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)

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

Persuasive rhetoric around the topic of abortion varies greatly between people, in this case, the variation addressed will be that of two prominent political figures: President Trump and   Former President Biden, both of whom use persuasive rhetoric to influence people’s opinions greatly, but differ greatly in how they address the public. While the initial objective of their statements may be to inform, they also work to persuade public opinion on various subjects in hopes to garner more voter support and donations.

There is a plethora of research analyzing how political leaders use persuasive rhetoric, particularly framing, emotional appeals, and identity-based language to shape public understanding of controversial ideas. Scholars like Lakoff (2004) have demonstrated how metaphors and framing have influenced voter perception around cultural topics. Similarly, scholars like Jamieson and Campbell (2001) have analyzed how presidential rhetoric uses pathos, ethos, and logos to construct political narratives. This tendency is consistent with Homolar and Scholz’s (2019) analysis of 74 Trump campaign speeches, which shows that crisis-laden, us-versus-them narratives create ontological insecurity and mobilize voter support.

In reference to reproductive rights, scholars like Medved and Rawlines (2000) and Ginsburg (1989) have explored how abortion is framed through competing moral, legal, and religious lenses. However, we have noticed a significant gap in recent work that compares the persuasive rhetorical strategies of former President Biden and President Trump specifically within the context of abortion rights.

Our objective was to fill that gap by asking: How do former President Biden and President Trump use persuasive language to frame abortion, and is it done emotionally, logically and/or by establishing their credibility to their audience? We wanted to understand how social identity and ideology can affect political communication, especially around issues as heavily debated as abortion.

Methods

We selected six public statements, three from each leader, delivered between 2020 and 2024. They were a mix of addresses, campaign speeches, and national statements.

Biden:

• National address on the Dobbs decision (June 24, 2022)
  • Democratic National Committee speech on codifying Roe (October 18, 2022)
  • State of the Union (March 7, 2024)

Trump:

• March for Life rally address (January 24, 2020)
  • Fox News interview responding to Dobbs (June 24, 2022)
  • Formal policy statement on abortion exceptions (April 8, 2024)

While the setting and occasion of each speech varied, we did not find a difference in the rhetorical strategies used across formats. This allowed for us to compare content collectively; we performed a manual discourse analysis focusing on three rhetorical appeals: ethos (credibility), pathos (emotion), and logos (logic) as described in Aristotelian rhetorical theory (Crowley & Hawhee, 2004). To code our data, we utilized a coding framework adapted from established methods in political communication research (Jamieson & Campbell, 2001; Charteris-Black 2014). Emotional appeals like personal stories or dramatic metaphors were marked as pathos while references to values or identity and personal experiences were considered ethos. We marked statements as logos if they were factual claims including a reference to the law or cause-and-effect reasoning. We proceeded to also look at how often they used these and the tone they employed when referring to their opposition. Once we had all of this information, we took the averages of the total numbers we were able to tally from observing ethos, pathos, and logos to compare which president uses rhetorical appeals more, or if one of them uses a specific rhetorical feature more than the other. Observing each candidate’s tone also played a key role in this analysis. We also counted how many times each candidate mentioned the other, whether negative or positive, in order to determine if one side mentioned the other more or not and try to see if that potential imbalance could tell us something about their overall persuasion style and technique.

Here are some examples of statements we heard:

Biden:

• “And it was a constitutional principle upheld by justices appointed by Democrat and Republican Presidents alike.”
• “The only sure way to protect a woman’s right to choose is for Congress to restore the protections of Roe v. Wade as federal law.”
• “I grew up in a home where not a lot trickled down on my dad’s kitchen table.”

Trump:

• “They are coming after me because I’m fighting for you”
• “You know, it’s about helping women, not hurting women”
• “Follow your heart, your religion, your faith,”

Results and Analysis

To analyze the information, we decided to summarize our results and convert them into tabs, color-coded schemes, and some bar charts so that our data would be easily recognizable for a non-initiated reader who wants to learn about the topic quickly. Our results found that in total, both President Trump and former President Biden used the most Pathos to connect with the emotions of their audience. However, former President Biden used it a total of 46 times whereas President Trump used it 25 times overall. This shows that former President Biden focuses more on appealing to the heart strings of his viewers. They both used mentions of opposition the least, making us believe that it did not affect either of their persuasion levels, since. In terms of ethos, pathos, and logos, President Trump used logos the least in total while Former President Biden used ethos the least in total but used all rhetorical features more than President Trump. This shows that Former President Biden relied more on legal or statistics-based reasoning (logos), whereas President Trump relied relatively more on personal credibility claims (ethos).

Table 1: Frequency of Rhetorical Features in Each President’s Speeches

Let’s Visualize It

Figure 1: Average Count of Rhetorical Device Use by Presidents

Figure 2: Bar Chart Showing Average Count of Opposition Mentions Used by Presidents

Discussion and Conclusion

Two takeaways from the results that are key factors in persuasion. First, both Presidents work the emotional angle, but Former President Biden does it twice as often as President Trump. Former President Biden speaks about kitchen-table stories and urgent pleas to be an active participant in American society and politics. President Trump still plays on pathos, but leans harder onto credibility, posturing as a guardian of faith and tradition. Second, their styles of framing are drastically different. For example, about abortion Former President Biden frames it as rights, privacy, and the rule of law. Whereas President Trump frames it as a fight to save an innocent life granted by God. This mirrors the partisan split across the nation, liberty language on the left while the right uses language aimed at morals.

Presidential framing influences the subsequent policy discourse. When Former President Biden emphasizes congressional action, he situates the argument within formal institutional mechanisms and encourages legislative engagement. President Trump characterizes abortion as a sacred mission, which reframes the debate to a doctrinal domain that resists policy compromise. Lexical analysis supports this distinction: Former President Biden’s address contains higher frequencies of rights-oriented and procedural terms, whereas President Trump’s statements prioritize morally and religiously coded vocabulary.

Context further adjusts these strategies. President Trump’s April 2024 policy statement includes exceptions for rape, incest and maternal health, indicating a calibrated appeal to moderate audiences. Conversely, Former President Biden’s June 2022 national address following Dobbs employs predominantly legal language, reducing emotive slogans to reach a broader viewership. These variations demonstrate that rhetorical choices are audience-contingent and strategically adaptive.

This project examined six prominent speeches; therefore, generalizability is limited, and manual coding introduces potential subjectivity. Future research should analyze a larger dataset and have a predetermined coding method to validate these patterns. The observed divergences illustrate how presidential rhetoric contributes to sustained polarization in public attitudes toward any issue for which policy is being produced.

