Sociolinguistics – Page 19

Driving from 101 to The 101: An Analysis of Determiner Usage in Californian Speech

Pranav Singh, Melissa Yang, Yoosoo Jang, Ross Perry, and Nathan Midkiff

Do you refer to Highway 101 as “101” or “the 101”? Perhaps many people have seen the case of putting ‘the’ in front of the highway. A determiner, like “the”, is an important element of grammar, and is usually used in front of a noun that has a specific meaning. But the rule of determiner “the” can be ignored in particular cases. We can also observe from the mass media that it is sometimes a little different when referring to highways. We found two videos from YouTube that show different ways to call Highway 101 according to region.

In the news on Los Angeles, Highway 101 is referred to as ‘the 101’.

In the news on San Francisco, Highway 101 is referred to as ‘101’.

Most people know that language can be influenced by culture and geography, but the majority of people do not know how they’ve influenced the language. Little research has been done to explore what reasons affect the difference between regions especially in referring to highways, so in this study we aim to analyze the connection between specific sociological/geographic factors and the usage of “the 101” or “101” by collecting data.

Introduction

On the outside, California may be seen as a monolithic region that has developed its culture around the year-round sunny skies and proximity to beaches. However, locals of the state have a much more varied view of the state and often see it as made up of distinct regions (Bucholtz et al., 2007). The most apparent divide would be between Northern California and Southern California, and good representations of these two regions would be the San Francisco Bay Area for Northern California and Los Angeles (LA) for Southern California, as both are extremely popular areas for their respective regions.

The divide between these two regions has created a difference in speech and cultures. One such difference is the way Bay Area locals omit the determiner “the” before referring to highways, while LA locals include the determiner “the” before referring to highways. For example, Bay Area locals would refer to highway 101 as just “101” while LA locals would refer to highway 101 as “the 101.” This is a widely acknowledged phenomenon, but there hasn’t been much empirical evidence to back up the claim that LA locals use “the” before referring to highways more than Bay Area locals do. So we’ve decided to examine whether or not this claim can be sustained with concrete evidence.

In addition, we believe that if there happens to be an increased usage of the determiner for LA locals, then this may be a result of a more prominent driving culture for the LA region than the Bay Area region. The LA region may have a culture that revolves around driving due to driving’s necessity and time-consumption. Driving takes up a large chunk of time in LA locals’ lives, both physically and mentally, so its importance is reflected in the usage of “the”, as the determiner is often used to signify importance or familiarity for the following noun (Birner, 1994).

Methods

In order to analyze this, we turned to the social media platform Reddit. Using the community pages (“subreddits”) for the Bay Area and Los Angeles (https://www.reddit.com/r/bayarea/ and https://www.reddit.com/r/LosAngeles/), we collected a sample of posts on each subreddit which referenced U.S. Highway 101. We chose to examine discussions of US-101 because this highway runs plays a major role in transit in both of these communities. It runs through both San Francisco and San Jose, two of the major cities which make up what is considered the Bay Area, and it runs through a large part of Los Angeles. We wanted to choose a highway that is common to both of the regions in order to rule out the possibility of the determiner use being a purely lexical distinction that is used only in combination with the names of specific roads.

We turned to Reddit for our data collection because it provided us good access to the members of the community in a natural setting. And, we decided that because this distinction was of a lexical nature, that it was likely to carry over into the written speech of both communities. Most importantly, by examining these Reddit communities, we are studying the speech patterns of people according to their identity. By participating in an online forum specific to a community, an individual establishes that they identify as being a member of that community, therefore showing that our observations are measuring people who identify as Los Angeles or Bay Area residents.

We collected a sample of posts discussing U.S. 101 from each subreddit, then observed the proportion of those in which the determiner “the” was used before the highways number. In order to examine the importance of the highway in each community, we then examined the proportion of posts in each subreddit that made reference to U.S. 101 within the past 3 months.

Results

What we observed was that members of the Los Angeles subreddit did, in fact, use the determiner “the” more often when referring to U.S. 101. In r/LosAngeles, 16 out of the 23 posts collected referred to U.S. 101 as “the 101”, whereas in r/BayArea, only 3 posts did so in the same sample size. In both communities, posts without determiner use included simply referring to “101”, or including other technical terms related to highways, such as “N”, “North”, “S”, “South” etc.

Figure 1: Comparing the proportion of posts that mention U.S. 101 that use the determiner between r/BayArea and r/LosAngeles

Our analysis of the rate of discussion about U.S 101 revealed that in the Los Angeles community, 14 of the 6364 posts from the past 3 months referred to the highway, and in the Bay Area Community, 11 out of the 5637 posts from the same time period did so. While Los Angeles referred to the highway only slightly more, leading to us being uncertain of its significance, the similarity between the rates at least shows that our belief that the highway was of similar importance to both communities is substantiated.

Figure 2: Comparing the proportion of total posts in the past three months that mention U.S. 101 between r/BayArea and r/LosAngeles

If we perform some simple statistical tests on this data, we can see that what we guessed is true. Our first test, looking at use of the word “the” before the highway number, was statistically significant, with a p-value of p=0.00005. This means that if our hypothesis was incorrect, and the Los Angeles subreddit didn’t use the determiner more often, then there would be a probability of 0.005% of getting the data we collected. This means it is very likely from our sample that our hypothesis is correct. However, when we do the same calculation on our test, looking at all posts in a three-month period, we get a p-value of 0.38209. This means that we are not able to conclude anything about how often the subreddits mention U.S. 101. Therefore our tests support the common belief that people from Los Angeles say “the 101”, but we can’t be sure if they talk about the 101 more than the Bay Area, so our experiments aren’t able to give a conclusive reason as to why people from Los Angeles say “the 101”.

There have been some limitations in our study that prevent certain conclusions. For one, our research is purely correlational, so we can’t say that the increased importance of driving resulted in the usage of “the” before freeways. Instead, we can only say that as the importance of driving increased, so did the usage of “the”. In addition, we only look at the 101 as a representation of all Californian freeways and the Reddit forum as representation of each community. But there may be many highways that have different circumstances than the 101, and there may be many different kinds of people that don’t use Reddit. And those who do use Reddit and choose to post may have unique motivations to do so, which skews our representation of the communities even more. This means that our results don’t necessarily apply to other freeways or the complete LA/Bay Area community, so we can’t be too general with our conclusions.

Conclusions

The usage of “the” with the highway 101 is much more common in the Los Angeles area than in the Bay Area. This is a well-known phenomenon: the California residents in our group unilaterally recognized that there was a divide between Northern and Southern California residents and that Los Angeles residents favored usage of “the.” In the article “‘The’ culture war” from the University of Pennsylvania’s Language Log, the author Mark Liberman cites a San Francisco advertisement reading:

“Bank while you wait for the BART or the Muni”

The Language Log viewer who sent in the advertisement claims that the company has “clearly … lost their SF roots” (Liberman, “‘The’ culture war”). This notion of “losing” a geographical identity by using a particular linguistic feature associated with another geographic location shows evidence of the determiner “the” possibly indexing a Southern California identity. However, it’s important to note that this phenomenon might be unique to highway names. Later in the article, Liberman brings up Northern California “the”-isms, such as “the Embarcadero” and “the Bay Bridge”, while also referring to a surprising group of Southern California non-“the”-isms, such as “Wilshire Boulevard” and “Rodeo Drive.”

Are highways special for this purpose? Is there something inherently important about public transportation infrastructure that makes a Los Angeles resident that much more of a Los Angeles resident? We cannot say for sure, but we aren’t the only people who notice it.

In the Saturday Night Live skit “The Californians: Stuart Has Cancer,” the linguistic cues that appear to index a Los Angeles-area identity are the exaggerated, annoying, and comedically out-of-place references to driving and highways. For example, when the character Stuart comes home to find his lover eating another man’s face (figuratively of course, this isn’t “The Transylvanians”), he tells him to leave in the following way:

“I said go home! Get back on San Vicente, take it to the 10, switch over to the 405 North, and let it dump you out onto Mulholland where you belong!” (Saturday Night Live, 2013, 1:03)

No rational spouse is thinking of giving the object of their wife’s extramarital desire directions home. Usually, pop culture tends to handle this situation with a crisp “I think you should get the f*** out right now.” But this is the key point: the writers are calling attention to Californians’ fascination with car culture by making it the central focus of every sentence, markedly indicating that Californians are obsessed with cars, getting stuck in traffic on highways, and memorizing every street name they encounter. And to further confirm findings from the Language Log article, when referring to the I-10 and the I-405, Stuart uses the determiner “the” while choosing not to use it for the street names of Mulholland and San Vicente.

