Month: December 2021

Introduction and Background

Language has the power to reinforce harmful racial stereotypes and to determine how marginalized groups navigate American society. These groups whose speech differs from standardized English, specifically Black communities, are viewed as lacking grammar and speaking a less sophisticated language. Despite these misinformed perceptions of AAVE, many people on social media platforms, including Twitter and TikTok, appropriate AAVE words, phrases, and stylings, claiming these features as part of Internet speech. Such phenomenon continues, despite attempts from actual AAVE speakers to denounce its harmfulness:

Previous studies have researched AAVE appropriation by non-speakers on modern usage; although the analysis is limited, these papers provide a framework for why and how people with different genders and sexualities appropriate AAVE. Elaine Chun did a case study on an individual Korean-American male and his usage of AAVE “slang,” theorizing that he used it to index a hypermasculine persona and separate himself from European Americans (Chun, 2001). Due to the lack of material on the LGBTQ+ community’s utilization of AAVE, we speculated that LGBTQ+ members also used it to disassociate themselves from the white mainstream and to appear hip and nonconformist. To further explore AAVE and the personas it indexes, Ilbury argues that online spaces use the orthographic representations of AAVE to embody a “Sassy Queen” persona, which relies on stereotypes of Black women (Ilbury, 2020). Furthermore, non-Black speakers perform similar stereotype-based persona construction when they “borrow” African American Language to participate in hip hop or urban youth style. Through media representation and the marketability of “Black cool,” which is the culmination of all the desirable features of Blackness: trendiness, resilience, and eloquence, many young people and influencers use AAVE to index this stereotype based on white imagination (Roth Gordan et al., 2020). Corporate companies recognize the benefits of profiting from this perceived coolness, exploiting AAVE in their marketing campaigns and their partnerships with young influencers. These articles assist us in understanding the social and financial motivations of non-Black people’s usage of AAVE and why they take on specific personas.

In light of the existing literature, we conducted a sociolinguistic study of non-Black Gen Z members by comparing their use of language in humorous and non-humourous contexts to identify how they utilize AAVE lexicon and accent to index a “funny” or “cool” persona. Also, due to the lack of research regarding the LGBTQ+ community’s usage of AAVE and the intersection of gender, our study analyzed the frequency of these linguistic features in social media influencers’ speech, particularly those who fit these categories. Overall, the usage of AAVE that became popularized through the Internet varies depending on gender, sexuality, and context. Methods We watched videos from eight non-Black, American, Gen Z influencers on YouTube and TikTok and recorded the instances in which they employed a lexical item or grammatical construction borrowed from AAVE.

Methods

We watched videos from eight non-Black, American, Gen Z influencers on YouTube and TikTok and recorded the instances in which they employed a lexical item or grammatical construction borrowed from AAVE. Our numerical analysis was based on a simple counting method. While it seems that counting a singular “instance” of grammar would be impossible in a traditional linguistic sense, this was possible because the influencers did not use any full grammatical constructions, but rather just one word in to imitate an AAVE grammatical style; e.g., “be” as in “[subject] be [verb]ing.” To determine what words and phrases were instances of AAVE, we referenced this guide, which was created by a group of Black people and non-black people of color with the intent to reduce appropriation in online communities. For words with multiple indexical meanings (such as “bitch” as a slur for women or “ain’t” as part of Southern dialect), we took their context into account to assess whether they were appropriated from AAVE. The eight influencers we examined were:

These influencers are a small group of the most popular internet users, and should not be considered a representative sample of all influencers of internet users. Rather, we intend for their selection to be a series of eight case studies examining intraspeaker differences in AAVE use. When watching the videos, we separated their content into two contexts for comparative purposes:

Non-humorous: Any segment of video or full video centered around the promotion of a product or service, such as a promotion of the influencer’s merch or a brand sponsorship

Humorous: Any other segment of video, because even those who are not solely comedy YouTubers are known to include humor in their content