References

Biden White House, (2022) Remarks by President Biden on the Supreme Court Decision to Overturn Roe v. Wade. National Archives and Records Administration, bidenwhitehouse.archives.gov/briefing-room/speeches-remarks/2022/06/24/remarks-by-president-biden-on-the-supreme-court-decision-to-overturn-roe-v-wade/. 

Biden, J. (2024). 2024 State of The Union Address by Joe Biden. The Joe Biden Whitehouse on YouTube. https://www.youtube.com/watch?v=nFVUPAEF-sw

Charteris-Black, J. (2014). Analysing political speeches: Rhetoric, discourse and metaphor. Palgrave Macmillan.

Crowley, S., & Hawhee, D. (2004). Ancient rhetorics for contemporary students (3rd ed.). Pearson/Longman.

Ginsburg, F. (1989). Contested lives: The abortion debate in an American community. University of California Press.

Homolar, A., & Scholz, R. (2019). The power of President Trump-speak: Populist crisis narratives and ontological security. Cambridge Review of International Affairs, 32(3), 344–364. https://doi.org/10.1080/09557571.2019.1575796

Jamieson, K. H., & Campbell, K. K. (2001). The interplay of influence: News, advertising, politics, and the mass media (5th ed.). Wadsworth.

Lakoff, G. (2004). Don’t think of an elephant!: Know your values and frame the debate. Chelsea Green Publishing.

Medved, C. E., & Rawlins, W. K. (2000). At the intersection of personal and political: Complicating abortion narratives. Women’s Studies in Communication, 23(2), 167–189.

Messerly, M., & Allison, N. (2024). Trump Says Abortion is Up to the States, Declines to Endorse National Limit, Politico. https://www.politico.com/news/2024/04/08/trump-says-abortion-is-up-to-the-states-declines-to-endorse-national-limit-00151022

National Archives and Records Administration. (2020). Remarks by President Trump at the 47th  Annual March for Life, National Archives and Records Administration, https://trumpwhitehouse.archives.gov/briefings-statements/remarks-president-trump-47th-annual-march-life/

Rev (2022) President Biden Addresses Democratic National Convention, https://www.rev.com/transcripts/president-biden-addresses-democratic-national-convention

TIME (2024). Read the Full Transcript of Donald Trump’s Interview with TIME, https://time.com/6972022/donald-trump-transcript-2024-election/

Image 1. Spencer Barbosa’s most recent vlog thumbnail showcases her approachable and relatable lifestyle content, reflecting the personal branding style typical of her online presence.

Image 2: TikTok app listing on the Apple App store with logo.

Introduction and Background

TikTok is a social media platform launched in 2016, becoming a globally popular app where users create and watch 5-second to 10-minute-long videos. TikTok uses an algorithm to curate a personalized feed of videos for each user on a “For You Page,” which distributes content. The algorithm prioritizes content of popular creators, leading to a rise in influencers who contribute to trends on the app. The video content varies greatly with marketing, influencing, political activism, comedy, and much more, attracting a wide range of audiences. In the U.S., TikTok has over 30.8 million active users daily, with kids averaging 75 minutes on the app daily (D’Souza, 2025). Demographically, 36.7% of its users are aged 18 to 24, and 52% are female. Notably, 60% of the influencers on TikTok are Gen Z age (Magnet, 2025). With Gen Z having a dominant influencer presence on the platform and females being the primary gender, we wanted to specifically observe Gen Z female influencers, looking at their language use.

Our study focuses on discourse markers, also known as filler words such as ‘um,’ ‘uh,’ and ‘like.’ ‘Um’ was added to the Oxford English Dictionary in 1672, but people used these pauses in speech long before by people of all ages, races, and backgrounds. (Thompson, 2021) (Keyes, 2017). Filler words originated as a pause for speakers to collect their thoughts but have evolved into a common habit in everyday speech. Psychologist Robert Ecklund estimates that up to six percent of our words act as “verbal punctuation” rather than conveying meaning, suggesting seemingly meaningless filler words serve great purpose in communication. With the rise of casual online conversations, like on TikTok, discourse markers can be more prevalently observed in our mainstream media.

Curious about the overall evolving use of filler words and Generation Z influencer’s dominance on TikTok, we examined the frequency at which Gen Z influencers on TikTok use filler words, specifically within different contexts of videos. We targeted Gen Z female TikTok influencers, ages 13-28, with over two million followers. We produced the following hypotheses:

1. Influencers who produce “informal” content like “get ready with me” and “storytime” videos will have a higher frequency of filler words as opposed to advertisements, which are often formally scripted or influenced by a brand that sponsors the video.
2. There will be a negative correlation between the number of cuts and the number of filler words used in a video, suggesting that editing in videos reduces the number of filler words^[2].

Methods

The methodological approach to this project accounted for the TikTok algorithm and stylistic differences. We started a new Tiktok account for the five of us to view the specific content we were observing in order to avoid our own individually curated algorithms. Each member of our group selected an influencer with over two million followers to research. The five we focused on were: Britany Broski, Spencer Barbosa, Alix Earle, Katie Fang, and Lexi Hidalgo. We recorded data for one vlog video, one get ready with me video, one advertisement video, one beauty or makeup related video, and one storytime video for each influencer. We logged the influencer’s general description such as their name, username, age, and follower count. For each video we recorded the title, the view count, the length, the number of cuts and a count for each of the following discourse markers: like, um, uh, well, you know, I mean, basically, actually, literally, and “other”. We recorded the total number of discourse markers used per video, and the corresponding rate of discourse markers per minute. We analyzed 25 Tik Tok videos comprising 45 minutes of content. From here, we created graphs to further interpret and compare our collected data.

Results and Analysis

We observed the highest counts of filler words in the casual conversational videos. Get Ready With Me (grwm) videos had the highest average of filler words (15.8/video) and beauty/makeup videos had the second highest average (12.6/video). The category with the lowest average number of filler words across all influencers was the advertisement category (5.2/video).

Figure 1- Total Number of Filler Words Per Influencer in Each Category of Video. The graph shows the total number of filler words recorded for each individual influencerin each Tik Tok video category with beauty/makeup and get ready with me being the highest

We noticed that Lexi Hidalgo had significantly more filler words than the other influencers in the dataset. To account for this, we looked at the results without her data and saw that the get ready with me still had the highest average number of filler words (17.25/video), meaning this type of video likely lends itself to more filler words in general. Without Lexi Hidalgo’s data, the lowest average number of filler words was also the advertisement category. We still see a pattern with beauty/makeup videos having the highest frequency of filler words and advertisements having the lowest frequency, even after accounting for our outlier.

