Gen Z college students ourselves, we are aware that the majority of Gen Z had a childhood without texting, but as we grew older, we started to text more using emojis and digital laughter as those evolved. While Gen Alpha grew up with texting, we wanted to explore if that dynamic made a difference in the use of digital laughter between the two generations. Early Gen Z are digital natives but vividly remember analog life. They’re the last generation who remembers what it was like to have a flip phone as their first device, use physical CDs or DVDs, write handwritten letters or notes, and live without constant access to everything online. Yet, they adapted to technology so quickly that they can navigate both analog and digital worlds fluidly. Gen Alpha however, cannot relate to that since emojis and digital laughter have been around since the beginning of that generation. For example, the “tears of joy” emoji, or the first laughing emoji was created in 2010, which is the beginning of Gen Alpha. Digital laughter amongst both generations is a crucial part of online communication and a way of expression.

References:

Chen, Y., Yang, X., Howman, H., & Filik, R. (2024). Individual differences in emoji comprehension: Gender, age, and culture. PLOS ONE. https://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0297379

Guo, J. (2016). Stop. Using. Periods. Period. – The Washington Post. The Washington Post. https://www.washingtonpost.com/news/wonk/wp/2016/06/13/stop-using-periods-period-2

McCulloch, G. (2019, June 1). Because the internet: Understanding the new rules of language. Explorations in Media Ecology. https://doi.org/10.1386/eme_00039_5

Petitjean, C., & Morel, E. (2017, January 25). “hahaha”: Laughter as a resource to manage WhatsApp conversations. Journal of Pragmatics. https://www.sciencedirect.com/science/article/pii/S0378216616302594

Sánchez-Moya, A., & Cruz-Moya , O. (2015, April 21). WhatsApp, Textese, and moral panics: Discourse features and habits across two generations. Procedia – Social and Behavioral Sciences. https://www.sciencedirect.com/science/article/pii/S1877042815013786

Introduction and Background

Texting has become a primary mode of interaction, and with it, a new kind of nonverbal language has emerged. As digital communication becomes more widely used to communicate, the rules for how one should interpret a text aren’t always agreed upon. Their interpretations vary due to our age and experience within our generational environments. What feels natural and obvious to one generation can feel informal, confusing, or new to another.

This study aims to investigate how two generations, Gen X and Gen Z, navigate digital body language. Our study begins by introducing digital body language (DBL), the way people subtly and non-verbally communicate and express themselves through their use of capitalization, punctuation, emojis, slang, acronyms, response times, etc., in constructing online messages (Irshad, 2024). Followed by an overview of our research methods and implementation. Then we present the key findings from the participants’ responses, highlighting generational differences in interpretation, tone, responses, and emoji usage. Finally, we review the broader implications of these different generations and discuss their ability to adapt when navigating unfamiliar digital cues and how their responses may be shaped by varying levels of cultural immersion in the digital age. 

As texting has become a dominant form of communication, people have developed new ways to express tone and emotion without speaking. These include punctuation, emojis, and message interpretations, which researchers often call digital body language. While these features help fill in the gaps left by the absence of facial expressions and vocal tone, their meaning can vary depending on who is reading them.

Prior studies have shown that younger generations tend to use and interpret these digital cues differently from older ones. For example, Gen Z, who grew up immersed in texting and social media, often assigns emotional weight or social meaning to features like lowercase typing or a final period. The New York Post report noted that, to young people, “using a period in messaging now looks rather emphatic, and can come across as if you’re quite cross or annoyed” (Frishberg, 2020). For example, Gen Z, who grew up immersed in texting and social media, often assigns emotional weight or social meaning to features like lowercase typing or a final period. In contrast, Gen X may interpret the same features through a more literal lens, viewing them as grammatical or functional rather than expressive. Binghamton University psychology research found that texts ending in periods were perceived as less sincere than those without punctuation, suggesting that punctuation alone can change emotional tone (Klin, 2016).

