Hedging and Gender in the STEM Community

Eric Chen, Abbey Mae Gozon, Khoi Nguyen, Paul Vu, Julia Wang

Hedging is an aspect of language that is easy for non-linguists to overlook. These terms are used to apply uncertainty to a statement, to make it seem less assertive. The question we seek to answer is, do women make more use of hedging than men do? Specifically, we seek this in the context of an environment where more is expected of women than of men. In this experiment, we take a look at the presence of hedging in the speech of female STEM students. These participants participate in interviews about the subjects they study, and then afterwards take a short survey in order to determine what it is that the participants believe is the root cause of their own hesitations. The recordings of the interview portions are scanned for hedges that are measured as uncertainty in the participant’s explanations. A numerous presence of which would imply that the speaker is not completely sure that they are correct and are choosing to leave room for themselves to err. This study intends to find out whether or not women hedging more than men contains more substance than is implied by the stereotype.

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Fun, Cool, Hip Title Here: AAVE Usage in Twitter Memes

Nick Ushiyama, Stella Oganesyan, Ava Boehm, Rachel Lee, Alesha Vaughn

Love them or hate them, almost everyone active on social media has come into contact with memes at some point. Chances are, one or more of those memes used a variety of English called AAVE, or African American Vernacular English. This variety originated from working-class African Americans and displays words (lexicon), word order (syntax), word pronunciation/spelling (phonology), and word combination (morphology) different from the Standard American English (SAE) taught in schools (Rickford et al., 2015). In our study, we tried to better understand how and why meme-makers switch between AAVE and SAE in their posts. We expected meme-posting Twitter users to use switching as a way to signal to readers that their posts should be read within the unique guidelines of meme-culture humor. For our research, we collected hundreds of memes and distributed a survey to see how people interpreted the switches. The results confirmed our expectations.

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Speech Patterns as Identity Constructors Across Social Media Platforms

Alissa McNerney, Akina Nishi, Ryley Park, Nicolas Simone, Fontanna Yee

As slang and social media usage has risen in popularity in recent years, we wanted to explore how different patterns of slang would change a speaker’s identity on different social media platforms. Although we initially thought that examining slang alone would give us a good picture of how social media identities were created, we soon realized that slang usage was part of the story, but not entirely dependent on the social media platform. This discovery allowed us to pivot towards analyzing not just slang but also how prosody, speech-related information such as intonation and gestures, also contributed to identity construction. By conducting a case study of TikTok influencer @sirthestar across three social media platforms, TikTok, YouTube, and Twitter, and analyzing both written and spoken content, we concluded that greater usage of slang and prosody contributed to creating a more comedic identity on TikTok and YouTube and lesser usage contributed to a more social activist identity on Twitter.

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Does She Listen to ‘Girl in Red’? Linguistic Markers in WLW Flirting

Tiffany Dang, Brianna Lombardo, Carlos Salvador Vasquez, Denisa Tudorache, Yuyin Yang

The present article focused on linguistic markers that are adopted by the Women Loving Women (WLW) population when identifying potential members of the WLW community. More specifically, this study focused on the strategies used by members of the WLW community for identifying fellow WLW with the intentions of pursuing a romantic or sexual relationship. Through analyzing popular YouTube videos featuring strategies on flirting with WLW, our first study captured the common beliefs regarding the need to take an extra step, and the possible methods on identifying WLW before taking any romantic or sexual advances. Followed-up by semi-structured interviews in study two with UCLA students who self-identify as WLW, we were able to examine the accuracy of the tips offered by the YouTube videos. This allowed for further investigation on the existence of specific linguistic markers adopted by WLW when flirting. We found that both popular YouTube videos and participants both discussed the need for WLW to take an extra step before they can comfortably pursue another woman and tend to make a conscious effort to not be too direct.

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Do you LOL out loud? Screen time influence on internet slang irl

Julia Baylon, Rachel Rim, Carolina Guerrero, Hebbah Elokour, and Caelynn Hwang

If you’re a college student reading this, you are a key individual in the composition of the Gen Z identity. Gen Z, today’s 18-23-year-olds, is defined by its fervent slang usage as well as its notorious association with and attachment to technology. Commonly used components of technology in Gen Z include social media platforms, such as Instagram, Snapchat, Twitter, TikTok, and Discord. This study investigates the relationship between the two variables of time exposed to social media and slang-impacted conversational speech style. As slang rapidly evolves to shape communication and accommodate the construction and expression of individual identity, we begin to ponder, where does this language style come from, and to what extent does it influence our diction? Through conversational analysis and survey/questionnaire conduction, we hope to understand the explicit impact of social media on conversational slang and whether or not the results support our hypothesis, which argues that greater usage of slang on the internet and/or overall internet presence will result in a higher frequency of slang used in real life.