Figure 2- Total Number of Filler Words Per Influencer in Each Category of Video (Without Lexi Hidalgo). This figure shows the total number of filler words for each category without Lexi Hidalgo’s data. The grwm category still has the highest average number of filler words.

In addition, we observed the number of edits in each Tik Tok category. We hypothesized that more formal video contexts, such as advertisement videos, will have less filler words and more edits, because advertisements can be edited to communicate a branded message. We found that vlogs had the most edits, with a sum of 247 edits, and ads had the second highest with 130 edits. The category with the lowest number of edits was the storytime category, with 59 edits. This data supports our claim that videos can be intentionally edited to present an individual in a certain way. We see a trend with advertisement videos having the lowest average filler words recorded and the highest number of total edits in our data set.

Figure 3- Number of Cuts/Edits in Each Category of Video.  The figure shows the total number of cuts/edits in the TikTok videos per category. We see that Vlogs had the highest total number of edits and ADs had the second highest.

While we see an association that influencers can edit videos to present themselves in a certain way, it is important to note the stylistic choices online creators can make. Despite the trend that advertisements have fewer filler words, influencers may intentionally choose to leave fillers as part of a stylistic package or preference. Also, we see that certain influencers have preferences in the words they use. For example, Brittany Broski had the highest recorded number of ‘uh/uhh’ across all of her videos in comparison to the other influencers[3].

Figure 4- Total Number of Cuts/Edits in Each Video by Influencer.  The figure shows the total number of cuts/edits in the TikTok videos per influencer. We see that Lexi Hidalgo had the highest total number of edits

Figure 5- Sum of Each Type of Filler Words Per Video Context.  The figure shows the frequency of each type of filler word in each category of video. We see that ‘like’ and ‘so’ had the highest counts in our data.

Discussion and Conclusion

One of the main patterns we saw in our data was that filler words were used much more in informal video types, especially in beauty and makeup videos and vlogs. These are the kinds of videos where influencers are speaking casually, often unscripted, and trying to connect with their audience in a more personal way. The most commonly used filler words were “like” and “so,” which help speakers sound more relaxed and relatable. Although we can’t say that informal video formats directly cause more filler word usage, we did see a strong association between the two. It’s also clear that each influencer brings their own style to the table. For example, Lexi Hidalgo had both the highest number of filler words and the highest number of video cuts; 235 in total. This suggests her fast, edited format and casual speech are part of a “stylistic package” that makes her content feel personable and conversational. On the other hand, Britany Broski didn’t edit her videos as heavily but used the filler “uh” more than any of the other influencers we studied. This shows that filler word use also varies by individual speaking style, not just the video type. Some influencers rely more on certain speech habits, and editing choices also shape how many fillers we hear. Factors like editing, video length, or whether the video is a voiceover versus real-time can all affect filler word counts. Just because an ad has fewer fillers doesn’t mean it’s because it’s an ad, it may also be more scripted or professionally edited. For future research, we could analyze how video length or editing impacts filler word use, and look at whether these words influence audience engagement; do more fillers make someone seem more relatable, and does that boost likes or comments?

References

Abrahams, M. (2021, April 23). “um, like, so”: How filler words can be effective in communication. Stanford Graduate School of Business. https://www.gsb.stanford.edu/insights/um-so-how-filler-words-can-be-effective-commucation

Barbosa, Spencer. (2024). “DAY IN MY LIFE (getting my new house & pilates)” [YouTube thumbnail photo]. Youtube. https://youtu.be/8KelCzKvt3w?feature=shared.

Bestvater, S. (2024a, February 22). How U.S. adults use TikTok. Pew Research Center. https://www.pewresearch.org/internet/2024/02/22/how-u-s-adults-use-tiktok/

Bosker, H. R., Badaya, E., & Corley, M. (2021). Discourse Markers Activate Their, Like, Cohort Competitors. Discourse Processes, 58(9), 837–851. https://doi.org/10.1080/0163853X.2021.1924000.

D’Souza, D. (2022, June 22). What is TikTok? Investopedia. https://www.investopedia.com/what-is-tiktok-4588933

Duvall , E., Robbins, A., Graham, T., & Divett, S. (n.d.). Exploring Filler Words and Their Impact- BYU. https://schwa.byu.edu/. https://schwa.byu.edu/files/2014/12/F2014-Front.pdf.

Erard, M. (2015, September 1). Pause fillers. The American Scholar. https://theamericanscholar.org/pause-fillers/The American Scholar

Lanzarotti, V. (2017, October). What does saying “like” say about you. YouTube. https://youtu.be/IstIqrAyS2I

Magnet ABA. (2024, December 31). TikTok statistics, facts & user demographics. https://www.magnetaba.com/blog/tiktok-statistics-facts-user-demographics

Muliadi, B. (2024, August 12). What the rise of TikTok says about generation Z. Forbes. https://www.forbes.com/councils/forbestechcouncil/2020/07/07/what-the-rise-of-tiktok-says-about-generation-z/.

Munaro, A., Barcelos, R., Maffezzolli, E., Rodrigues, J., & Paraiso, E. (2024, March 28). Does your style engage? Linguistic styles of influencers and digital consumer engagement on YouTube. Does your style engage? Linguistic styles of influencers and digital consumer engagement on Youtube. https://www.sciencedirect.com/science/article/pii/S0747563224000852. 

musical.ly Inc. (n.d.). TikTok [Mobile app]. Amazon. Retrieved June 6, 2025, from https://www.amazon.com/musical-ly-Inc-TikTok/dp/B0117U0G3M

The Daily Free Press. (2011, April 13). Historically, the speech hesitation. https://dailyfreepress.com/04/13/11/177892/

Zhu, G., Caceres, J.P., & Salamon, J. (2022). Filler Word Detection and Classification: A Dataset and Benchmark. (arXiv:2203.15135v2 [cs.CL]).

Introduction and Background

Why Joke About Trauma?

What does it mean when a TikTok about grief racks up millions of likes? Or when a stitched joke about student debt leads to hundreds of people commenting, “Too real?” For Gen Z and Millennials, dark humor, jokes that deal with death, anxiety, trauma, or mental health, is not just comedy. It’s a language of solidarity. It’s a way to say, “I’ve been there too,” without getting too earnest or heavy-handed. It’s not about making fun of pain, it’s about making pain bearable by laughing through it. What’s striking, however, is that while both generations lean into this humor, they do so in different ways. Millennials often rely on sarcasm and absurdity to create emotional distance from the discomfort they feel. Their humor is layered, witty, and steeped in cultural references. Gen Z, on the other hand, tends to blend irony and sincerity, using dark humor as a way to be publicly vulnerable, often self-deprecating, chaotic, and confessional. These differences are deeply tied to each generation’s coming of age context and their digital fluency. This blog investigates how these generational styles of dark humor reflect broader identity performances on TikTok. We argue that this humor is a powerful communication tool for expressing emotion, belonging, and generational identity. Through an analysis of both videos and comment threads, we explore how people use humor not just to entertain, but to cope and connect.