We considered commercial content like sponsored segments, brand collaboration videos, and formal interviews non-humorous because influencers tend to act “professional” and remove the informal, humorous aspects of their speech in promotional contexts. If their use of AAVE is significantly higher in humorous contexts, we can assume that their use of AAVE is tied to the indexing of humor, rather than a reflection of their natural speech variety. For the LGBTQ+ speakers, usage of AAVE mostly was characterized by borrowed lexical terms that indexed sass and cheekiness. Here is an example of how we recorded and examined AAVE use. This is transcribed from a Nikita Dragun video titled “REACTING TO MY BOY VIDEOS!!”:

● [0:58] “tea” (used to describe drama and not the drink) 

● [2:13] “clock it the house honey,” 

● [3:08] “shook,” 

● [7:08] “unclockable” (derived from clock), 

● [7:31] “thick” (referring to a person’s figure and not literal thickness). 

● Total: 5 in 10 min

This is transcribed from a Bretman Rock video titled “Hugging for the first time? Catching up with my Sister”:

● [01:11-01:24] I just know they’re bomb though. I just know they’re bomb_ (bomb meaning cool/great, extremely positive connotations) we have two dishes of broth--uhm; they’re both seafood broth one is spicy and one is mild and I believe this one is kimchi. We have udon Chinese CABbage enoki mushrooms up. In. this. BITCH. (imitating AAVE grammatical structures) 

● [01:57-2:17] SIS. (used to refer to friends/peers, not literally speaker’s sister, although in this case she is) whatchu been up to? (grammatical structures) PERIOD! (used to mean “and that’s that,” a marker of finality—online may be spelled “periodt”) and we gon keep doing you. (grammatical structure) Purr. (used to express approval/satisfaction) um YEAH BITCH you’re twenty ONE we can have drinking videos now. Period. 

● [02:55-03:02] she got FUCKT! (lexical feature, -t suffix replacing -ed) UP!Perio;d that’s all that matters_ NEW age; new DICK. purr! 

● [03:37-03:39] this bitch been telling everybody (using the habitual be, e.g. is, are, am) 

● [04:15-04:17] yeah you don’t deserve. Period.

● [04:34-04:41] the thing IS she ain’t even done like celebrating her birthday (grammatical structure) yet what the <<slurred, quick>fuck’re you gonna do in LA bitch this bitch is gonna stay in my apartment 

● [04:54-05:01] bitch I always be telling you tips and tricks but you don’t even listen so it’s like you know what some people just wants to learn on their own and that’s fine 
● Total: 14 instances in 5 minutes 
● Overall, Bretman Rock’s usage tends toward leaning heavily on the “sassy Black woman” stereotype as a persona. He overutilizes—at times randomly and incorrectly— “periodt” and more commonly puts on a persona when he is interacting with women and LGBTQ+ individuals.

As for the Non-LGBTQ+ speakers, their usage of AAVE varied. Males usually used borrowed grammatical forms and lexical terms. Here’s an excerpt from Ricegum’s video titled “Meeting My Online Crush For The First Time!”:

● [00:43] - [0:45] Yo so listen, but you can’t even be sliding into dms in Tik Toks (invariant be usage) 
● [00:58] - [01:02] She’s finna just appear. It’s finna just be magic (borrowed term) 
● [05:33] - [05:35] But you don’t be hittin’ those other dudes back? 
● Overall, Ricegum ports an overall blaccent and overutilized penultimate consonant deletion. 
● Total: 10 instances in 8 minutes

For female users, their usage of AAVE varied based on their humor. In particular, one of the influencers showed signs of self-awareness and utilizes it ironically. On the other hand, Brittany Broski uses lexicon to index a sassy persona. Here’s an example from Brittany’s video titled
“Deep Diving My Angry 2013 Tumbler”:

● [0:52]-[0:58] This whole idea of stan culture on Twitter really was like the thing at the time. If you didn’t have a stan account 
● [1:00]-[1:04] So this was my stan account in high school because I never had a One Direction Twitter 
● [3:50]-[3:54] Oh my god I loved this bitch I ate this shit up 
● [4:57]-[5:00] I genuinely was like real mean don’t wear pink, swag 
● [8:44]-[8:48] Another boyfriend Harry post wow period, period 
● Total: 11 instances in 11 minutes

Results and Analysis

Discussion and Conclusions

Most of the influencers examined here have significant differences (see Figure 1) in AAVE usage between humorous and non-humorous contexts, which confirms our hypothesis that influencers choose to change their styles for social benefits. Sexuality and gender expression also appear to have an effect on the instances of AAVE usage. LGBTQ+ influencers, especially men, tended to use AAVE to index a “sassy” persona, based on the stereotype of the “sassy Black woman.” On the other hand, non-LGBTQ+ men tended to use AAVE to index a “tough” or “masculine” persona, based on the cultural masculinization of Black people. Influencers use AAVE to index a funny, sassy, or edgy persona because of cultural stereotypes around Black people and their language. These stereotypes date back to minstrelsy in which Black people were treated as entertainment tools for white people. Today, this derogatory cultural lineage lives on in the appropriation of Black people’s language as an entertainment tool for non-Black people. These public figures’ influence leads to further spread of these speech patterns, indexed in these ways, without acknowledgement of the source and the price paid by Black people who have native fluency in AAVE. Influencers of this caliber have great potential to impact their audience, and carelessly using AAVE without acknowledging its origins will propagate the idea that AAVE is simply netspeak, which leads to the broader population of Non-Black Gen Z speakers using incorrect forms, or completely disregarding the cultural implications of knowing the language:

People tend to feel inappropriate when they incorrectly use grammar in a language they do not speak or are just beginning to learn. However, that does not appear to be the case for when non-speakers of AAVE misuse its grammar. This further contributes to the myth that AAVE is not, in fact, seen as a legitimate vernacular language of its own, and helps fuel extreme cases where people actively discredit AAVE entirely:

It is not inherently bad that the internet is shortening gaps between different cultures and as a consequence, languages. However it is the due diligence of every responsible internet user to ensure that they are not just passively absorbing all the information that is available to them and mindlessly replicating them. When the majority uses (and misuses) the language spoken by a minority only to express particular ideas, they, at best, contribute to the viewpoint that the variant is not legitimate, and at worst, reaffirm the racist ideologies that exist to oppress Black people (see Figure 5). When Black people use AAVE, people assume that they don’t know Standard English. However, when non-Black people use AAVE, people assume that it’s a choice—one to appeal to humor. These double standards further propagate racism towards Black people and devaluation of African American Vernacular.

References

Chun, E. W. (2001). The Construction of White, Black, and Korean American Identities through African American Vernacular English. Journal of Linguistic Anthropology, 11(1), 52–64. https://doi.org/10.1525/jlin.2001.11.1.52

Davis, Chloe. “The Language of Ballroom.” The Gay & Lesbian Review, 9 Mar. 2021, https://glreview.org/the-language-of-ballroom/

Eberhardt, M., & Freeman, K. (2015). ‘First things first, I’m the realest’: Linguistic appropriation, white privilege, and the hip-hop persona of Iggy Azalea. Journal of Sociolinguistics, 19(3), 303–327. https://doi.org/10.1111/josl.12128

Ilbury, C. (2020). “Sassy Queens”: Stylistic orthographic variation in Twitter and the enregisterment of AAVE. Journal of Sociolinguistics, 24(2), 245–264. https://doi.org/10.1111/josl.12366

Roth-Gordon, J., Harris, J., & Zamora, S. (2020). Producing white comfort through “corporate cool”: Linguistic appropriation, social media, and @BrandsSayingBae. International Journal of the Sociology of Language, 2020(265), 107–128. https://doi.org/10.1515/ijsl-2020-2105

Introduction

The U.S. is a concoction of many distinct cultures and languages as a result of settlement by non-natives and international students (Wharton University of Pennsylvania, 2017). There are also a variety of different English accents found in the U.S as a result of the people all over the world that are learning English. As of fall 2018, the percentage of public school students in the United States who were English as a second language speakers was 10.2 percent or 5.0 million students (National Center for Education Statistics). However, this does not mean that the U.S is free from prejudice and bias.