From our empirical results in this study, we see that Los Angeles residents have a predilection for using the determiner “the” and talking about highways like US-101. And from our external links in this section, we can see that this phenomenon is well-known. We see that highway talk and the determiner “the” might be ways that Los Angeles residents are viewed to exert their local identity, and we would recommend that budding sociolinguistics researchers devote attention to how “car culture” affects Californians in other ways that we might not have captured.

Reference

Birner, B. et al. (1994). “Uniqueness, Familiarity, and the Definite Article in English.” Proceedings of the Twentieth Annual Meeting of the Berkeley Linguistics Society: General Session Dedicated to the Contributions of Charles J. Fillmore.

Bucholtz, M. et al. (2007). Hella Nor Cal or Totally So Cal? The Perceptual Dialectology of California. University of California, Santa Barbara.

CBS Los Angeles. (2018, November 9). The Woolsey Fire Jumps The 101 Freeway. YouTube. https://www.youtube.com/watch?v=H4AU_U9YwlM

KPIX CBS SF Bay Area. (2020, August 18). San Jose Police Standoff That Shut Down Hwy 101 Comes To Dramatic End. YouTube. https://www.youtube.com/watch?v=1OSYGcRN5P4

Liberman, M. (2010, December 16). ‘The’ culture war [Web log post]. Retrieved December 16, 2020, from https://languagelog.ldc.upenn.edu/nll/?p=2844

Saturday Night Live. (2013, August 12). The Californians: Stuart Has Cancer – SNL. YouTube. https://www.youtube.com/watch?v=Tt-tG6ufH90

California, Determiners, Indexicality

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Periodt, Sis! Gender Identity and TikTok Term Usage

Sociolinguistics

Camille Lanese, Chang Liu, Heather Pritchard, Merton Ung, Tracy Zeng

If you were to go on TikTok right now, one word might stand out to you: “periodt.” With a hashtag including more than 632 million views and endless videos with teenagers exclaiming “and that’s on periodt!”, you might wonder what is up with this word. In our study we examined exactly who is using the term “periodt” and when they are using it. Through surveying college-aged students, we examined if factors such as gender identity and sexual orientation affected whether or not TikTok users used the term “periodt” online or in their daily lives. After looking through the results, we concluded that gender identity and sexual orientation seemed to affect whether TikTok user knew of the word “periodt,” but had no impact on when they used the term. Overall, most participants were most comfortable using the term online, and were extremely uncomfortable with the idea of using “periodt” in a professional setting. In the future we aim to further examine the origins of “periodt” and how people acquire it as a word.

Introduction

TikToks are vertically filmed videos 6-60 seconds long. TikTokers play off various viral trends with repeated phrases and terms, and introduce new lexical items into users’ everyday speech. Importantly, users comment on others’ TikToks and share TikToks on other social media platforms. Also of note, TikTok users will write specific hashtags in their captions to make their videos appear on the “For You page”, the place where TikTok users discover new videos. Due to a combination of the users comments, hashtags, and sharing it is common for new words to become extremely viral through widespread use. TikTok’s powerful influence is reflected by the fact that the social media platform has over 800 million users worldwide. Thus, TikTok forms a kind of speech community with jargon through interaction between speakers. We analyzed college-aged students’ usage of the TikTok lexical item “periodt”, used to emphasize a point or signal the end of a discussion. We hope to answer, does a speaker’s gender identity and/or sexual orientation affect whether they adopt TikTok terms into real-life social interactions?

Figure 1: a YouTube compilation of female AAVE speakers saying Periodt

Background

The field of sociolinguistics understands linguistic variation as an “essential feature of language” through which speakers “index” aspects of their identity (Eckert 2012: 94). Specifically, gender is performance, in which speakers use linguistic features to construct and reinscribe their gender for their audience (Butler 1990: 179). For the older 18+ TikTok population, which includes our target population, using TikTok language requires more speaker agency because TikTok is not the norm, and thus TikTok language indexes aspects of identity for these speakers. Holloway and Valentine (2014) show that a teenager’s identity can become a mixture of their online and offline influences, since teenagers perceive both their online and offline identities as real spaces. Based on the studies by Eckert and Butler we see that a person’s identity is reflected in their lexicon. Our study seeks to examine whether the influence of the online spaces of TikTok will be reflected in our participant’s lexicons. Podesva (2011) analyzed the Californian vowel-shift for one LGBTQIA+ speaker and demonstrates that speakers use phonological features to index their identity. Podseva focuses on speech acoustics in various social settings. Our study is different from this in the way that we are examining our participant’s lexicons, and in the way that we are analyzing a large number of people rather than one person. The study that was most similar to ours was Banman’s (2014) which analyzed Tweets by men and women and compared gender markers such as pronouns. Banman et. al found that there are lexical items strongly associated with each gender. Our study is similar because we also seek to examine the lexical differences between gender, but also different because we are also examining their sexual orientation. To learn more about gender and other demographics in social media, check out this TED Talk by Johanna Blakley.

Figure 2: Rickey Thompson, openly gay American actor, using “periodt” on Twitter

Methods

We collected data from 100-200 American college students (aged 18-22) through an online survey with questions about usage of TikTok lexical items as well as gender and sexual orientation. Our preliminary hypothesis was that members of the LGBTQIA+ community are more likely to know and actively use TikTok language, since many TikTok terms are associated with said community. We also hypothesized that self-identified females are more likely to use TikTok terms, because of TikTok’s origins in Musical.ly and because self-identified females are usually the pioneers of new lexical items. The word that we chose to analyze was “periodt”, since it is a word that not only has roots in the LGBTQIA+ community, but was also popularized through TikTok by the use of hashtags and viral audios used in various videos. If you’re interested, check out this TED Talk by Dao Nguyen that discusses what kinds of videos and topics go viral.

Results

We received 109 total responses. Of those, we received 80 female responses, 26 male responses, and 3 nonbinary responses. Although our percentage of female responses was disproportionate, we still found that a far greater majority of female respondents were familiar with the term.

Table 1: Percentage of respondents familiar with the term “periodt”, by gender

Similarly, according to our hypothesis, when sorted by self-reported sexual identity, all of our LGBTQIA+ respondents were familiar with the term, while a smaller percentage of our heterosexual respondents were familiar with the term. At the same time, we are aware that there is further diversity within the queer community, so analyzing the entire LGBTQIA+ community as one homogenous section does weaken our analysis.

Table 2: Percentage of respondents familiar with the term “periodt”, by sexual orientation

Figure 3: How comfortable participants are using TikTok terms in different scenarios from a scale of 1 to 5, including all participants that know the term “periodt”

Figure 4: How comfortable participants are using TikTok terms in different scenarios from a scale of 1 to 5, divided by both gender identity and sexual orientation

Regardless of gender identity and sexual orientation, overall, the participants feel the most comfortable using the term “periodt” online with their peers, a little less comfortable when using it face-to-face with peers, and almost equally uncomfortable when using it online and face-to-face with a professor/boss. When using the term with peers, female and non-binary participants feel slightly more comfortable when using it online than using it face-to-face. From our data, male LGBTQIA+ participants clearly feel more comfortable than other groups when using the term “periodt” with their peers online and face-to-face, and slightly more comfortable when using it with a professor/boss online. But since only 2 LGBTQIA+ male participants responded, we cannot confirm that this pattern applies to most people with this gender identity and sexual orientation. Also based on the results we received, non-binary participants feel less comfortable than other groups when using the term with their peers online and face-to-face. But since only 3 non-binary participants responded, we cannot confirm that this pattern applies to most people with this gender identity and sexual orientation.

Discussion and conclusions

Based on our results, we conclude that gender identity and sexual orientation seems to affect the familiarity with newly-emerged TikTok terms, but have little effect on the usage of those terms in both online and in-person settings. We collected data on many factors that we didn’t have the time or space to explore, such as participants’ native languages and hometowns. We also asked participants which other TikTok terms they were familiar with, which in the future could be helpful to contextualize “periodt” among other TikTok lexical items. An additional factor that we didn’t have the space to explore was race. Although the word “periodt” originates from the African American community, we did not analyze whether our participant’s knowledge of the word could originate from their racial backgrounds. The final shortcoming of our study was our lack of LGBTQIA+ participants. In an ideal study, our study would have equal amounts of participants for every subdivision we analyzed, especially for the male LGBTQIA+ participants.

In further studies, we would be interested in correlating time spent on TikTok with comfort using the word. Then, we would analyze whether time spent on the app factors more into TikTok term usage than gender or sexual identity (Holloway and Valentine, 2014). A similar study could also be performed on other words that have become popular with AAVE and LGBTQIA+ roots, like “shady, tea, sis”, but on platforms like YouTube or Twitter. Also of interest, how do people, especially non-binary and LGBTQIA+ groups, acquire TikTok terms? Our study asked whether the participants knew the word, but not whether the participants learned the word from TikTok. A few more specific questions to investigate include: Is there a difference in the usage between talking with close friends and classmates? Is there a difference when talking with the same group of people, but about different topics?