Same Joke, Different Vibe

Millennials, born between 1980 and 1994, were the first generation to grow up alongside the internet. Their humor was shaped by platforms like Tumblr, Reddit, and meme forums, where sarcasm, nihilism, and absurdism flourished. Broderick (2018) describes Millennial humor as “cultural therapy,” often used to intellectualize or distance oneself from emotional discomfort. Shows like Rick and Morty capture this smart, ironic, and deeply existential tone. Gen Z, born between 1995 and 2012, came of age in a digital world shaped by Instagram, Vine, and especially TikTok. Their humor style is faster, more fragmented, and more openly vulnerable. Jacob (2023) describes Gen Z humor as a “performance of authenticity,” in which users lean into self-mockery and emotional chaos to show relatability. Instead of hiding pain under wit, Gen Z often makes the pain itself the joke. This shift can be understood through the sociolinguistic lens of Bucholtz and Hall (2005), who argue that identity is not fixed but constantly performed through language and interaction. On TikTok, dark humor becomes a discursive tool, a way to perform who you are and to whom you belong. A single comment like “same bestie 😭” signals not just shared feelings, but shared values and generational belonging.

Methods

What We Watched and How We Looked

To explore how dark humor operates differently across generations, we conducted a qualitative analysis of 10 TikTok videos that shared themes of trauma, grief, or existential dread. We chose videos that had hashtags like #genzhumor, #millennialhumor, #traumajokes, and #griefjourney to ensure generational and thematic diversity. These videos ranged from ironic skits to darkly humorous storytimes. From these 10 videos, we collected and analyzed a total of 236 top level comments. We coded the comments using several frameworks, humor type based on Samson & Gross (2014), emotional tone, generational markers, and linguistic style. We looked for common humor strategies, such as self-deprecation, irony, absurdism, or sarcasm, and also examined how emoji usage, slang, and hashtags helped signal generational identity and emotional intent. Rather than analyzing the content of the videos alone, we focused heavily on how users engaged with them in the comments. These interactions revealed how humor becomes collaborative, social, and identity-forming.

Results and Analysis

Patterns in Digital Dark Humor

Across our sample, four major humor categories emerged. First, self-deprecating humor was the most common, accounting for 38% of comments. These included phrases like “crave the grave,” “I’m not laughing, I’m relating,” and “literally me 😭.” (See Figure 2, Humor Type Distribution). Next, ironic and meta-humor made up 26% of the comments, often blending sarcasm and detachment, such as “ghosting over a joke is crazy 💔” or “me laughing at this while sobbing IRL.” Supportive or emotionally affirming comments made up 21%, often taking the form of gentle validation like “she would’ve loved this” or “sending hugs to anyone who gets it.” Finally, nihilistic or absurd humor made up the remaining 15%, marked by comments like “damn again?” or “just another Tuesday in hell.” (See Figure 2).Looking more closely at generational patterns, we found that Gen Z-coded comments (n = 120) were typically short, fast-paced, and emotionally raw. Commenters used emojis like 🥲💀😭 to intensify their tone, often stacking them for emphasis. Phrases like “same bestie” and “real for that” appeared frequently, offering micro-validations that signaled both empathy and in-group belonging. This aligns with the high rate of emoji use seen in Gen Z comments (See Figure 2, Emoji Use in Gen Z Comments). Gen Z’s humor, which was deeply communal, inviting others to share in the emotional experience. Millennial-coded comments (n = 116), in contrast, were more likely to be narrative-driven. Users told short anecdotes or crafted witty one-liners like, “I screamed into my Trader Joe’s tote bag after watching this.” These comments often featured cultural references, dry sarcasm, or a clear setup-punchline structure. Emoji use was minimal, and tone leaned toward ironic detachment. This generational contrast is further illustrated in Figure 1, which shows Millennials using sarcasm more frequently and Gen Z relying more heavily on self-deprecation. Sentiment analysis revealed that 39% of all comments were supportive, 33% were negative or critical, and 28% blended irony with sincerity. (See Figure 2, Sentiment Distribution)These findings echo Van der Wal et al. (2022), who argue that humor can serve social regulation and bonding functions. On TikTok, we see this in real-time: a grieving user posts a dark joke, and strangers respond with humor, empathy, or shared experience, creating a temporary but powerful moment of digital solidarity.

Discussion and Conclusion

If you really want to understand  how young people cope with stress, loss, and mental health struggles, look past the punchline and into the comments. That’s where the real conversations happen. When a Gen Z user jokes about grief and someone replies “too real 🫠,” it’s not just a laugh, it’s an act of recognition. It says, “I get it. I’ve been there too.” For Millennials, telling a deadpan story about a panic attack on public transit isn’t just entertainment, it’s emotional processing disguised as comedy. What’s especially fascinating is how these generational styles also create boundaries, both of inclusion and exclusion. When someone from outside the in-group comments, “This isn’t funny,” Gen Z users often respond with layered irony or dismissive humor, reinforcing the communal tone of “if you know, you know.” Millennials might disengage or reply with a witty retort, maintaining their signature emotional distance. These micro-interactions show how humor doesn’t just express identity, it defines who’s in and who’s out. Ultimately, these humor styles reveal how differently each generation experiences and narrates vulnerability. Gen Z foregrounds emotional chaos and authenticity. Millennials lean on cleverness and control. Both, however, are trying to do the same thing: make sense of a world that often feels senseless. And in doing so, they build digital spaces where humor becomes survival, and connection.

If you really want to understand  how young people cope with stress, loss, and mental health struggles, look past the punchline and into the comments. That’s where the real conversations happen. When a Gen Z user jokes about grief and someone replies “too real 🫠,” it’s not just a laugh, it’s an act of recognition. It says, “I get it. I’ve been there too.” For Millennials, telling a deadpan story about a panic attack on public transit isn’t just entertainment, it’s emotional processing disguised as comedy. What’s especially fascinating is how these generational styles also create boundaries, both of inclusion and exclusion. When someone from outside the in-group comments, “This isn’t funny,” Gen Z users often respond with layered irony or dismissive humor, reinforcing the communal tone of “if you know, you know.” Millennials might disengage or reply with a witty retort, maintaining their signature emotional distance. These micro-interactions show how humor doesn’t just express identity, it defines who’s in and who’s out. Ultimately, these humor styles reveal how differently each generation experiences and narrates vulnerability. Gen Z foregrounds emotional chaos and authenticity. Millennials lean on cleverness and control. Both, however, are trying to do the same thing: make sense of a world that often feels senseless. And in doing so, they build digital spaces where humor becomes survival, and connection.