Sometimes accented speeches are harder to process and understand. When people are perceiving accented speech, they encounter difficulties that will reduce the “processing fluency”, as reported by Lev-Ari et al. (2010). Yet instead of simply perceiving the accented speech as harder to understand, people tend to perceive it as less truthful. One of the major groups of speakers affected by this is Korean speakers who grew up in a Korean-speaking environment. They tend to speak Korean-accented English due to not having been as exposed to standardized English (Linguistic Society of America). And so, the ESL Korean speakers could be perceived as less credible and intelligent, simply because they struggle with pronouncing some sounds in English and causing native speakers to have a harder time processing their speech.

The present study seeks to better understand how Korean ESL speakers who have Korean-accented pronunciation may be treated differently than standard American English speakers. This study explores whether listeners will perceive Korean accented English speakers as less favorable when compared to standard American accented English speakers.

Background

In Rickford & King’s (2016) study, a leading prosecution witness who spoke in African American Vernacular Accent (AAVE) had her crucial testimony dismissed. What led to this is that her speech was deemed incomprehensible and not credible. Unfortunately, this is not an unfamiliar experience for many people, including ESL Korean speakers. There are more than 81,000 Korean students studying in the United States, which accounts for 7% of the US’s international students population as stated by the United States Homeland Security (2015). Any non-native English speaker could face prejudice and unfair experiences in our society due to certain linguistic features that ESL speakers have (Lippi-Green, 2012). 

How was this study designed?

This research project focused on two parts: how non-English speakers are perceived socially about their intelligence level and credibility, and the perception of English speakers on Korean ESLs. The research was done by Google survey, and since our group members are all UCLA students, the participants were recruited on the campus. The survey was in two parts. The first part of the survey is generally asking the participants’ demographics and any experiences of racial discrimination. In the second part of the survey, the first thing that we did was ask their pre-existing bias about specific jobs’ credibilities and intelligence levels. To collect these data, we used the Likert scale to rate how they feel about teachers, lawyers, and doctors generally about their credibility/ intelligence level should be. Then we asked the participants to listen to three different recordings of reading English scripts by three different accents: Native English speaker, Korean ESL speaker who has a lot of Korean accent in it, and the third speaker who is fluent in English, has an American-ish accent, but have trouble pronouncing certain words. After listening to each recording, they were asked to rate how likely they would hire each recording’s voice as their teacher, lawyer, or a doctor. We selected these job fields because teachers, lawyers, and doctors are known to have high-intelligent, high credibility. The ratings were done on an interval scale of 1 (less likely) to 5 (most likely), and the ratings were done only after they listened to each recording.

Results & Analysis

From Table 1 and 2, we can see that most participants agree that the three professions—lawyers, teachers, and doctors— intelligence level and credibility lie between 4 or 5 on the Likert scale, which indicates that they agree the intelligence level and credibility of these professions are relatively high.

The data were run with two major systems: ANOVA and Post Hoc test. ANOVA gave an idea of whether there is a significant relationship between all three speakers, whereas the Post Hoc test compared each individual relationship between the three speakers. The following tables demonstrated the results of the ANOVA test as well as the Post Hoc test in terms of intelligence level and credibility. Note that speaker 1 represents the native speaker, speaker 2 represents the Korean accented speaker, and speaker 3 indicates the 3rd speaker.

The means of intelligence level of the three speakers are presented in Table 1. The native speaker has a mean of M = 3.01, the Korean accented speaker has a mean of M = 3.30, and the 3rd speaker has a mean of M = 3.55.