Our study regarding the term “periodt” is also important for broader research and issues. The large number of participants using “periodt” despite not knowing the origins of the word is emblematic of the larger issue of erasure in western society. Despite the fact that “periodt” has its origin from AAVE, the word was co-opted by the LGBTQIA+ community during the late 1960s. “Periodt” was then popularized in mainstream culture as a word that indexes LGBTQIA+ membership, since it was popularized by shows like Rupaul’s Drag Race, Queer eye, and various YouTube content creators. Prior to TikTok, “periodt” was only popular within the LGBTQIA+ and African American communities. After “periodt” was popularized on TikTok through famous audios, and hashtags, the word had become viral and it was no longer used exclusively by the queer and African American communities. Our study shows an abundance of users who use “periodt” without actually knowing the origin of the word. Other viral phrases that were created by the black community and was co-opted by the LGBTQIA+ along with “periodt” that do not have their origins widely acknowledged would be “spill the tea, sis, yas, queen, shady”. Acknowledgement of the origins of “periodt” is important because of the U.S. erasure of black and queer history.

References

Bamman, David, et al. “Gender Identity and Lexical Variation in Social Media.” Journal of Sociolinguistics, vol. 18, no. 2, 2014, pp. 135–160., doi:10.1111/josl.12080.

Butler, Judith. 1990. Gender Trouble: Feminism and the Subversion of Identity. New York: Routledge

Eckert, P. (2012). Three waves of variation study: The emergence of meaning in the study of sociolinguistic variation. Annual Review of Anthropology, 41, 87-100.

Holloway, Sarah, and Gill Valentine. “Cyberkids? Exploring Children’s Identities and Social Networks in On-Line and Off-Line Worlds.” 2014, doi:10.4324/9781315011257.

Kulkarni, Vivek, and William Yang Wang. “TFW, DamnGina, Juvie, and Hotsie-Totsie: On the Linguistic and Social Aspects of Internet Slang.” 22 Dec. 2017.

Podesva, R. (2011). The California vowel shift and gay identity. American Speech, 86(1), 32-51

gender

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Code-Switching Between Mandarin Chinese and English: Do You Use “lol” or “xswl”?

Sociolinguistics

Wenqian Guo, Sum Yi Li, Yichen Lyu, Sok Kwan Wong, Yingge Zhou

Code-switching has become increasingly common as globalization allows international exchanges across cultures to take place more frequently. And as studying abroad becomes more accessible to students around the world, more speech communities with distinctive code-switching patterns are being formed. As we pondered the topic for our research project, we looked around and realized that not only are the majority of our group members native Mandarin speakers studying in the US, but collectively we also belong to this wider speech community that tends to code-switch between Mandarin and English. We could not help but wonder — do local students in China talk like us at all? And is there a reasoning behind the way we talk? It is these questions that formed the basis of our research.

For the project, we narrowed down our research to focus on just Internet slang used on WeChat, China’s answer to WhatsApp. Through our proprietary survey and by combing through chat history we collected from our participants, we discovered some very interesting findings. Continue reading to find out how and why Mandarin-speaking international students in the US code switch on WeChat.

Introduction and background

Chinese students who study in the U.S. often code switch between Mandarin and English and our project was aimed at examining the motivations behind the phenomenon.

We specifically looked into our subjects’ texting patterns on messaging app WeChat and compared them to Chinese students either residing in China or other non-English speaking countries in an attempt to confirm our assumption that code-switching is prevalent among Chinese students in the US. We also surveyed the students and asked for reasons behind their choice of words. We hypothesized that convenience as well as a desire to appear foreign-educated are what motivated the code-switching.

We based our project on Luke’s (1984) study on language mixing in Hong Kong, where English words are often inserted into Cantonese conversations. Luke (1984) concluded that code-switching in Hong Kong is partly pragmatically motivated (when the objects being discussed do not have Chinese translation) and partly socially motivated (when the individuals want to identify as better educated and westernized).

We extended Luke’s (1984) study to cover Mandarin Chinese, which is spoken in Mainland China. Presumably English mixing is more prevalent in Hong Kong because it is a former British colony. Code switching is not common among locals in China, but it is observed among individuals who have exposure in English-speaking countries.

Methods

The target population for our project consists of two major groups: college students who study in the US and college students who study in their home country China. Both groups of students are native Mandarin speakers. Our data collection was divided into two parts. First, we created a survey to ask both groups of students to select from provided word choices under different text conversation scenarios and provide us with a reason behind each choice. The participants were also asked to specify how frequently they would code-switch in their daily conversations with friends and family on WeChat. Second, we collected a series of chat history based on three main topics, namely schoolwork, casual conversations and sensitive topics. For the survey, we interviewed a total of 17 students from UCLA as our sampling for the groups of students who are foreign educated. We also interviewed a total of 6 college students who are based in China.

Results

For the purpose of discussion, our project referred to college students who study overseas as “US students” and those who study in China as “local students”.

School Work Survey Question 1: Which word would you use when you want to talk about a homework assignment that will be due soon. For example, “我明天有个作业___”. (Tomorrow I have some homework__)

For the first survey question regarding homework assignment, all of the US students chose the English word “due”, while the majority of the local students chose the equivalent Chinese words “要交”. Even though most students from both groups attributed their choice to a similar reason, which is language habit, from the perspective of the US students, “language habit” refers to a way to try to assimilate into the American culture, while in the context of the local students, it is more of an innate and natural habit.

School Work Survey Question 2: Which word would you use when you want to unenroll a class you have registered before? For example, “这节课不符合我的时间表, 我想__这节课”. (This class doesn’t match my schedule, I want to __ this class.)

For the second survey question relating to unenrolling classes, all of the US students chose the English word “drop”, while the majority of the local students picked the Chinese equivalent “退选”. However, both groups have different reasons behind their word choice. The majority of the US students said they chose the word “drop” because there is no equivalent translation in Chinese. On the other hand, the local students preferred using Chinese due to language habits influenced by their friends and family.

Based on the reasons the participants provided, it appears that being in different environments and different speech communities are the main reason students develop different language habits. Besides, the source of learning also influences their word choices. The US students tend to find it hard to find Chinese translation for words related to schoolwork since they learned these words in an English-speaking environment.

Casual Conversation Survey Question 1: Which word would you use if you want to express laughter or something that is funny when you are chatting with your friends.

When asked how they would express laughter when texting, the majority of the US students chose the Chinese words “哈哈哈哈” (“Hahahaha”), while the majority of the local students picked another Chinese phrase “笑死我了” (“I laugh to death”). Even though both groups of students used Chinese to express laughter, each side has their own reason for the specific choice. The US students said they preferred “哈哈哈哈” (“Hahahaha”) as a language habit, while the local students preferred “笑死我了” (“I laugh to death”) because they believed the expression could better demonstrate their emotions and the situation. The results showed that Mandarin-speaking college students preferred to use Chinese when expressing laughter regardless of where they are studying.

Casual Conversation Survey Question 2: How often do you often replace words in a sentence from Chinese to English when you are chatting with your friends? For example, “我一会儿有个meeting or presentation”, “让我来表演一段rap”.

When asked how frequently they code-switch between Chinese and English when texting their friends and family, the majority of the US students said at least once every one to two days. Meanwhile, half of the local students said they seldom code-switch — only at least once every few weeks or months during their daily conversations.

Sensitive Topic Survey Question 1: What kind of curse words would you use most frequently when you are chatting with your Mandarin speaking friends?

When asked which curse words they most frequently use, both groups of students chose “卧槽/我操/我靠” (roughly translated as “Damn it/Fuck”) but for different reasons. The US students said this option best describes their feelings, while the local students said it sounds less harsh than the other choices.

Sensitive Topic Survey Question 2: Which word would you use when you need to discuss something that is related to the issue of sexual assault. For example “你上个月有听说那条新闻吗？有个女生被__了”. (Did you hear the news? A girl was __)

When asked what words they would use to say “rape”, the majority of both the US and local students chose the Chinese words “强奸”, instead of “rape” in English or the abbreviation “QJ”. Most local students said they did not worry whether the word sounds too direct or inappropriate but would rather want to just say what really happened. Similarly, the US students said they felt more comfortable with the Chinese words.

Our survey: English version and Chinese version

Chat History Analysis

The chat history was obtained from Chinese students studying in the US. The words marked red were originally in English, while those in black are translations from Mandarin.

This is a conversation between two participants about the recent US election. P1 said they expected people to stop protesting in two days. Instead of using the Chinese words (“游行”) for protest, P1 code-switched from Mandarin to English. Since protests are rare in China, and the Chinese words for protests are rarely used, presumably it is easier for the participant to just use the English word when texting.