Figure 1: Sentiment and Humor Type Breakdown with Gen Z Emoji Use This set of pie charts presents three types of analysis from TikTok dark humor comments. Left: Humor Type Distribution—shows the proportion of humor types (Self-deprecating, Ironic/Meta, Supportive, Nihilistic/Absurdist). Middle: Sentiment Distribution—illustrates the emotional tone across comments (Positive/Supportive, Mixed/Ironic, Negative/Critical).Right: Emoji Use in Gen Z Comments—displays how frequently and in what style emojis appear, with 40% of comments using exaggerated or emotional emojis (e.g., 🤣😭💀), reinforcing Gen Z’s preference for hyperbolic and affective expression.

Figure 2: Comment Style Breakdown by Generation
This bar graph compares the comment styles used by Gen Z and Millennials in TikTok dark humor content. The x-axis shows the three main comment styles (Self-deprecating, Sarcasm, and Empathetic), while the y-axis represents the percentage of total comments in each style. Gen Z (blue) shows a higher rate of self-deprecating and empathetic comments, while Millennials (green) use more sarcasm overall

References

Broderick, A. E. (2018). ” Traumatized for Breakfast:” Why Millennials Respond to the Trauma, Comedy, and Dark Optimism of Rick and Morty. State University of New York at Stony Brook. https://www.proquest.com/docview/2138913981?pq-origsite=gscholar&fromopenview=true&sourcetype=Dissertations%20&%20Theses

Bucholtz, M., & Hall, K. (2005). Identity and interaction: A sociocultural linguistic approach. Discourse studies, 7(4-5), 585-614. https://doi.org/10.1177/1461445605054407

Jacob, R. (2023) Unveiling the Dark Humour and Self-Image of Generation Z in a Polymedia Context. The Criterion: An International Journal in English, 14, 215-26. [Journal-article].  https://www.the-criterion.com/V14/n4/LL07.pdf

Samson, A. C., & Gross, J. J. (2014). The dark and light sides of humor. Positive emotion: Integrating the light sides and dark sides, 169. https://books.google.com/bookshl=en&lr=&id=1vNQEAAAQBAJ&oi=fnd&pg=PA169&dq=how+dark+humor+operates+in+different+contexts+and+what+social+function+it+serves&ots=oJgSRN9EGw&sig=m4AO3FgFdMIjLutcRkYGcT8fh8#v=onepage&q=how%20dark%20humor%20operates%20in%20different%20contexts%20and%20what%20social%20function%20it%20serves&f=false

Van der Wal, A., Pouwels, J. L., Piotrowski, J. T., & Valkenburg, P. M. (2022). Just a Joke? Adolescents’ Preferences for Humor in Media Entertainment and Real-Life Aggression. Media psychology, 25(6), 797–813. https://doi.org/10.1080/15213269.2022.2080710

Introduction and Background

Linguist Asif Agha (2015) says that slang exists in “microspaces”, which are defined as tight-knit communities where language choices reflect shared practices. Fraternities are a perfect example of this. Frat slang isn’t just just casual, they’re performances of masculinity and markers of group status. According to Asif Agha (2015), “Many kinds of slang coexist with each other within a language community and define many micro spaces of interaction linked to specific social practices and groups.” Fraternity members’ interactions are the types of microspaces that slang is prevalent within, which is why we chose them for our study, mainly focusing on the significance and role of slang in each interaction.

According to Yanchun Zhou and Yanhong Fan (2013), “If somebody uses the words and expressions within a certain social group or professional group, he will blend with the group members from mentality. That is to say, if a student says a sentence containing the special college slang, he must want to get the result of showing and strengthening the emotion that he is belonging to the inside of the teenager group.” This aspect of assimilation into a group through slang will be a core focus of our study. The idea of an “in-group” and “out-group” regarding understanding and employing slang is elemental to our theory on how slang functions to build social identity and reinforce group norms among fraternity men.

The idea of being in and out of “the know” is highlighted by the following excerpt: “In general terms, identity is realized when those people who are competent with a slang word come to infer that a speaker who uses is a member of the group for whom use is conventional based on their knowledge” (Alice Damirjian, 2024). A study done by Scott Kiesling shows that specific linguistic choices within groups like fraternities, such as slang, serve as markers of group identity and solidarity among fraternity members. He analyzes the “-in” variant among fraternity members and (e.g, “walkin” instead of “walking”).  Kiesling notes, “The -in form, through its widespread indexicality of casualness… is one of the resources Speed, Waterson, and Mick use to take these stances, along with other linguistic features.”(Scott Kiesling, 2005). This demonstrates how fraternities use specific linguistic forms to reinforce group cohesion through shared language.

In terms of linguistic properties typically found in the communicative patterns of slang among fraternity men, brothers often adopt distinct linguistic features that reinforce their group identity by differentiating insiders from outsiders. From an academic article by Pongsapan (2022), it is stated, “In the document analysis and questionnaire result, the researcher found that the students used language variations, especially slang in their interaction with various types, such as fresh and creative, compounding, imitative, acronym, and clipping.” Fraternity slang is reflective of this nature as most slang used by members is often a more casual and playfully coded language than standard, and often specific to Greek life. Phrases and expressions often reflect shared experiences, humor, or references that may be unintelligible to those outside the fraternity. The use of slang fosters familiarity and signals belonging.

Methods

We used a mixed-methods approach to capture how slang operates in these houses:
– Interviews: We sat down with UCLA frat members and asked them how their speech had changed since joining.
– Surveys: We gave participants Likert-scale questions and open-ended prompts.
– Naturalistic Observation: We attended casual hangouts to document body language and informal conversations.

Firstly we conducted a survey via Google Forms and collected 33 responses from Fraternity members in three different houses at UCLA. The survey included both closed and open-ended questions asking about their language use, perceptions of communication differences, and feelings of closeness with their brothers. Many of our questions were open-ended, so we conducted 3-5 minute one-on-one interviews with fraternity members for further data collection. We went deeper with interviews asking members how their language had changed overtime, how slang or jokes functioned in their house, and how these factors impacted their sense of identity and connection. Using both quantitative and qualitative methods helped us capture a fuller picture of fraternity communication. The survey gave us broader patterns, while interviews revealed personal experiences, shared rituals, and the emotional meanings behind the slang.  Altogether, these tools are what allowed us to analyze not just what was said, but how language operates in fraternity settings to reinforce bonds, in-group norms, and a unique culture.