According to Table 1, the ANOVA test in terms of intelligence level demonstrated a p-value of less than o.oo1. It is known that a p-value less than 0.05 rejects the null hypothesis and indicates that it is statistically significant. Since we found statistically significant results by computing the ANOVA, we also ran a Post Hoc test to determine where the differences come from. The Post Hoc test suggested the mean differences of intelligence level between a native speaker and a Korean-accented speaker being 0.293; between a native speaker and 3 being 0.543; and between Korean-accented speaker and 3 being 0.250. The largest difference in means occurred between a native speaker and 3rd speaker with a native speaker mean of M=​​3.01 and a 3rd speaker mean of M=3.55 words. Although we found a significant relationship among the three speakers, the data does not support our hypothesis as we hypothesized that the Korean accented speaker’s intelligence level will be rated as the lowest, but the results suggest that the native speaker is rated as the lowest.

The means of credibility of the three speakers are presented in Table 3. The native speaker has a mean of M = 3.27, the Korean accented speaker has a mean of M = 3.25, and the 3rd speaker has a mean of M = 3.34.

Although in terms of credibility, the Korean accented speaker has the lowest rating compared to others, the ANOVA test in Table 2 demonstrated a p-value of p > o.05, which indicates that there are no significant differences. This result shows that the rate of credibility among the three speakers is similar, and thus reject our hypothesis that the Korean accented speaker will be perceived as less credible.

The results contradict our hypothesis. Our results suggest that Korean accented speakers are not perceived as less favorable in terms of both intelligence level and credibility.

Discussion

This research aimed to investigate how non-English speaking Koreans perceive socially and how others’ perspectives were. The ratings of credibilities and intelligence level were not as expected, and we are assuming that is due to the slower speaking pace that the native speaker recorded in the recording. Moreover, the ratings of Korean ESL speakers were not as expected and disconfirmed our hypothesis, which was that Korean ESL speakers were often treated less credible and less intelligent. However, there was a significant main effect on the intelligence level ratings between the three speakers. The third speaker with an American-ish accent was rated highest among other speakers, suggesting a third variable of speaking pace since the third speaker spoke at a fast speaking speed.

There are some more limitations in the study, such as gender bias—we only used voice recordings of male speakers. So we might want to include female recordings to avoid gender bias. Secondly, there might be a participant bias, meaning participants might assume what our study was about and rate accordingly. We also see some tendencies in the job fields; for instance, teachers were not rated as expected in the pre-rating survey. They were rated not highly enough than our expectation on both credibility and intelligence level. Since the recording was quite long, there might be selective attrition. Participants might feel bored and drop out during the experiment and rate every Likert scale as 3, which is neutral. Lastly, the sample size used was very small. Most participants were UCLA liberal arts students, and most of them were our friends who are also Korean ESL speakers. Since the participants are our friends, they could recognize our voice recordings during the experiment, which could affect the ratings. Moreover, since the sample size was small and limited, the study cannot generalize all Korean ESL students. We hope further research will consider speaking pace for future directions—for example, the relation of speaking speed and one’s perception. Or removing the slow speaking rate and making all the recording speakers talk about the same speaking pace. Moreover, to investigate further, we suggest using a Latin square counterbalanced 2×2 factorial design to see the interaction between gender and accented speech in the perception of credibility and intelligence level. So the first Independent variable would be gender (male, female), and the second Independent variable would be the accent with two levels: accented speech and non-accented speech. Further research would need more diverse participants in age and demographic. And hoping the further study could research other East Asian ESL speakers, not only limited to Korean ESL speakers.

References

English Language Learners in Public Schools. National Center for Education Statistics. (n.d.). Retrieved November 12, 2021, from https://nces.ed.gov/programs/coe/indicator/cgf.

FAQ: Why Do Some People Have an Accent? Linguistic Society of America. (n.d.).