Marked code-switching is observed when P1 expresses a slight disagreement with P2. As P2 feels empathetic to Trump, P1 emphasizes with English to express that they don’t feel sad for Trump’s loss.

Another interesting point is that P2 referred to Trump as “Grandpa Trutru”, his Chinese nickname. Chinese people sometimes use nicknames to refer to important politicians, which is likely stemmed from China’s censorship on sensitive topics. Discussions about certain politicians are considered highly sensitive, so to avoid censorship, they come up with nicknames for the politicians. For example, Trump is also known as “懂王” (“the king who knows it all”), while Biden is “睡王” (“the sleepy king”). The use of nicknames exudes a sense of humor as well as dials down the seriousness of the discussion of political issues.

In casual conversations, code-switching again lends convenience and gives emphasis. It also constructs a common identity among people in the conversation. In the above conversation, the participant is telling a story about their roommate being forcefully taken away to the hospital after answering routine behavioral questions wrong, which is a well-known cultural shock among Chinese students studying in the US. As this routine is not performed in China, the participant constantly code-switched on noun and verb phrases for convenience. Also, all participants of this conversation are Chinese international students. The code-switching builds a common identity among them because the participant expects everyone to know the consequences of answering yes to routine behavioral questions. The last two sentences are examples of marked code-switching that emphasize on the participant’s disbelief: The participant is surprised that his roommate answered yes. It is a final revelation of the ending to this anecdote.

Similarly, in the above conversation, code-switching again serves as building a common identity among Chinese students in the US. The participants constantly chose to use short English vernaculars, such as “yes,” “go,” and “yea.” This indicates that they have been immersed in an English-speaking environment, so when expressing agreement and excitement, they tend to code-switch to English. Also, P1 ignored all English spacing in the conversation. This is because when typing English with the Chinese keyboard, adding spacing can be time-consuming. For convenience, P1 simply ignored the spacing during texting. Even so, P1 chose to respond in English, demonstrating that it is more natural for them to use these English vernacular phrases.

In academic scenarios, code-switching almost entirely occurs in jargon. By using English jargons such as “peer interaction” and “mutual engagement”, the participants demonstrated their educational background as foreign.

Discussion and Conclusion

From the aforementioned data findings, it can be concluded that Chinese students who study abroad and those who only study in China show different patterns in their language usage. When it comes to schoolwork and casual conversations, insertions of English words into Chinese sentences and code-switching between English and Chinese are mainly observed among students who study abroad, while less so is seen among the domestic students. This difference can likely be attributed to a lack of translation equivalence, as many school work-related words are only applicable in the US. The same goes for casual conversations. It is therefore not difficult to understand why the US students would tend to code-switch to English when there seems to be a lack of translation equivalence in Chinese.

Besides, the needs for social and emotional expressions evoked by surrounding cultural environments and contexts also contribute to the different language patterns and code-switching. In China, a conservative country both culturally and politically, pro-government language is encouraged, while the freedom of speech is repressed. This repression has in turn cultivated stronger needs for expressing emotions among the local students, leading to their choice of more confrontational and direct wordings when discussing sensitive topics. On the other hand, a higher frequency of code-switching among the US students revealed their needs to be identified as foreign-educated and share common identities with other speech participants of similar backgrounds.

Our findings point to the important roles that social surroundings and the kind of language encouraged within these environments could play in one’s speech patterns and code-switching. That being said, the language choices that the participants made are not entirely dependent on their own characteristics but are rather choices commonly negotiated by one’s surrounding social context as a whole. This contextual-based understanding therefore sets a reminder for future conversation analysis and sociolinguistic study, that one’s speech patterns could not be analyzed and identified without incorporating the nature of the surrounding speech contexts and cultural environments.

Hedging and Gender in the STEM Community

Sociolinguistics

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.

Introduction + Background

Entering new environments with high expectations can be difficult for anyone. This is especially relevant in academic environments, where imposter syndrome and the resulting stress are abundant. As a general stereotype and resulting from years upon years of patriarchal oppression, it is believed that women hedge more than men. This also carries the implication that women are less intelligent. Now we know from a lot of other research (and a thing I like to call common sense) that simply being a woman does not make a person less smart than anyone else. Though why are we looking at female STEM majors in particular? The number of women in stem has been far less than the number of men in the field for many years in our past. In recent years, many more women have been joining the STEM field, choosing to become STEM majors. Their numbers have been steadily increasing every few years from what it was in the past, but also has not been increasing fast enough to be considered a “boom” of any sort. Does the high expectation for the small percentage of women entering these male-dominated fields make them feel less confident in their abilities to acquire/distribute STEM-related information? Could the use of more hedging be intentional, and used as a sort of cushion for being able to make mistakes? It is also possible that the content within STEM fields, which is generally considered difficult to grasp, may make it harder for people to assert their knowledge of the subject. This semi-spontaneous interview test attempts to collect data that can be used as evidence to answer these questions.

Methods

To collect our data, we used a sample of twelve STEM majors at UCLA: six male, six female. We conducted a short interview, asking questions that would illicit hedging. The questions were:

1. What is one STEM class that you’re currently taking?
2. Can you explain something that you’re learning in that class?
3. What is the most difficult thing you have learned in that class?

These questions were designed to provoke deeper thought and test mastery of the participant’s field of study. After the interview, we then explain to the interviewees our study and hedging, specifically what it is and how someone might use it in certain scenarios. We give this explanation to allow them to reflect on the subject and determine how much they think they use it and why they use it as a linguistic tool. We also do this after conducting the interview so the participants are unaware of the topic, giving us the most genuine, unaltered responses. Finally, we give them a post-interview survey to answer based on their recent reflections on a scale from 1- 10. The questions include:

1. How often do you think you use linguistics hedging?
2. How confident or capable do you feel in your field of study?
3. How often do you feel condescension or face condescending remarks said to you in your field?
4. How much do you believe the environment you face (and the amount of discrimination / condescension) in your field has contributed to this your confidence in said field?
5. How much do you believe the level of confidence you have affects the number of hedges you use?

With this whole study, we are able to collect genuine responses of hedging from the interview and perspectives on hedging from the participants with the survey.

Results

Each interview from the twelve STEM majors lasted around two to three minutes. We noticed that the STEM majors commonly used hedges: “like”, “possibly”, and “may”. From the twelve interviews, we noticed that the most common hedge used by both genders was the word “like”. The STEM majors used the word “like”, not to show comparison, but to express vague statements. For example, one of the interviewees said:

“… Learning how you compose and create a CT scan from like Fourier transforms …”

Here, the interviewee used hedging to evasively state that she was learning how CT scans are created through Fourier transforms.

To analyze the interviews, we counted the occurrences of hedges in each interview and calculated the average frequency of hedges per gender.

The data revealed that females had an average of 6.83 hedges per interview while males had an average of 8.83 hedges per interview. Although females had a lower average of hedges than males, we noticed that the amount of hedges for both males and females were fairly similar with the exception of a few outliers. The maximum amount of hedges used was 18 hedges by a male, which is much higher than the amount of hedges counted for the other males. The minimum amount of hedges used was two hedges by two females. Because of the outliers and the fairly similar amount of hedges, it is hard to conclude that gender causes a change in frequency of hedges. Our data suggests that it is possible that the frequency of hedges is related to the individual’s competency in the subject rather than gender. To have more conclusive results, we should have interviewed a large amount of people, but because our sample size was too small and hedging counts were fairly similar, it is hard to definitely conclude that gender affects hedging usage.

When we analyzed the questionnaire data from our post-interview surveys, we discovered several notable observations. One of those observations was that women felt significantly more adversity than men. For the question “how often do you feel discriminated against or underrepresented in your field?”, we found that women felt approximately 7 times more discrimination than men. Similarly, for the question “How often do you feel condescension or face condescending remarks said to you in your field?”, we observed women feeling about 3 times more condescension than men.

Figure 2: Perceived adversity by men and women

From those two questions alone, the data suggests that gender disparity continues to exist and has propagated into the UCLA community as well. Specifically, it seems that how much women experience discrimination today has not changed enough especially when it comes to factors such as earnings and promotions in the workplace.

Furthermore, when we analyzed the data from the question asking how confident they felt in their field of study, we observed that women felt more confident than men by a slight margin of 0.5. However, the data from the “How much do you believe the environment you face (and the amount of discrimination / condescension) in your field has contributed to your confidence in said field?” showed that women gave less credit to their environment. Surprisingly, the data suggests women believe they developed their confidence outside of the environment in their respective fields. This begs the question, if this is the case then where exactly are they getting their confidence from and why is this the case?