Results and Analysis

Figure 1: Answers to the question “Do you think the type of speech you use in the fraternity house makes you feel closer to your brothers? If so, why and how?”

As seen in Figure 1, 100% of respondents agreed that the type of speech they used in the fraternity house made them feel closer to their brothers. Our survey also revealed that a majority of respondents felt their communication style had significantly changed since joining the fraternity (Figure 2). 90% agreed that they use house-specific slang specifically when within the frat (Figure 3). Interestingly, many also acknowledged that this language created an unintentional barrier to outsiders, and 80% of respondents believed that the average person would not understand the language inside the frat (Figure 4). This data highlights how slang functions not just as decoration, but as a central tool in building and navigating social dynamics within Greek life.

Figure 2: Has your language changed and adapted since joining a fraternity? If so, how?

Figure 3: In the house, do you think you speak differently?

Figure 4: Do you think the average person would understand slang said in the fraternity house among brothers?

Discussion and Conclusion

What the Brothers Said

One brother explained, “Yes, it’s difficult to describe, but there’s a lot of inside jokes, I would say, very, the least formal way that English can possibly be spoken” This sentiment came up often, that slang and inside jokes helped them feel more exclusively connected. Another said, “Um, Absolutely, but it’s difficult to describe. I would say it just comes down like limited vocabulary. For example, if we want to play a drinking game, we’ll just say just one word. Or going out, just go out. Sometimes, like, you get so close to each other, you could literally just point and it works. Kind of like, read each other’s minds.”

Another interviewee emphasized that their slang is constantly evolving: “
Like one person will say something funny and then it’s just kind of part of my lingo for at least like a month or two, and then before I know it, it’ll be something else.” This dynamic adaptation of language shows how slang reflects not only identity but also the constantly shifting social fabric of fraternity life, and the constant lexical changes we go through as a fast-paced generation.

The fraternity context also makes room for a specific kind of humor. “Like, I wouldn’t go insulting my classmates that I’m working on a group project [with], but, like, someone that I live with and, like, I’ve been through the thick of it with them, like, I feel all right, insulting them every once in a while. Totally.” This idea of bonding through teasing or “chirping” was repeated across multiple interviews. It illustrates that fraternity slang isn’t just about phrases; it’s about the tone, style, and culture of communication that define these relationships.

Future Directions

Our project opens up opportunities for questions and future research. Could intentionally modifying group slang affect bonding outcomes in new member orientations? Might there be a way to track how slang evolves in digital spaces like on GroupMe or Instagram DMs? As Greek life continues to adapt to changing campus climates and public perception, understanding the linguistic pulse of these communities may offer insights into broader shifts in masculinity, identity, and group belonging in Gen Z culture.

Our results were technically inconclusive, but heavily suggest a positive correlation between slang use and closer bonding. Slang in fraternities is a tool for navigating identity, forming friendships, and establishing social hierarchies. Our study shows that when frat brothers speak their own lingo, they’re doing more than talking. They’re building brotherhood, one “dub,” “ferda,” and “foenem” at a time.

References

Agha, A. (2015). Tropes of slang. Signs and Society, 3(2), 306–330. https://doi.org/10.1086/683179

Damirjian, A. (2025). The social significance of slang. Mind & Language, 40(2), 138–156. https://doi.org/10.1111/mila.12530

Kiesling, S. F. (2005). Fraternity men: Variation and discourses of masculinity. In D. Santa Ana (Ed.), Tongue-tied: The lives of multilingual children in public education (pp. 105–121). Routledge. https://www.researchgate.net/publication/316501721_Fraternity_Men_Variation_and_Discourses_of_Masculinity

Pongsapan, N. P. (2022). An analysis of slang language used in English students’ interaction. Jurnal Onoma: Pendidikan, Bahasa dan Sastra, 8(2), 917–924. https://e-journal.my.id/onoma

Zhou, Y., & Fan, Y. (2013). A sociolinguistic study of American slang. Theory and Practice in Language Studies, 3(12), 2209–2213.

Introduction and Background

Gamers have become a major source of online entertainment. They are especially popular among younger generations, who see them as more relatable and authentic than traditional celebrities. Profanity is everywhere in streaming but it’s not treated the same across genders. Male streamers are often seen as funny and entertaining when they swear, compared to females who are seen as rude or inappropriate.

Existing studies comparing the swearing differences between male and females have found that men typically use more intense swear words, swear more in public, and have a larger profanity lexicon than women (Bailey and Timm, 1976). Regarding public perception, men find women who swear less attractive while women find men who swear more attractive (O’neil, 2001). These findings point to gender norms in that cursing is associated with dominance and masculinity. Women who curse may be perceived as deviating from these norms and face more intolerance towards their speech patterns (Lakoff, 1972). Interestingly enough, recent studies have shown that women do not curse drastically less than men, as the rise of social media has increased the swearing rates of younger generations of women (Tikile and Ngulube, 2025). While most studies cover general gendered swearing patterns, we are interested in examining swearing patterns in streaming contexts and if societal gender norms are still enforced on digital platforms. In our study, we analyze the top gaming streamers by analyzing their swearing patterns, identifying trends regarding their speech.

Methods

To explore swearing patterns used in live-streaming environments, our group focused on a select group of popular Twitch streamers: Kai Cenat, Ninja, Caseoh, Jynxzi, Pokimane, Valkyrie, Loserfruit, and Kyedae. They all had at least a million followers and ten million hours of watched content. The streamers also played the shooter games such as Fortnite, Valorant, and PUBG, where entertaining viewers and performing well simultaneously is a key part of their content. We observed how the streamers communicated in various situations, such as under pressure, intense gameplay, moments of frustration, joking with friends and viewers, or reacting to unexpected events of the game.

In a chart, we documented every instance of a curse word within a thirty minute streaming segment and noted its different features. For example, we noted specific profanity used and its intensity. We also observed who or what the curse word was directed to (e.g., the game, the gamer, another player, general conversation, etc.). We noted the volume and tone in which the profanity was delivered (e.g., excited, frustrated, surprised, calm). We also categorized each curse word by type (e.g., sexual, scatological, religious, slur, expletive) and documented the context as well as transcripted sentence in which the word was used. Finally, we noted the function of swearing, such as expressing frustration after dying in the game, emphasizing excitement or disbelief, or entertaining the audience. All data was recorded manually in a spreadsheet where each member documented the curse word and selected tags that applied to the word. Each streamer was viewed twice by members which allowed us to cross-reference observations and resolve disputes in different interpretations of the data, ensuring accuracy and consistency. In the end, we tallied the total number of curse words used in the segment as well as the average time between each curse word for each streamer.