Retrieved November 12, 2021, from https://www.linguisticsociety.org/resource/faq-why-do-some-people-have-accent.

Lev-Ari, S., & Keysar, B. (2010, June 25). Why don’t we believe non-native speakers? the influence of accent on credibility. Journal of Experimental Social Psychology. Retrieved November 12, 2021, from https://www.sciencedirect.com/science/article/pii/S0022103110001459

Lippi-Green, R. (2012). English with an accent: Language, ideology and discrimination in the United States. Routledge.

Rickford, J.R., & King, S. (2016). Language and linguistics on trial: Hearing Rachel Jeantel  (and other vernacular speakers) in the courtroom and beyond. Language 92(4), 948-988. Doi: 10.1353/lan.2016.0078.

‘speaking American’: Regions, accents and the subtleties of language. Knowledge@Wharton. (2017, January 18). Retrieved November 12, 2021, from https://knowledge.wharton.upenn.edu/article/speaking-american-regions-accents-and-thesubtletis-of-language/.

Streamers in our research

This experiment revolved around three male streamers and three female streamers. The three male streamers who were analyzed were: AverageJonas, itsRyanHiga, and Ethos and the three female streamers are Kyedae, Ploo, Tiffae. These six streamers were chosen due to their popularity/recognition, skills, and various personalities.

 The male streamers are around 30 years old, and the females are around 20. They are mostly all of white or East Asian descent and are English speakers. To be more specific, AverageJonas is a 32-year-old Norwegian English-speaking streamer, itsRyanHiga is a 31-year-old Japanese American English-speaking streamer, and Ethos is of unknown age and is an Asian American English speaking streamer. Kyedae is a 19-year-old Japanese Canadian English-speaking streamer, Ploo is a 22-year-old Asian American English-speaking streamer, and Tiffae is a 27-year-old Taiwanese English-speaking streamer.

What we already know

Based on gender stereotypes, men are perceived to be louder, more aggressive, and curse and insult more, and also since gaming is seen as a “male-dominant” space, we also thought gaming terms would be more male-gendered as well. Because of that, we decided to analyze vocabulary reactions to certain game events by gender.

We also analyzed the vocabulary differences by comparing streamers in an all-male team and a mixed-gender team. Previous research states that men in an all-male friend group played into male stereotypes in their group interactions more than those who had their girlfriend or female friend in their groups (Cameron 1997). In our experiment, we thought that these previous findings could suggest that female streamers may also feel pressure to conform to their male teammates and speak in a more stereotypically male manner, so that was an aspect we decided to look at.

We decided to analyze streamer and fan interactions between genders. Previous research stated that women communicate to build relationships, so we thought we would see that when observing how streamers interact with their viewer’s donations (Van Herk, 2012).

Methods

For the experiment, we watched six streamers for key vocabulary words in their speech and their chat interactions. The streamer’s vocabulary choices were analyzed when they reacted to these three events: donations from fans, winning a game, and losing a game. The reactions to those events were grouped by event and gender- for example, one group would be “Vocabulary in reaction to winning a game from a male streamer.” Then the groups would be compared across the same event and against opposite genders. An example comparison would be comparing reactions to winning by male streamers against responses to winning by female streamers. We watched each streamer stream for an average of 2 to 3 hours in total.

While we were watching the stream, we noted down some vocabulary and phrases used by the streamer. Then we sorted phrases between each scenario and compared the differences. After we sorted the words and phrases, we looked at the difference between streamers and gender. We also decided to analyze our overall results of vocabulary choice based on the gender of the streamer’s teammates.

Thesis

We found a clearer divide in the vocabulary when we compared streamers that played with all-male team and streamers that played in a mixed-gender team. Through our results, we can conclude that while there were differences in a streamer’s vocabulary due to the streamer’s gender, the main differences relied more on the gender of their teammates. Streamers with all-male teammates used more gaming-specific terms, insults, and cursed more. But as for the streamer’s own gender, we found that females were more likely to negatively refer to themselves in the face of a negative event, while males would blame the game and enemy team. We also found that females were more likely to initiate and maintain friendly interactions with their viewers and team.