Discussion and Conclusions

Our hypothesis was inconclusive that women in stem hedge more than men since the data that we received was inconsistent. Even though women in the survey reported a higher frequency of using hedging the frequency of hedges in the interviews conducted was around the same. Therefore, we cannot conclude from our study that women in STEM hedge more because of under representation and lower confidence levels in their field. However, there are many parameters that could influence our findings such as our small sample size, as there were only 12 people interviewed and surveyed in total. Another factor is that the people we used in our experiment were people we were familiar or friends with thus they might have been more comfortable around us, which could influence the frequency of hedges they used.

In the future we could possibly recreate the study but with a larger sample size which would even out the outliers in our data. We could also sample random people which we have not met previously. Furthermore ,we could research the differences in hedging between gender in different majors of the STEM field, such as computer science, mathematics, physics, etc, and observe whether the field you are in can affect the difference in hedging frequencies of men and women. We could also conduct the study among people interested in STEM of different education levels, such as in high school, and different colleges.

Through examination of hedging we can have a better understanding of the effects of gender in the STEM fields and language use. A continuation of this study could be very beneficial to stem majors when considering the role of gender in the stem field and the level of confidence they portray. In particular this research can be important when considering the work force, specifically that women are less likely to ask for raises and promotions. This could be tied into hedging since hedging relates to being uncertain and less confident in one’s ideas. We believe that this is a very important area of research with a lot of potential to explore the effects of gender in STEM and look forward to future contributions regarding this topic.

gender

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

Sociolinguistics

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.

Introduction and background

A meme is a piece of cultural information that holds certain ideologies or behavioral concepts and is transmitted from person to person. The word ‘meme’ stems from the Greek word ‘mimeme.’ The root mim- means to mimic and the English suffix –eme is used to imply a unit of linguistic information, as seen in words such as phoneme and lexeme. The term ‘meme’ was coined from ‘gene’ and similar to a biological gene, the nature of a meme is to mutate or replicate when being transferred from person to person. The world of social media is full of memes as they are seen as a major part of today’s popular culture.

We noticed that a good handful of popular memes contain AAVE regardless of whether or not the original poster was a member of the Black Community. These memes were quite popular, too, which makes the use of AAVE within memes apparently index ‘coolness’ or ‘hipness’. There also wasn’t just one part of AAVE that memes utilized, but instead integrated syntactic, lexical, phonological, and morphological aspects of the dialect.

Figure 1a: An Example of AAVE Switching Involving a Syntactic Feature (“he b getting yelled at”)

Figure 1b: An Example of AAVE Switching Using a Lexical Feature (“the class was **wildin**”)

AAVE has been studied pretty extensively by linguists in the past. Of the studies that are relevant to our project, most of them show different ways that AAVE contributes to identity. That is, they show that people use it to communicate things about themselves to others. Those things could be anything from membership in social groups (Rickford et al., 2015; Anderson, 1999; Labov 1973; Sweetland, 2002), to particular attitudes (Ilbury, 2020). However, almost none of this research looks at AAVE on social media, let alone in Twitter memes.

For youths, social media is quickly becoming one of the richest sites for creating cultural connections. As such, the linguistic norms that are founded there can quickly become widespread. Our work addresses this understudied, but extremely significant, domain of AAVE usage. We set out anticipating that meme-creators would incorporate AAVE in their posts to tell readers that those posts should be read and interpreted as memes.

Methods

Occurrences in Memes

Before we tested our hypothesis, we first had to figure out what kinds of switches were occurring between SAE and AAVE. As such, we collected instances of AAVE usage in memes by visiting meme-posting pages on Instagram. We recorded whether these AAVE features were syntactic, lexical, phonological, or morphological in nature, and we also considered what topics the memes addressed. Figure 2 below explains which topics we observed.

Survey

Upon gaining a lay of the land, a survey was designed. We sent it out in order to receive data that would allow us to address our hypothesis. In the survey, participants first provided consent to publish their (anonymously-attributed) data. They then stated their age and level of experience with memes.

Following this demographic collection portion, the participants were exposed to examples of memes in which one AAVE feature (and therefore one switch) was used. They were then asked…

- Whether they believed the usage of AAVE was ironic (disingenuous) or not given a poster’s race (African American and non-African American).
- What they believed the posters were trying to do by switching from SAE to AAVE
- Whether they believed their answer to (2) would change if the poster’s race was the opposite of that presented in (1).

At the end of the survey, we asked them to respond to the following question if they had identified any switch as ironic: “If you said that some usages were ironic, do you think that irony is meant to indicate something about how the humor in the posts should be interpreted?” This allowed us to directly address our hypothesis.

Results/analysis

Occurrences in Memes

The meme data consisted of the type of linguistic feature involved in the switch from SAE to AAVE and the topic that the meme addressed. We calculated the number of occurrences for syntactic, lexical, phonological, and morphological features per topic, and the results are presented below in Figure 3.

Figure 3: Raw Number of AAVE Occurrences per Topic

We then calculated the percentages of each occurrence per topic, and these results can be seen below in Figure 4.

Figure 4: Raw Percentages of AAVE Occurrences per Topic (NOTE: overall here means all topics combined)

In terms of the overall number of AAVE features observed, the data showed a clear preference for AAVE syntactic features, followed by lexical, phonological, and morphological features. This order of preference occurred in 4 out of the 11 identified topics. The most popular topic of memes was daily routines, while the least popular was the occupation topic.

From our survey, we received a total of twenty seven completed responses. Twenty six participants were in the age range of 19 – 29 (approximately the same age as meme-posters), and one older participant (age 38) was also included in the data given their experience level with memes. Out of the four possible meme experience levels, only 3 were observed (options 2, 3, and 4). Figures 5a and 5b summarize their meme experience:

Figure 5a: Breakdown of Participant Meme Experience

Figure 5b: The Meme Experience Levels we Observed

We then averaged irony scores for each example among meme-experience groups, age groups, and overall. The irony score represented how strongly participants believed the poster’s switch would occur as a natural tendency as opposed to a conscious choice. Except for the fourth example (which participants did not view as having a switch at all), irony scores were greater when the poster was assumed to be non-African American. The meme-experience group who chose option three had higher irony scores than those who actually made memes. That said, this difference was not statistically significant according to an F-test and a t–test between the two groups. This data can be seen below in Figures 6a and 6b.

Figure 6a: Irony Score Per Participant Age

Figure 6b: Irony Score Per Meme Experience Group

We then analyzed short answer responses, which consisted of what participants believed switches indicated about the humor of the examples. We boiled down their statements into ‘themes’ of explanation and counted how many responses fell into these themes. We specifically focused on themes relating to humor and noted how strongly these were represented among the three present meme experience levels. A summary of the response data can be seen below in Figures 7a-e.

Figure 7a: Short Answer Data Summarized – How Many Different Themes (Dispersion) and How Many Rejected Responses (n/a portion)

Figure 7b: Short Answer Data Summarized – Ratio of Humorous Themes to Total Entries Under Varying Poster-Race Assumptions for Each Example

Figure 7c: Short Answer Data Summarized – Different Meme Experience Levels’ Ratio of Humorous Themes to Total Valid Entries for Each Example (NOTE: red cells are option 4 group, white cells are option 3 group)

Figure 7d: Short Answer Data Summarized – Mode (Most popular Theme) and Values of Mode For Each Example

Figure 7e: Short Answer Data Summarized – Disagreement in What Switches Meant for Each Example

As seen in Figure 7e, we calculated the degree of disagreement on what switching meant for each example. Generally, there was less disagreement when participants were told that the poster was not African American, and overall disagreement increased in later examples considerably.

Finally, we sorted responses to the final question, regarding what ironic switching was meant to indicate about how humor should be interpreted. Not every participant was instructed to answer this question, only those who indicated that ironic code-switching to AAVE was present in the previous examples. Out of the 21 responses that were eligible, 85.71% of participants believed ironic switching indexed something about how the humor of the meme should be evaluated.

The most popular response was a positive confirmation of the question. The most popular elaborated response stated that switching to AAVE signaled to read the post as a meme. To be read as a “meme” is best explained by one participant’s response:

“Yes, I believe that switching to AAVE shows to users that it is not a formal post but instead casual, humorous, and meant to be related to.”

Discussion and conclusions

Our most significant finding was the general consensus that meme-posters use AAVE to indicate how the humor in their posts should be interpreted. And indeed, our hypothesis was confirmed: participants directly stated the switch to AAVE was done so the humor of memes would be evaluated along comedic standards specific to memes (as opposed to stand-up or sketch comedy). This would suggest that AAVE has become associated with humor. And to be sure, there are negative consequences to this association. The variety could be portrayed as something humorous, lighthearted, and not to be taken seriously. One of our participants in fact commented that AAVE’s appearance in memes is justified because “certain vernacular have a playful connotation that doesn’t imply seriousness.” Obviously, this would pose a problem for those who use the variety in their daily lives, in that their speech would be trivialized and even seen as unfit for participation in larger economic and civil institutions.