Figure 1: Data collection chart for cursing behavior of streamer “Pokimane” with tags selected for each feature of curse word.

Results and Analysis

Figure 2: Bar chart showing distribution of cursing functions between male and female streamers in proportion of total curse words.

Our analysis reveals that there are distinct gender-based differences in both frequency and function of cursing across male and female streamers. For male streamers, their overall
frequency was much higher than female streamers with 85 total instances compared to 36 instances for women. For the function of cursing, we can see in Figure 2 how male streamers mostly employed it as a way to express frustration or insult 47% of the time and emphasize 32% of the time. In contrast, female streamers used cursing for those functions 28% and 17% of the time, respectively. Male streamers used curse words to instruct 14% of the time while women never used it for that purpose. Conversely, women used cursing to apologize 3% of the time while men never used it for that purpose. This may suggest that women tended to be less confrontational and more intentional with the way they relationally used profane language.

Figure 3: Bar chart showing distribution of curse word categories between male and female streamers in proportion of total curse words.

As far as the type of curse words in Figure 3, female streamers overwhelmingly favored milder, religious profanity, accounting for over half of their total curse words at 55.32%. Male streamers, on the other hand, displayed a broader distribution of slurs (10.38%), scatological terms

(24.53%), and sexual language (27.36%), suggesting their appeal for more intense profanity. This may highlight how male streamers may feel more dominant or accepted by other men, especially in their community, if they adopt the “Boys will be boys!” mentality and incorporate more sexual and derogatory forms of profanity. Women, conversely, must make up for their use of profanity by catering towards less offensive forms and abide by social conventions.

Figure 4: Bar chart showing distribution of cursing intensity between male and female streamers in proportion of total curse words.

Figure 5: Bar chart showing distribution of cursing in negative tones between male and female streamers in proportion of total curse words.

Figure 6: Bar chart showing distribution of cursing in positive tones between male and female streamers in proportion of total curse words.

Lastly, in examining emotional tone, we found that men were more likely to curse in a negative tone, such as sounding annoyed (15%), angry (12%), or condescending (8%). Women were more likely to curse in positive tones, such as friendly/playful (20%), calm (17%), and excited (17%). For the angry and condescending tone in Figure 5, we can see a drastic difference between how much men employed these tones compared to women, who used them just 2% of the time. In Figure 6, this pattern is reversed for positive tones in that women embodied this attitude at a significantly higher rate than men, who only used it 3-5% of the time. These findings suggest that profanity use is not just a matter of vocabulary, but reflects deeper gender communication norms. Male streamers tend to use curse words to assert dominance, frustration, or authority, often paired more intense and negative emotional tones. Women, however, employ profanity more creatively or socially to integrate it into positive and affiliative expressions. This gendered contrast highlights how language, even profanity, is shaped by broader patterns of social behavior, emotional expression, and interactional goals.

Discussion and Conclusion

All in all, our hypothesis was supported in that men used direct profanity at a higher frequency and intensity to express anger, while women used it at a milder, lower frequency to maintain relationships. We can see how both male and female cursing patterns reflect broader gender norms as communication is shaped by culture and societal pressures, even in digital spaces. Men employ profanity to express masculinity, dominance, and intimidation, while women tend to conform to their expectation of maintaining proper, socially acceptable behavior by regulating their profanity to build rapport. This reflects their subordinate, emotionally sensitive role compared to males as they may feel moderating language helps them remain approachable and likable. Previous studies support these findings such as Coates (2015) and Lakoff (1975) in highlighting women’s adherence to cautious, civil behavior in public settings.

Men may not have this capability to regulate emotions as well as they are more aggressive and their brains simply do not have the potential to cope with intense emotions as well as female brains (Güvendir, 2015). This biological basis has shaped gender roles in determining what communication patterns are appropriate for each gender and therefore dictates how people perceive those who conform and deviate from such conventions. Audiences may not receive female cursing as well as male cursing as it’s unconventional for females to use harsh language, prompting a more restrained, lighthearted usage in fear of judgment. Males, however, may be perceived as powerful and admirable in establishing dominance, allowing for more frequent cursing. As gaming is a rapidly evolving environment, it’s important for streamers to recognize these norms and understand the differences in audience perception. Streamers must navigate challenges in maintaining authentic personas and simultaneously conform to gender expectations to resonate and attract viewers that will appropriately receive their content and language style.

References

Bailey, L. A., & Timm, L. A. (1976). More on Women’s — and Men’s — Expletives. Anthropological Linguistics, 18(9), 438–449. http://www.jstor.org/stable/30027592.

Coates, J. (2015). Women, Men and Language: A Sociolinguistic Account of Gender Differences in Language (3rd ed.). Routledge. https://doi.org/10.4324/9781315645612.

Güvendir, Emre. (2015). Why are males inclined to use strong swear words more than females? An evolutionary explanation based on male intergroup aggressiveness. Language Sciences, 50, 133-139. https://doi.org/10.1016/j.langsci.2015.02.003.

Lakoff, R. T. (1975). Language and woman’s place. Harper & Row.

O’neil, Robert Paul. (2001). Sexual Profanity and Interpersonal Judgement. LSU Historical Dissertations and Theses. https://repository.lsu.edu/gradschool_disstheses/427.

Tikile, E. A., Ngulube, I. E. (2025), The Usage of Swear Words Among Generations X, Y and Z in Rivers State University. International Journal of Literature, Language and Linguistics 8(1), 37-49. 10.52589/IJLLL-7YPDRYKS.

Introduction and Background

At UCLA, Panhellenic sororities serve as hubs of social life, where language plays a significant role in shaping connections, identity, and a sense of belonging. The subtle yet powerful tool of compliments is crucial in shaping these interactions. But the common question is what exactly happens in these exchanges, and how distinct are they compared to women outside of the Greek Life bubble? This intriguing question led to a deeper exploration into the complexities of everyday communication among younger women. Frequently, sororities make headlines for their perceived social image, sisterhood, and extravagant events, but the nuanced day-to-day interactions that truly define these relationships often go unnoticed. Compliments are perceived as simple affirmations of admiration and are categorized as casual speech, yet they hold intricate layers of significance. They reinforce group cohesion, used as a tactic to navigate social hierarchies, and influence personal identities. After recognizing this unexplored realm, our study aims to compare these interactions between sorority-affiliated women and their peers outside the Greek community. This research is essential to identify the mechanics of social belonging while uncovering how power dynamics and identity negotiations occur in everyday conversations. To accomplish this, we have designed surveys and quantitative research methods focused on dyadic, or one-on-one, interactions among women aged 18-23. By analyzing their experiences, we strive to uncover whether sorority life uniquely shapes communication or if broader cultural factors influence all women’s interactions similarly. Ultimately, this research aims to gain a deeper understanding of how undergraduate women navigate their daily lives within various social structures.