Despite growing research in online communication and generational differences, there is still limited understanding of how digital body language is interpreted across generations in casual text exchanges. Our study focuses on this gap by comparing how Gen Z and Gen X understand and use key digital features in message-based conversations. We aim to uncover whether the same text message is read differently depending on the generation of the reader, and what this might reveal about changing norms in digital communication.

Figure 1

Generational Differences in Emoji Use and Interpretation

TikTok – Generational Emoji Usage

Note. A viral TikTok humorously captures the difference between generational interpretations of emojis. The creator of the video explains, “we really gotta teach our parents how to properly use emojis…” then shows the text he received, which said “your uncle Mark died [skull emoji]”. Gen Z often uses the skull emoji to express exaggerated reactions, humor, or sarcasm, unlike Gen X, which interprets this emoji as literal death, which contributed to the message sounding comical and inappropriate.                 

Figure 2

Generational Differences in Texting

YouTube Video – Texting Across Generations

Note. A snippet from a podcast episode discusses how our digital interactions are shaped by our relationships with others. One of the speakers talks about how the way she positions her digital body language depends on the person she’s texting, saying that “different people bring out different texting sides of me” (The Council, 2025). She then goes on to talk about how this switch-up is especially prevalent when she is texting members of older generations, such as her father. When messaging her father, she’s observed that their text conversations are based entirely on message content and practicality, and there’s less of an emphasis on tone and nuances.

Methods

Data was gathered from ten participants, five members of Generation X and five members of Generation Z, through semi-structured interviews. Each interview consisted of the same exact format, but was semi-structured in the way that the design encouraged follow-up questions and elaboration from interviewees. The interviews were divided into four parts: (1) demographic and technology profile, (2) message-interpretation task, (3) punctuation and capitalization, and (4) emoji-choice scenario. The first section collected basic background information, such as age, gender, and technology usage habits. The second part presented five different text messages, written by us. Each message contained one or more features of digital body language—punctuation marks, capitalization or lack thereof, emojis, and acronyms. We showed each participant these messages, one by one, and asked them what tone they perceived and how they would respond. The third exercise was also made up of messages we designed, but it focused on only punctuation and capitalization features, such as capitalizing or not capitalizing the first word of the message, capitalizing the entire message, as well as using a period, exclamation point, or no punctuation at all. It also featured three messages with the same exact word content, but two different versions of each message: Version A and Version B. What set these versions apart were their differing levels and use of punctuation and capitalization. After presenting these two different versions, we then had participants choose which version they think is friendlier and explain why. In the final section, participants were presented with eight different emojis to choose from and asked to choose which two they’d use to respond to a message from a friend announcing a new job. Once interviewees chose their top emojis, we asked them two follow-up questions, which were why they picked those emojis and if their choices would change if the message had come from an acquaintance rather than a friend. Through these interviews, we analyzed how members of these different generations interpreted and expressed various online non-verbal cues, also known as components of digital body language. We sought to identify generational differences and patterns in how each generation processed these features.

Results and Analysis

Based on what we have collected, we have obtained results that can help us make inferences out of the differences between Gen X and Gen Z. We found most importantly that in relation to punctuation and formatting, a period (e.g., “Okay.”) is interpreted as annoyance, finality, and/or passive aggression among our Gen Z interviewees. This follows our literature base where, according to Teresa Apgar, “research suggests that younger individuals interpret text-final periods to be more negative in tone, while older individuals interpret it to be neutral” (Apgar, 2022, 1). When it came to tone shift and identification, we found that Gen Z had a strong sensitivity to changes in tone, particularly when familiar contacts shift from informal to formal texting styles that are reflected in the punctuation and capitalization. Another piece of research literature that helps us understand passive-aggressivity mentioned previously is the situation where both subject Gen Z’ers are observed: “Lisa evaluates Nelly’s [non-punctuation lowercase] reaction as inappropriate by repeating her utterance and adding an iterated question mark” (Busch, 2021, 38). Conversely, lowercase messages without periods were deemed more so as friendly and casual among Gen X interviewees. Now, when it comes to emoji use and interpretation, we found that emoji choice changes depending on the relationship for Gen Z (e.g., [flame emoji] is for close friends). This relationship-dependent emoji use selection was not as prevalent in Gen X responses. We theorize that this lack of variety in emoji usage was probably due to Gen X’s simple lack of exposure time to the emoji features. See Figure 3 below.