Our raw data also suggested that neither age nor meme experience significantly affected the likelihood to see irony in AAVE usage. At least one of our examples however was flawed and may have skewed the data. And in fact, given final question responses, it’s likely that being in the higher meme experience group did make participants slightly less likely to view switching as ironic. It is tempting to draw the conclusion that greater experience with memes in turn translates to lower likelihood to view AAVE usage as cultural appropriation. One participant in the option four group actually recognized that SAE to AAVE switching could constitute appropriation. However, they also noted that it is unlikely that there are ill intentions around the usage itself. They believed that though meme culture may inadvertently stigmatize the variety, the community itself is not systematically “anti-black.” All of this said, we cautiously state here that the negative consequences of AAVE usage in memes do not escape some members of the meme community but also that they don’t view their actions as malicious. As such, it’s unlikely that AAVE usage will cease any time soon.

The greatest number of AAVE features found in memes were syntactic features, the first three survey examples (containing the two syntactic switches) displayed greater numbers of humorous entries, and these first three also included lower levels of disagreement towards the meanings of switches. This suggests that AAVE syntax is not only more heavily associated with memes but is also the most used type of feature in communicating information about humor. And indeed this aligns with what Sweetland (2002) claimed regarding AAVE usage: AAVE syntax was the primary means of linguistically indicating a belonging to the AAVE speech community. Meme posters are arguably not, however, trying to indicate belonging to the AAVE speech community, so there are two likely implications this finding could have. Perhaps the users are trying to imitate and evoke stereotypes regarding African Americans. Conversely, the users could be attempting to signal in-group status of their own. That is, they could be trying to say “I’m a member of the meme community, too!” by switching. We make no conclusions here since we lack evidence to prove either, but leave readers with the understanding that, regardless of humor, there are real world consequences to this type of usage.

References

Anderson, E. (2000). Code of the street: Decency, violence, and the moral life of the inner city. W. W. Norton & Company

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

Labov, W. (1973). The linguistic consequences of being a lame. Language in Society, 2(1), 81- 115. doi:10.1017/s0047404500000075

Rickford, J. R., Duncan, G. J., Gennetian, L. A., Gou, R. Y., Greene, R., Katz, L. F., Kessler, R. C., Kling, J. R., Sanbonmatsu, L., Sanchez-Ordoñez, A. E., Sciandra, M., Thomas, E., & Ludwig, J. (2015). Neighborhood effects on use of African-American Vernacular English. Proceedings of the National Academy of Sciences, 112(38), 11817–11822. https://doi.org/10.1073/pnas.1500176112

Sweetland, J. (2002). Unexpected but authentic use of an ethnically-marked dialect. Journal of Sociolinguistics, 6(4), 514–538. https://doi.org/10.1111/1467-9481.00199

AAVE, Code-Switching, Memes

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Speech Patterns as Identity Constructors Across Social Media Platforms

Sociolinguistics

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.

Introduction and Background

Slang has long been an integral part of colloquial speech and plays an important role in social communication especially among younger people, as younger generations seem to be the greatest users and creators of new slang words (Zhou, 2013). Currently, a visible linguistic trend is the frequent usage of slang usage on social media platforms, both because of the younger demographic and because the informal dialogue on social media translates well to the nonstandard forms of slang (Teodorescu, 2015). Oftentimes, different subgroups end up creating their own slang usage (Zhou, 2013), and our research question centered around how slang would define certain subgroups on social media. More specially, we asked how users constructed different identities through their linguistic variation on different social media platforms. Initially, we hypothesized that a user’s identity could be influenced by the particular slang terms they used more frequently on a social media platform, and that slang usage would be different depending on the platform it originates from. However, after initial data analysis, we pivoted to include prosody in our analysis, encompassing non-lexical speech-related information such as pitch, intonation, articulation, and gestures, which contributed equally as much to identity construction as slang (Shih & Kochanski, 2002). Our final revised hypothesis was that a user’s identity could be influenced by the varied prosody and other speech patterns, as well as frequency of particular slang terms, they used on each social media platform.

Methodology

For this research study we conducted a case study of TikTok influencer @sirthestar by examining his written and verbal communication on his TikTok, Twitter and Youtube accounts. Tiktok is a video sharing social media platform where users create a wide variety of different types of videos, with over two billion downloads across the world (Zukin, 2020). Like other forms of social media, TikTok has been the origin of new slang terms that have expanded in usage beyond the platform, especially because internet slang can develop and spread quickly because of the viral nature of online content (Zhang, 2016). We decided to analyze the speech patterns of only one person to minimize non-linguistic external factors that could affect the user’s identity. Although most well-known for storytimes on TikTok, Sir is also active on both Youtube and Twitter, which made him an ideal candidate.

We searched for both written and spoken samples from these social media platforms, and using our slang references, analyzed linguistic variation in each of these platforms. We collected data by using the closed captioning system in YouTube, written speech on Twitter, and self-captioning and our own transcriptions of TikTok dialogue. Before pivoting, our intention was to categorize the types of slang we found, such as alphabetisms, blends, clippings, and reduplicatives (Kulkarni & Wang, 2018). However, for Youtube, we could not find enough video data to make any conclusions about slang usage, and when we parsed the data of Twitter and TikTok to find significant slang terms, we saw inconclusive results because slang alone was not enough to distinguish between Sir’s identities on TikTok, Twitter, and YouTube.

Figure 1: Bar Graph of Frequency of Slang Usage (TikTok vs Twitter)

Because of this, we switched gears to include more than just slang as part of our research. With a new direction, we looked at each platform’s data and divided the samples into “comedic” and “non-comedic” content to take a look at the tone presented by Sir. We determined this by identifying if a tweet or piece of dialogue from YouTube or TikTok was meant to elicit laughter and be entertaining or not, and then identified changes in slang, prosody, and other speech patterns that contributed to creating “comedic” or “non-comedic” content on each social media platform.

Results

Twitter

Figure 2: Non-Comedic Tweet. Sir takes a stance on Black Lives Matter.

Figure 3: Comedic Tweet. Sir shares comical experience.

Twitter showed us a pretty even split between comedic and non-comedic tweets. This was mostly due to Sir’s numerous tweets on Black Lives Matter, and his purpose of spreading awareness. For “comedic” content, Sir tweeted with themes of relatability and silly snippets of his life. As for speech patterns, comedic tweets had simple words and were only a few lines long. There was not conclusively a disregard for grammar, because there was still proper capitalization and usage of punctuation like “…” or “*scrolling twitter*”, but it was fashioned casually to be humorous. Meanwhile, non-comedic tweets were usually more than one line long, and had more proper punctuation and capitalization. Some common themes included social activism, self-esteem/positivity, and gratitude towards his fans. An example is his tweet on September 17- “Something I’ve learned…don’t search for or force love, it’ll only hurt you in the long run. Focus on loving yourself, be in your bag. This creates a positive energy which attracts positive relationships ✨” This is not to say, however, that these kinds of rules applied to strictly all of Sir’s tweets, as we saw that both comedic and non-comedic tweets included use of emojis, like in the above examples, and use of all-caps written speech to emphasize emotion.

YouTube

Figure 4: Screenshot of one of Sir’s comedic videos, as shown by facial expression

On YouTube, we noticed that there was an imbalance between comedic and non-comedic videos uploaded. While Twitter was a platform that @sirthestar utilized to voice his opinions on more serious matters, YouTube was a platform where we regularly saw @sirthestar telling stories for entertainment. His language contains many slang words, and his speech pattern of repetition appeared frequently throughout his videos. One example was a quote from a video on July 24 – “Ain’t nobody gonna cheat on us, cheat on us, treat us like sh*t, none of that, none of that. What we gotta do is we play the game, we play the game, and if they think they win. NO! They didn’t win, tie them up!” This was most likely due to the fact that YouTube videos were much longer than the time duration given on TikTok, and therefore @sirthestar was able to take his time to relay his messages with more humor. However, this longer time duration also appeared as a hindrance in terms of collecting data. While TikTok and Twitter content did not contain too many words, the YouTube videos had twelve to fifteen minutes worth of spoken language, which made collecting data from multiple videos too difficult. Overall, we could see that the repetition of certain phrases that appeared on YouTube was not present on the other two platforms.