Methods

To examine the use of gendered communication through the use of compliments among college women, our research employed a comparative study of undergraduate women at UCLA, focusing on two groups: women that belonged to sororities and those unaffiliated with greek life. Our goal was to explore how compliments, both verbal and nonverbal, function in how young women build relationships, reinforce identity, and negotiate social dynamics between these groups.

Our research utilized two separate surveys that asked the same questions, though distributed separately among sorority members and non-sorority members. The survey included both quantitative questions, such as frequency and context of compliment use and qualitative questions, for example, descriptions of specific instances in which compliments were given, exchanged, or received. The questions we included in the survey was designed to capture patterns among and between the groups, in addition to their individual perspectives of their lived experiences in this context. Participants were asked about their communication habits in everyday social settings, such as hanging out casually with other girls, social events, and through digital platforms, and how they perceive compliments to be used and received in those interactions.

Our analysis placed an emphasis on the observable elements of communication such as linguistic style, nonverbal behavior, and contextual cues. We applied thematic analysis to qualitative responses to identity recurring patterns in how compliments were used between the groups. The use of discourse analysis in our research enabled us to analyze the functioning of language and communication in reinforcing group norms or upholding social hierarchies. Lastly, comparative analysis was employed in our study to highlight the differences and similarities in the strategies of communication and the function of compliments used by both sorority and non-sorority women. By examining self-reported survey responses, we hoped to gain a thorough knowledge of how young women at UCLA manage social belonging and identity through gendered communication

Results and Analysis

Sorority women report both giving and receiving compliments more frequently than their non-affiliated peers, with many engaging in this exchange multiple times a day. This elevated volume supports the idea that sorority life involves more frequent social interactions where compliments function as everyday tools for communication and cohesion. The constant flow of affirmations may reflect not only a culture of positivity but also an underlying strategy—compliments can be used to reinforce group identity, affirm social standing, or ease interpersonal dynamics. These patterns align with the project’s hypothesis that Greek life intensifies both the frequency and function of compliments, making them a central part of navigating relationships and maintaining one’s place within a tightly knit social hierarchy.

Sorority women more frequently perceive compliments as a social expectation, which suggests that these exchanges are not just spontaneous acts of kindness but part of a broader communication norm within their community. This expectation may foster a culture where compliments serve as a kind of social currency—used to maintain group harmony, affirm alliances, or smooth over tensions. Additionally, the finding that compliments more often lead to deeper conversations or bonding among sorority members reveals their higher social utility. Compliments in these settings appear to function as gateways to relationship-building and group integration, helping women establish rapport and navigate the layered dynamics of Greek life. In contrast, for non-sorority women, compliments may remain more surface-level, serving as gestures of kindness without necessarily opening the door to a closer connection.

Both sorority and non-sorority women overwhelmingly report feeling appreciated after receiving a compliment, suggesting that affirmations are generally well-received across groups. However, sorority women are also more likely to feel empowered (50% vs. 30.8%), indicating that compliments may play a stronger role in reinforcing identity and confidence within Greek life. They also report feeling obligated to return the compliment more frequently (45% vs. 38.5%), hinting at social expectations or pressure to reciprocate. Notably, only sorority women reported feeling suspicious, which may reflect an awareness of strategic communication or unspoken power dynamics in their social circles.

Discussion and Conclusion

By examining the role of compliments among sorority and non-sorority girls at UCLA, this study contributes to a broader understanding of how compliments serve as mechanisms of social bonding and power negotiations in female social networks. It offers valuable insight into how gendered communication influences the way young women connect with one another. Popular media and television often depict female interaction as superficial or ingenuine, reinforcing dismissive stereotypes for comedic or dramatic effects. In contrast, our findings illustrate that language not only reflects social norms but also shapes relational dynamics within our social network.

This observation is supported by Ayers (2012), who discusses how compliments and seemingly kind remarks can also function as tools of relational aggression or competition, particularly among young women navigating subtle forms of power. Compliments, in this sense, are not always innocent; they can act as veiled strategies to assert dominance or reinforce group boundaries. Bryan’s (2013) research on sorority women reinforces this dynamic, illustrating how identity control theory plays out in Greek life through continual feedback—where compliments help regulate behavior and self-presentation within tight-knit groups. These findings align with our participants’ reports of both empowerment and pressure, especially among sorority members, who often experience compliments as part of a broader system of expectation, social maintenance, and inclusion.

Although the sample of young women is limited to UCLA specifically, the patterns observed in our findings support the phenomenon of power dynamics shaping female relationships across various social contexts and in fact challenge traditional assumptions about stereotypes embedded in sorority and non-sorority discourse. By recognizing the power compliments hold in day-to-day communication, the study helps highlight the significance female dynamics play in shaping social hierarchies and networks-demonstrating how language can elevate, encourage, include, and ultimately empower the bonds women create over time. Moreover, understanding these nuanced communication patterns encourages more mindful interactions that can foster genuine connection and challenge superficial stereotypes within female social networks.

(Figure 2: Chanel Oberlin, played by Emma Roberts, in TV series Scream Queens)

References

Ayers, Danielle, “Queen Bees: An Examination of the Mean Girl Phenomenon” (2012). Honors Theses. 767.

Bryan, H. (2013). The Sorority Priority: The Role of Interactional Feedback Mechanisms in Shaping Body Image in Sororities.

Frankenberger, W. R. (2024). Relational Conflicts Experienced Through Digital Platforms Among Generation Z Sorority Women (Order No. 31146923). Available from ProQuest Dissertations & Theses A&I; ProQuest Dissertations & Theses Global. (3068910761). www.proquest.com/dissertations-theses/relational-conflicts-experienced-through-digital/docview/3068910761/se-2

Rees-Miller, Janie. “Compliments Revisited: Contemporary Compliments and Gender.” Journal of Pragmatics, vol. 43, no. 11, Sept. 2011, pp. 2673–88. DOI.org (Crossref), doi.org/10.1016/j.pragma.2011.04.014.

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