Results / Analysis

Win reactions based on streamer’s own gender

To start off, we compared the vocabulary that females used when they won a game versus the vocabulary used by males when they won a game.

Female wins

As you can see from the word cloud generated from the data across the three female streamers, we identified that female streamers didn’t use any swear words in the situation of a win. Female streamers tended to comment on the win as the group’s achievement, using phrases that begin with the subject “we”. Two out of the three streamers used different variations of the phrase “oh my god” which is commonly associated with the regional marker of valley girls. Male wins

Looking at the result of the male streamers, we saw that male streamers would swear after a win. One of the male streamers, Ryan Higa used the Sh-word after a win. Overall, when commenting on a win, the male streamers tend to phrase the comment as talking about the game and not how well they played as a group(we).

Through these word clouds, we can see that there’s gendered language – the females kept their female gender identity through speech reminiscent of valley girls, despite being in a male-dominated space, and males, true to our hypothesis, used more crude language.

Loss reactions based on streamer’s own gender

Next, we analyzed the reactions of losses by females and by males.

Female loss

The word cloud on the right shows the utterances of the female streamers when facing the scenario of a loss. Unlike the result from utterances after wins, we saw swear words. The F-word was used in the phrase “oh my f**king god”. Another commonality noticed between the female streamers is that they tend to address the loss of the team as their responsibility, using the subject “I” when commenting on the loss, for example, “I messed up” or “I can’t win a game”.  Female streamers also used encouraging phrases such as “that was a very nice try” to show support towards teammates.

Male loss

The male players also used swear words after a loss, for example, the F-word was used in the phrases. In general, they form their phrases without criticizing the loss as their responsibility or their team’s responsibility. Instead, they use phrases that begin with “that”, “they” or “this girl” to address the game or the other team/team member. 

The main difference between female reactions to losses and male reactions was not what we expected- we saw both genders swear (but males to a greater degree). The biggest difference was actually the target of negative or aggressive language. The females tended to blame themselves for the loss and put themselves as the target of any negative descriptors or words. Males would blame the game or the enemy team. We also saw that females, to further put the blame solely on themselves, would congratulate their teammates.

Donations / subscriptions interactions based on streamer’s own gender

We then looked at reactions from females and males to donations and subscriptions by their fans. We mainly looked at the manner in which the streamers interacted with their fans, and we found that all-male streamers used the same phrase for all their donations and subscriptions: Thank you XXX (viewer’s name) for the donation/subscription. Two of the female streamers also used this format; however, one of the female streamers interacted with the audience by asking about their life and would reciprocate the viewer telling her “Peace and love” by saying “Peace and love” back. This indicates that female streamers demonstrate a rapport style and make an effort to build relationships in their communicative behavior. This agrees with Deborah Tannen’s claims, suggesting that women are more likely to use language to build and maintain relationships while men are more likely to use language to communicate factual information (Van Herk, 2012).

Overall vocabulary results based on teammate’s gender:

While we conducted our research, we noticed that there seemed to be a divide in gendered terms not by the gender of the streamer, but by the gender of the teammates that the streamer played with. We aggregated our vocabulary results and split them by the gender of their teammates, resulting in the bar chart below.

Streamers playing with all-male gamers said many more swear words (f*** words, sh** in particular) and gamer terms (frags, bait, camp, pick, etc), while streamers playing with a mixed-gender team did not say any swear words from our analysis and fewer gamer terms. This is due to in particular how male streamers feel the need to act more masculine and also use more common lexical entries when with other male teammates only. However, when female streamers were with female gamers or mixed gendered teammates, they tend to overall be calmer and use more relaxed language.