TikTok

TikTok was where Sir also had a large majority of comedic versus non-comedic videos. Of the thirty TikToks that we examined, there were only a few that focused on serious topics like politics, racism, and homophobia, but even in these videos, Sir maintained his comedic persona. This persona was evident through his use of slang and prosody in his videos. On TikTok, Sir used the most slang by far, with words like “bitch” and “y’all” being used frequently in his videos. Along with this slang, Sir would commonly clap and stress certain words in his TikTok videos for emphasis on his jokes. An example of this was in a TikTok where he said “ Mind you, I already had a long ass day, and getting cat-called was not gonna end good for him [clapping between each word].” Sir’s use of prosody and articulatory gestures were present in the majority of the TikToks we analyzed, and unlike other platforms, there was plenty of slang usage in the majority of his TikToks. TikTok was also the platform where Sir used the most stream-of-consciousness type of speaking by utilizing run-on-sentences and speaking quickly, which emphasized that every video was a performance, and he maintained a very exaggerated, entertaining persona even when he was speaking on a sensitive topic. These linguistic features contributed to his comedian persona that he developed on the application in a different way than his other social media platforms.

Conclusion

Our findings were able to help us understand how one’s identity differed depending on which social media platform they were utilizing. By researching @sirthestar’s profiles on Twitter, YouTube, and TikTok, we saw that he used Twitter for more non-comedic content, and thus there was proper grammatical usage, punctuation, full sentences, and less slang, although there was still usage of emojis in both comedic and non-comedic content. His speech patterns on Twitter proved to be more formal and demonstrated that it was a place where he could be more serious and voice his opinions on social activism. On the other hand, on YouTube and TikTok, @sirthestar uploaded content for entertainment purposes. After seeing the higher use of slang and prosody in YouTube and TikTok, we were able to conclude that he has a comedic persona on these platforms, although the persona was created in different ways on each platform. On YouTube, his comedy came from word repetition, partially because of the longer timeframes, whereas on TikTok, he had the higher frequency of slang words as well as articulatory gestures. Although we had to pivot about our original hypothesis, we learned that it wasn’t completely wrong. Slang usage is not dependent on the social media platform but rather plays a role alongside prosody and other speech patterns in developing a comedic persona on YouTube and TikTok.

References

Kulkarni, V., & Wang, W. Y. (2018). Simple models for word formation in English slang. arXiv preprint arXiv:1804.02596.

Shih, C,. & Kochanski, G (2002). Section 1: What Is Prosody? Prosody and Prosodic Models, www.cs.columbia.edu/~julia/courses/CS4706/chilin.htm.

Teodorescu, H. N., & Saharia, N. (2015, October). An Internet slang annotated dictionary and its use in assessing message attitude and sentiments. In 2015 International Conference on Speech Technology and Human-Computer Dialogue (SpeD) (pp. 1-8). IEEE.

Zhang, L., Zhao, J., & Xu, K. (2016). Who creates trends in online social media: The crowd or opinion leaders? Journal of Computer-Mediated Communication, 21(1), 1-16. doi:10.1111/jcc4.12145

Zhou, Y, and Fan, Y. “A sociolinguistic study of American slang.” Theory and Practice in Language Studies 3.12 (2013): 2209.

Zukin, M. (2020, Aug 05). TikTok Age of in the Quarantine. Variety, 46-49. https://search.proquest.com/docview/2434859387?accountid=14512

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

Sociolinguistics

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.

Introduction and Background

While there have been past studies done on examining the speech of gay men, particularly the California vowel shift among gay men (Podesva, 2011), and one that revealed a concept of gay-dar, the belief that gay men possess an ability to pick out each other in a crowd (Shelp, 2003), little research has been done on uncovering linguistic patterns within the Women Loving Women population (WLW). A member of the WLW community is loosely defined as anyone who identifies as a woman and differs from the mainstream preferences in terms of their sexual practice and identity (Eliason & Morgan, 1998). Due to being seen as deviant from the mainstream practices, they may feel the need to take different approaches when making romantic pursuits in order to establish a mutual understanding of their interest in women when talking to another individual. As WLW may often struggle with compulsory heterosexuality, the fear of being perceived as predatory, as well as the potential dangers that come with revealing their sexuality, we aimed to investigate whether there were any linguistic markers adopted among the members of the community to aid in implicitly seeking each other out. This study explores the ways WLW work around the potential barriers they face when pursuing romantic interests and when revealing their identity in hopes of gaining insight on ways to improve the inclusivity of a general community. We hypothesized that WLW would adopt practices where they refer to certain WLW-group-specific terminologies or features before making romantic or sexual advances towards another woman.

Methods

Study 1 collected people’s lay knowledge on identifying WLW by looking at popular YouTube videos that featured strategies on how to initiate romantic/sexual advances with a WLW. We found three relatively popular videos created by members of the WLW community who also covered a large realm of dating advice and made a list of those that were related to indexing sexual identity. In addition, we watched two videos that featured heterosexual dating advice and made note of the advice given to men to romantically or sexually pursue other women. By comparing the two lists of notes, we were able to identify potential strategies that are WLW group-specific.

Study 2 consisted of two semi-structured interviews that took place and were recorded through Zoom. We interviewed a total three members of the WLW community, with two of them being in a committed relationship. They were primarily asked to describe and draw from their past experiences. The interviews were guided by six open-ended questions (see Appendix A) with the interviewer following up with questions when necessary. Our questions focused on the WLW’s description of their experiences in establishing mutual interest in women using non-direct measures. Participants were recruited using snow-ball sampling and all answers were kept anonymous. After the interviews, we listened to the audio recordings and made notes of the different ways WLW chose to index their sexual identity as well as the cues they used to determine the sexual identity of their romantic interest.

Study 1 Results

In Study 1, we were able to uncover several recurring themes. One point made consistently across multiple videos was that the WLW always felt the need to immediately make their sexuality known once they realized they had feelings for the other party.

Reasons for this were that they did not want to confuse the other party into thinking that they just wanted a female friend, and they also did not want the other party to assume that the speaker is straight and think differently of them. WLW worry about giving ambiguous signals if they were to not reveal their sexual identity soon enough, which leads to the subtle incorporations of various cues in conversations, such as mentioning the pride parade, to demonstrate their sexual identity.

They also made mentions of lurking through the other party’s social media for signs pertaining to possible membership of the WLW community to know whether it would be appropriate for them to make romantic advances. WLW also tend to be cautious in making advances as they adopt a “flirting by not flirting” technique. This allows them to slowly determine if the other party has reciprocated romantic feelings without being too overbearing and only continue to proceed if there is a positive response.

Figure 1: A selection of videos on WLW flirting used in Study 1

WLW flirting: Video 1 Video 2 Video 3

In contrast, when we explored flirting advice geared towards men to pursue women, there was no mention for men to index their sexual identity to women before flirting or at any stage of the courting process. The videos generally focused on advising men to be indirect to increase excitement in women and how to appear playful and masculine.

Figure 2: A selection of videos on heterosexual flirting used in Study 1

Heterosexual Flirting: Video 1 Video 2

Although there was some overlap in advice given to women to pursue other women and given to men to pursue women, such as being subtle and indirect, the reasoning behind it was different, and a clear difference was the need for WLW to drop hints about their sexual identity. Because there tends to be less confusion in intentions when a male approaches a female, neither party is advised to hint at their own sexual identity nor advised on how to determine the other party’s sexual identity. In contrast, a common theme across videos geared towards WLW is to use references to hint at their own gayness or try to determine whether the other party is gay before advancing.

Study 2 Results

Interview 1

A summary of common themes that arose in Interview 1 are presented in Table 1 below along with some illustrative examples given by the interviewee.

Interview 2

To illustrate the results derived from Interview 2, Table 2 consists of the most important statements made by both Subject 1 and Subject 2 in the conversation. It is important to note that Subject 1 and Subject 2 have been in a WLW relationship for over a year. When answering the interviewer’s questions, they both reflected on when they first met and how this has changed or remained consistent. The middle column consists of what they answered similarly.

Study 2 Analysis

From our interviews we gathered that the majority of strategies available for Women-Loving Women to identify and flirt with other WLW are mostly non-linguistic in nature. In both interviews, WLW referred to style of dress as a primary identifier for fellow WLW. These and other aspects of popular WLW culture were also drawn upon during the flirting itself, which leads us to one overtly linguistic flirting strategy we found was used by WLW– compliments. Compliments between WLW referenced nonverbal yet mutually understood markers of WLW identity, so they were used to confirm sexuality and communicate an attempt to flirt, in addition to their function as simple compliments. Importantly, compliments between WLW and platonic ones between heterosexual women were said to differ solely in their content and not their form. We conclude that this arises from a need or desire for WLW to flirt “under the radar” to avoid the very real danger of homophobia and bigoted comments.

We also noted the potential for confusion and ambiguous interpretations of these, arguably necessary, nonverbal flirting methods. Subject 1 even described a trend among WLW to pull back on “standard” physical or verbal affection (at least among other WLW) as a way to avoid creating confusion since more open displays of platonic affection are expected among groups of women. This may contribute to a societal perception of WLW as being “colder” or “more masculine.” Future studies might investigate whether or not this is true among a larger sample size.