Figure 3

Digital Communication Cue Interpretation: Gen Z vs. Gen X

Note. This chart summarizes/simplifies our results as a whole according to relevant categories (extreme left).

Discussion and Conclusion

Our exploration into generational interpretations of digital textual communication demonstrates a divide in how meaning is constructed and perceived across age groups. Through interviews with members of both Generation X and Generation Z, we observed that the tools of modern text-based communication carry different meanings depending on one’s digital fluency and cultural context.

Within Generation Z, we noticed there to be a deeper sensitivity to the emotional and social undertones of simple texting features. Even something as minute as a period at the end of a sentence could indicate a different tone or even a conflict. Generation Z interpreted texts figuratively and consistently attempted to read between the lines.

Contrastingly, Generation X  looked at the texts in a more literal and formal light. Punctuation was used for the purpose of grammar, and emojis were viewed straightforwardly with little subtext. Some members were aware of the subtext, but did not spot it as consistently as their Gen Z counterparts.

A key insight of our study is that, while members of both generations utilize and recognize non-verbal cues in text, Gen Z appears to have developed a more nuanced form of digital literacy. Their fluency allows them to read undertones that older generations would not as easily detect, which may allow them to engage in complex, layered conversations entirely through digital platforms, where meaning is often conveyed as much by how something is said as by what is said.

As digital communication keeps evolving, the language and meaning embedded within it follow. Our findings suggest that generational gaps in digital interpretation are not just a result of age, but additionally, cultural immersion. Many members of Generation Z grew up using computers, tablets, and phones, where communication was quite text-heavy and rich with emoji use. Generation X was not introduced to the same kind of communication until their adult lives; they are perfectly capable of adapting, but the trend for this group is to interpret digital language through a more traditional, literal lens.

References

Apgar, C. T. (2022). Wait wdym?: Examining the (mis)perception of emotional valence in text messaging across generations (B.Phil. thesis, University of Pittsburgh). University of Pittsburgh.

Busch, F. (2021). The interactional principle in digital punctuation. Discourse, Context & Media, 40, 100481. https://doi.org/10.1016/j.dcm.2021.100481

Danger. [@natedanger]. (2021, April 2). Rip uncle mark #family #emoji #fyp [Video]. TikTok. https://www.tiktok.com/t/ZP8MEvPnM

Frishberg, H. (2020). Young people don’t trust anyone who uses this punctuation mark. (2020, August 24). New York Post. https://nypost.com/2020/08/24/young-people-dont-trust-anyone-who-use-this-punctuation-mark/

Irshad, S. (2024). Deciphering digital body language and the Gen-Z in new normal. Creative Saplings 2(10), 31-41. https://creativesaplings.in/index.php/1/article/view/498

Klin, C. (2016, June 12). Study: Punctuation in text messages helps replace cues found in face-to-face conversations. Binghamton University News. https://www.binghamton.edu/news/story/873/study-punctuation-in-text-messages-helps-replace-cues-found-in-face-to-face

The Council. (2025, January 31). Texting across generations: The surprising differences [Video]. YouTube. https://www.youtube.com/watch?v=JcC5_VWPf4U

Introduction and Background

Communication serves as a powerful tool to observe and interpret hidden messages. In the workplace, communication depending on various parts of one’s identity may affect roles, actions, or speaking styles in professional settings; specifically gender and level of experience influence how individuals speak and are addressed. Discourse plays a large role in language and gender studies “because many masculine and feminine aspects of speech emerge during communication” (Kizi, 2024). Social hierarchies and biases can be shown through something as simple as a different tone of voice or body movement. Our research dives into these different forms of communication by gender and level of experience of the employee being addressed.