Another difference that was noticed was the patterns that male streamers and female streamers exhibit during the same scenarios were unexpected. For example, when male streamers and female streamers make the call-out that the enemies were attacking the B site, Kyedae says “They’re going B, right?” and Ethos says “They’re going B, they’re going B.” Kyedae said this scenario to a teammate in more of a questioning, unsure manner even though the enemies were clearly rushing B site. Ethos, however, said his callouts in a confident, command-like manner. Furthermore, what was interesting was that even if the call-out was wrong, female streamers tend to speak in a safer question-like manner, but male streamers spoke in confident manners then apologize when they were wrong.

Conclusion

Since we analyzed male and female speech in a heavily male-dominant space, we believe that our findings could apply to other male-dominant spaces. We looked at references to a similarly male-dominated industry- the sports entertainment industry. In Eastman & Billings (2000), they found that the tone used by commentators when describing male athletic wins was “enthusiastic…but derogatory” towards women’s athletic wins. This seems to relate to our findings that female streamers were more negative towards themselves and could suggest a larger connection to how derogatory attitudes towards women’s achievements in male-dominated spaces prevails and can even permeate to the women themselves. Also considering that E-Sports is still an emerging field, it’s plausible to say that the previous biases that existed in older male-dominated industries like the sports entertainment one examined in Eastman & Billings have been long established and continue to reappear today.

As for future directions, we looked mainly at the gender of streamers and controlled on popularity, the game played, and the scenarios reacted to. But through our study, we found that there were a lot more factors that could be confounding. For example, the players had varying skill levels that could have swayed our results, so we could better control for that. Another factor we saw was the gender of the teammates our streamers played with. We saw a stronger correlation between the gender of the teammates and gendered lexical entries. It would be an interesting follow-up study to analyze the interactions of an all-female team such as Cloud9 White, versus an all-male team’s interactions.

References

Çakır, G. (2021, March 5). Twitch slang and common terms explained. Dot Esports. Retrieved November 12, 2021, from https://dotesports.com/streaming/news/twitch-slang-and-common-terms-explained

Cameron, D. (1998). Performing gender identity. Language and gender: A reader.

Clayton, N. (2021, February 24). Valorant wants more women and ‘minority genders’ competing at its highest levels. pcgamer. Retrieved November 12, 2021, from https://www.pcgamer.com/valorant-wants-more-women-and-minority-genders-competing-at-its-highest-levels/

Eastman, S.T., & Billings, A.C. (2000). Sportscasting and sports reporting: The power of gender bias. Journal of Sport and Social Issues, 24, (2), 192-213

How many people play valorant? player count tracker (2021). Dexerto. (2021, October 4).         Retrieved November 12, 2021, from    https://www.dexerto.com/valorant/how-many-people-play-valorant-player-count-tracker-2021-1668158/.

Messner, M.A., Duncan, M.C., & Jenson, K. (1993). Separating the men from girls: The gendered language of televised sports. Gender and Society, 7 (1), 121-137

Nakandala, S. (2016, November 22). Gendered Conversation in a Social Game-Streaming Platform. Gender Conversation in a Social Game-Streaming Platform . Retrieved from https://www.researchgate.net/profile/Supun-Nakandala/publication/310611118_Gendered_Conversation_in_a_Social_Game-Streaming_Platform/links/5839c31808aef00f3bfbbbf1/Gendered-Conversation-in-a-Social-Game-Streaming-Platform.pdf.

Pellicone, A. J. (2017, May 6). The game of performing play: Understanding streaming as Cultural Production. Retrieved November 12, 2021, from http://library.usc.edu.ph/ACM/CHI%202017/1proc/p4863.pdf

Takahashi, D. (2021, June 14). How riot games will ensure that Valorant’s esports stars include women. VentureBeat. Retrieved November 12, 2021, from https://venturebeat.com/2021/06/14/how-riot-games-wants-to-ensure-that-valorants-esports-stars-include-women/

Van Herk, G. (2012). What is sociolinguistics? (Vol. 6). John Wiley & Sons.