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“It’s not always negative, but sometimes it is”: Exclusivity in sororities vs. cottagecore communities

Sandra Fulop, David Huang, Yinling Li, Joyana Rosenthal

An important part of college is finding a space to belong. For marginalized students such as LGBTQ women, this can also be the most difficult part. Although there are often groups such as Gay-Straight Alliances or LGBTQ resource centers, these revolve entirely around the LGBTQ identity. But general women’s spaces, such as sororities, are notorious for being less accepting and more exclusive of marginalized identities. This presents an issue for LGBTQ women, who may struggle to create an identity outside of being LGBTQ while avoiding prejudice from groups meant to include all women. Our study focused on the vocabulary choices of LGBTQ women when discussing their own women-centric spaces, specifically Panhellenic sororities or cottagecore communities. We discerned how comfortable and included they felt in their respective spaces and how they felt others perceived them inside and outside that group. We created vocabulary categories to differentiate between inclusive/in-group and exclusive/out-group language, averaged the frequency of use across each group, and compared them. We found that LGBTQ cottagecore women expressed much more comfort in the space they belonged to, while LGBTQ sorority women swept their marginalized identities under the rug and focused on out-group perceptions and stereotypes.

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“Sorry, I Didn’t Quite Get That: The Misidentification of AAVE by Voice Recognition Software”

Shannon McCarty, Lam Pham, Alora Thresher, Alexandria Wasgatt, Emma Whamond

This study investigates the transcription accuracy by AI speech recognition systems using natural language processing when interpreting standard American English dialects (SAE) versus African American Vernacular English (AAVE). We inspect the percentage of misidentified words, and the degree to which the speech is misidentified, by AI speech recognition systems through analyzing authentic speech found in YouTube videos. The accuracy of voice recognition with respect to AAVE will be determined by selecting for distinct AAVE features, such as G-dropping, the [θ] sound, reduction of consonant clusters, and non-standard usages of be. The methodology includes feeding YouTube clips of both SAE and AAVE through an AI speech recognition software, as well as examining YouTube’s auto-generated transcripts, which are created by automatic speech recognition based on the audio of the YouTube video. The purpose of this study is to bring attention to the needs of diversity in technology with regard to language variation, so that AI speech systems such as Amazon’s Alexa or Apple’s Siri are more accessible to all members of society, as well as to help destigmatize a variety of American English that has carried social, cultural, and historical stigma for centuries.

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They and Them: Gender Inclusivity Across Languages

Alexander Gonzalez, Maeneka Grewal, Nico Hy, Zoe Perrin, Vivian San Gabriel

The relevance of gender-neutral language has surged due to growing acceptance towards nonbinary and gender non-conforming people as well as the dissolution of the gender binary. Through comparative analysis of native English and Spanish speakers, we investigated the impact of grammatical gender on the methods speakers employ to express gender neutrality. Since Spanish sentences require full gender and number agreement, expressing gender neutrality in Spanish presents more challenges than in English. We asked participants to describe images of individuals and observed that the English speakers used gender-neutral language at higher rates than the Spanish speakers did. Their methods differed as well. Spanish speakers were more likely to mix feminine or masculine forms, alongside neutral descriptions, which we interpreted as attempts to use gender-neutral language. We can infer that even when Spanish speakers are looking to express something gender-neutrally, they may be limited by the lack of gender-neutral lexical items that can be used throughout an entire utterance. Our experiment was limited to written responses and as a result may not be representative of these speakers’ language use overall. More experiments dealing with oral speech and analyses of other gendered languages would contribute to the knowledge and understanding of this field.

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“I’m Sorry”: A comparative study of gender and individual differences in applying apology strategies in YouTube videos

Kristin Nguyen, Luxuan Huang, Vanessa Zhu, Andrea Mata, Shiyun Zhou

In recent years apology videos have become a very popular tactic used by social media influencers in efforts to help restore their online image. This study will compare and contrast the apology strategies used in 3 male and 3 female YouTuber apology videos by investigating the types of linguistic features that are found in both genders.  Moreover, we will further explore how the specific apology strategies being used influence the perception that their audiences/supporters have towards these specific Youtubers based on the comment section. The results showed that male Youtubers are more likely to use the “acknowledgement of responsibility” and “promise of forbearance” approach when apologizing while females are more likely to use the “explicit expression of apology” and explanation or account” strategy. Interestingly enough, the videos with the most positive responses came from 2 male and 1 female YouTuber which suggests that, according to their data set, there is a pattern in certain apology strategies that are more effective than others.

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Gendered Use of Compliments and Insults in Professional Video Game Streaming

Kavi Dalal

This study examines male to male power hierarchy in online multiplayer video games. Using screen recorded footage of a professional gamer’s live broadcast as data in addition to transcription based conversation analysis, I present some observations on how compliments and insults are used in male socialized environments. The analysis sheds light on actual tactics employed by men in order to build solidarity and/or establish power amongst themselves. In conclusion I discuss the importance of continuing linguistic analysis at the intersection of gender and hierarchy in emerging online and male dominated environments.

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