Before conducting our own research, we searched for information that could be beneficial for us to evaluate to help make conclusions. Past research showed that women and men often use different communication styles; men use report talk, which is assertive and goal-oriented, while women use rapport talk, which places importance on connection and collaboration (Mohindra and Azhar, 2012). Relating back to the workplace, these differences can lead to how workers are valued and judged in professional settings. For example, rapport talk, typically used by women, is seen as less skilled than report talk, often used by men. Additionally, communication that stereotypically aligns with feminine traits is often overlooked in performance evaluations and leadership assessments (Chai, 2022). While we were able to find plenty of research examining communication by gender in the workplace, we were unable to find research that studied age and level of experience that influence communication in the workplace. We were curious to examine the intersection between experience and gender in the workplace. Do older, more experienced women get spoken to with more respect? Do newer female employees experience more condescending tones than their male counterparts? These are questions we were hoping to answer in our own research methods.

Methods

When conducting this study, we first formulated our hypothesis: “How do both gender and professional experience influence tone, word choice, and body language used by supervisors when addressing employees in the workplace?” We wanted to come to a conclusion regarding communication for those of different genders and different positions (hierarchy-wise) in workplaces. Our method consisted of conducting a poll via Google Form, asking our respondents the same set of occupation-based questions. Our questions began with basic intro topics: age, gender, and career experience. We then dove into more personal subjects, creating a list of yes or no questions surrounding communication within the workplace. The questions focused on how each participant is treated and addressed based on their gender. We chose to make each question very straightforward (not open-ended) to avoid any biases in the study. We chose to send this survey to four people each, two of each gender. We wanted to have an equal number of males and females in the study to make our outcome as accurate and honest as possible. We chose participants of all different ages and employment levels. Some were interns, some were in the service industry, some worked high-paying corporate jobs, and some even worked for themselves. The variation amongst every participant provided us with the most transparent results. By focusing on all different backgrounds and levels of experience, we could make a consensus about gender and many different workplaces in 21st-century America. Although our questions required a yes or no response, we focused our topics around rapport and report, displaying differences amongst age, sex, and gender.

Results and Analysis

When our participants were asked if they notice differences in communication styles between their male and female bosses, 64.3% of participants replied “YES,” while 35.7% replied “NO.” One of our participants even shared that their female bosses tend to be more sincere and considerate, while their male bosses take a condescending approach in their workplace. (Johnson, 2017) This manifests itself in the specific tone used by one’s boss. We chose to ask questions about bosses of both genders to come to a consensus with report versus rapport talk. Report and rapport contribute to how each gender takes on an authoritative position, especially in an intense work environment. The results in this pie chart were supported by one of our participants, a young female intern. At the end of our survey, she wrote, “I feel like I get spoken to too harshly by my male bosses and that they give the guys that I work with much more leeway.”

Pie chart displays responses of different experiences between bosses of each gender

In addition to communication between employees and their bosses, we also factored in age to our questionnaire (as stated above). In gender-based studies, age often gets overlooked. Incorporating age as a question in our Google Form allowed us to connect the outcome to our predominantly gender-focused study. Age and gender are without a doubt connected, especially when it comes to male and female interactions on a professional level. When asking about age, 92.9% of our participants said that their age has affected their communication at work. That is almost our entire population, leaving out 7.1%. Age has a direct correlation with work experience, so it is bound to impact communication, how one is addressed by both their peers and authoritative figures, and even how they are perceived by others.

Pie chart displays correlation between age and communication in the workplaces

Younger employees are more likely to be recipients of condescending or belittling behavior. This connects to realistic factors such as lack of experience or deeper ones such as gender. Whatever the motives may be, it is inevitable that age has a direct connection to how one communicates and is communicated with in their workplace.

Discussion and Conclusion

Based on the results of our research, we found three main patterns in communication in the workplace between males and females. The first main finding is same-gender communication leads to  relaxed and informal conversation. Our evidence illustrates how females engage in rapport talk over text and this is consistent with our survey findings because about 64.3% of our respondents reported differences in communication styles between male and female bosses. In an interview, we found that female bosses were perceived as less condescending and more considerate, male bosses were described as “more self-centered.” The second main finding is that Supervisors use more formal and respectful language when addressing experienced employees. We noticed patterns of tone shifts within text conversations. This aligns with our literature review where experience and age shapes the formality in the workplace, and a polite tone may emerge toward more seasoned women. This also connects with our survey findings because 53.8% rated a 4 or 5 (on a 5-point scale) in agreement that they were addressed differently based on age or experience. The third and final theme is long-term experienced employees are spoken to with continued respect, but less formality. We found that texts between male bosses and experienced male employees are more relaxed. This aligns with Holmes and Marra (2004), relational practice evolves over time and becomes less formal with workplace bonds, and Mohindra & Azhar (2012), men adopt direct talk (report) once relationship is established. This connects to our survey results because 92.9% said their age/experience affected communication. Qualitative responses such as “I tend to feel less comfortable with male bosses” reflect same-gender or familiar dynamics. Ultimately, with our research we focused on the differences of language, tone, and communication between men and women in the workforce. Our research highlights gender roles through a lens of language. We aimed to examine the differences in language use amongst themselves, their bosses, clients, colleagues, and especially how they are addressed by their peers. We emphasized tone, word choice, and body language as a whole. Our research question was: Are women addressed with different tones, word choices, and body language than men in the workplace? We hypothesized that gender would influence workplace communication and that women are more likely to be spoken to with different linguistic and behavioral communication when compared to their male counterparts. Our hypotheses were found to be all true as our survey results demonstrated same gender communication leading to more relaxed and information conversations, supervisors being more formal with older more experienced employees, and finally, experienced employees having less formal but still respectful conversations. Our findings imply that there is only a small amount of discrimination in the workplace through communication styles or behaviors among colleagues or bosses based on one’s gender or experience. Further, when one stays longer in one job, they attain more respect and are more lenient with their formality/conversation styles. Gender can cause more formal, serious tones and closed off body language between male bosses and female workers. This is because of gender perceptions of female competence and ability to get work done. Women have to work harder to prove their capabilities and this causes women to engage in rapport talk whereas men engage in report talk. Furthermore, our findings are important because it illustrates why men and women generally use different communication styles and how it can boost their status or job performance.

References

Mohindra, V., & Azhar, S. (2012). Gender Communication: A Comparative Analysis of Communicational Approaches of Men and Women at Workplaces. IOSR Journal of Humanities and Social Science, 2, 18-27.

Chai, Y. (2022). Implicit gender-biased speeches in workplace: Embedded stereotypes and effects on women’s Career Development.

Johnson, Stefanie K. “What the Science Actually Says about Gender Gaps in the Workplace.” Harvard Business Review, 17 Aug. 2017, br.org/2017/08/what-the-science-actually-says-about-gender-gaps-in-the-workplace. Accessed 05 June 2025.

Holmes, J., & Marra, M. (2004). Relational practice in the workplace: Women’s talk or gendered discourse? Language in Society, 33(3), 377–398. https://doi.org/10.1017/S0047404504043039

Ergasheva Nozima Khasan Kizi (2024). GENDER DIFFERENCES IN COMMUNICATION IN THE WORKPLACE. Eurasian Journal of Social Sciences, Philosophy and Culture, 4 (6-1), 171-175. doi: 10.5281/zenodo.12176621