Figure 3: A meme employing WLW popular music artist ‘Girl in Red’ to euphemistically index a WLW identity

Discussion and Conclusions

Our ultimate takeaway from these interviews was a strong indication that, motivated by a possible fear of negative attention, members of WLW groups feel the need to be covert in romantic contexts. As a result of this covertness, we noticed a trend of relying on nonverbal cues (like clothing choice) more than an awareness of individuals phonetically or lexically indexing their “gayness.”

Even in situations where an individual might directly state “I like girls,” the implication of “I’m romantically interested in you” often remains covert. This gives the other individual a choice as to whether or not an interaction is romantic in nature, but can end up causing some confusion. Thus arises the stereotype that WLW do not flirt. In many cases, their advances can easily be interpreted as platonic interaction among women in a society where affection among women is more normalized than among men, and where revealing your sexuality to the wrong person can have negative repercussions.

Further Reading Recommendations: Although we did not cover this information in our study, there have been numerous studies done on the language WLW may use that distinguish their patterns from heterosexual women. Robin Lakoff in Language and A Woman’s Place (1975), defines stereotypical “women’s language features (WL)” as those associated with “heterosexual women’s performance of femininity.” She contrasts this with the existence of typical “men’s language features (ML),” thus creating a binary of “women’s speech v men’s speech.” It would be interesting to use this and analyze whether women in the WLW community use either one or both of the language features, and whether this could be a distinguishing feature.

References

Eliason, M.J., Morgan, K.S. Lesbians Define Themselves: Diversity in Lesbian Identification. International Journal of Sexuality and Gender Studies 3, 47–63 (1998). https://doi.org/10.1023/A:1026204208243

Lakoff, Robin (1975). Language and A Woman’s Place. Language in Society, Vol. 2, No. 1, 45-80.

Podesva, R. J. (2011). The California vowel shift and gay identity. American speech, 86(1), 32-51.

Rich, A. (1980). Compulsory heterosexuality and lesbian existence. Signs: Journal of women in culture and society, 5(4), 631-660.

Rieger, G., Linsenmeier, J. A., Gygax, L., Garcia, S., & Bailey, J. M. (2010). Dissecting “gaydar”: Accuracy and the role of masculinity–femininity. Archives of Sexual Behavior, 39(1), 124-140.

Shelp, S. G. (2003). Gaydar: Gaydar. Journal of Homosexuality, 44(1), 1-14.

flirting, gender, Language, romantic advances, sexual identity, Women Loving Women

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

Sociolinguistics

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.

Our burning question

In a direct relationship with the evolution of social speech, internet slang has transformed from being a marked informal language style to an everyday form of communication. Internet slang influences both the communicative behavior that individuals in society practice along with their daily usage of language. Previous research on first language acquisition reveals that exposure to the respective language is a crucial component to the extent of production ability. Implementing the same argument, we hypothesize that exposure to the internet, where there is an abundance of slang, will affect the frequency that internet-users produce slang in conversation. This study ultimately seeks to understand our proposed research question that asks: Does the time exposed to social media affect an individual’s conversational speech style and does this influence bleed into their different social media platforms varying informality?

How did we approach this?

In this study, undergraduate university students will be the target population; the age range is between 18 to 22 years old. Data collection will occur in two parts, first, case studies of three to four recorded conversations, each at least 15 minutes long, will be observed to determine the frequency of slang words used by each participant. The latter half of the data collection will comprise a self-report survey, in which the participant will disclose their social media, respective user handle, and average screen time for each platform. The survey will then prompt participants to reflect and report individual uses of abbreviations, acronyms, and other slang. It concludes with a free response question of whether they think that internet slang influence has a positive or negative impact on speech.

Ultimately, we aimed to examine any correlations between screen time, app usage, self-reported analyses of slang used in real life, and actual usage of slang in real life. The participants involved in this study were unaware of the purpose of the study beforehand, as well, to elicit as natural-sounding conversation as possible. Furthermore, the participants were all friends of the co-authors, to hopefully eliminate any biases potentially introduced by the onset of an “interview-like” setting. The participants were all also friends of the co-authors since during quarantine we were admittedly unsure how to go about encountering people we didn’t already know and recording natural conversations between them.

Our Findings

We asked individuals if they used Instagram, Twitter, LinkedIn, and Facebook. If they answered “yes”, they reported their average weekly screen time based off their devices’ reports or guestimate.

Figure 1: Self-reported screen time. * Reported additional socials. Only Subject F reported screen time for Facebook (2 hrs)

We asked the participants if they used any other socials not mentioned in the survey and, if the did, to list them and report their screen time.

Figure 2: Additional socials. Only the subjects who reported any screen time are displayed.

Each of the 10 subjects was asked to assess whether they used Internet slang in their spoken language. The results are displayed here in this pie chart, with the overwhelming majoring responding “yes.”

Figure 3: Self-reported usage of slang in everyday speech

Each of the 10 subjects was asked to assess whether they substituted words in their spoken language. Results appear to be mixed, but again with the majority indicating that they do so at least “Sometimes.”

Figure 4: Self-reported usage of abbreviations in everyday speech

Although Subject A(she/her) had a pretty high average screen time (see Figure 1), the frequency of internet slang used in speech and posts is very little. Looking through her different social media platforms we found that in 2020, 0% of her Instagram posts, 0% of her LinkedIn posts, and 2.5% of her Twitter posts contained slang. About 45-55 minutes of recorded speech were collected for Subject A; this data was collected in the form of a phone call with a friend and the conversation topics were school, quitting a part-time retail job, a trip to a museum, and apartment life. The results showed very little slang used (3 instances) in the form of emphasized agreement. When the friend expressed being happy living away from home and great dislike at going home for long periods of time, Subject A said “Facts! Period.” There was just one other instance of internet speech used but it was within the same conversation. While analyzing the recorded speech, we were surprised that, in the conversation about the job she hated and quit or the conversation about her trip to a museum, she did not use any slang. The tone of the first was annoyed/frustrated, while the second was very emotional and exciting. However, the Subject used the slang when the tone of the conversation was more neutral; perhaps it was the realization of the neutrality and therefore it was an effort to make the conversation more lively, or it just so happens that she thinks moving out and getting an apartment was the best decision of her life and gets excited when others feel the same. The interesting part was that after the recording was taken and the nature of the experiment revealed, Subject A laughed and expressed that she gave us a lot of material but was surprised when informed that she had only used slang 3 times. This overestimation of slang was seen among the other participants as well (see Figures 3 and 4). In the post-conversation survey, when asked if the influence of internet slang had a positive or negative effect, she answered,

“I think a little bit of both. It’s fun to use and say when with friends or in public. It also helps to reduce tension in conversations or with strangers. However, it is embarrassing and worrisome when people only speak like that or use the slang in essays, emails, etcetera.”

Additionally, Subjects E(he/him) and H(he/him) were also of note due to their significant presence on gaming platforms such as Discord (see Figure 2). During roughly 17 minutes of recorded speech, we were surprised to learn that there was little slang used (sick made an appearance a few times, as well as the occasional expletive), given that the gaming culture has evolved to contain speech community-specific language, which was confirmed in the post-conversation study and their Discord posts. Although their slang was not as active in their other social media platforms, Subject E had 68.7% of posts show slang and Subject H had 31.8% of posts show slang on their Discord (ACM Studio). This large difference between them can be attributed to Subject H’s position of co-presidents of ACM Studio, so his posts tended to be announcements, therefore, a bit more formal. The context of the conversation was generally relaxed and took place while cooking dinner. The topics of conversation strayed away from gaming topics, as there was a third individual present (not included in the study, as she is a co-author of this blog) who isn’t as big in the gaming community. Instead, the conversion-focused on classes, general Instagram-related topics, career focuses, and the like. However, in the post-conversation “debriefing” survey, the participants indicated a higher usage of gamer slang when talking to other gamers/about gaming-related topics. This included words such as pog, gg, F (..to pay respects), and broken (as in someone or something who is unfairly overpowered). As was discovered with Subject A, Subjects E and H also overestimated the extent to which they used slang in their everyday speech, indicating on the post-conversation survey that they believed their speech to “often” or at least “occasionally” use slang words.

The larger sociolinguistic picture

When trying to rationalize how little slang was used by the subjects, the words of sociolinguist Vera Regan came to mind. During her TEDxDublin, “What your speaking style, like, says about you” talk, she discusses how people’s language reflects their values, their goals, and their perception of themselves (2014). So, although Subject A is constantly exposed to internet slang, she will only choose the words that best represent her, as she is right now and who she wants to be in the future. As researchers, it highlighted for us how language is not only a tool for communication but also a form of creative expression that can hint at identity markers (like where we work, where we are from, our interests, etc.) like our style and choice in clothes.

In conclusion, throughout our study, we came to three main observations. The first observation states that some participants may have overestimated the extent to which they use slang in their speech. According to Kim and Ra, a higher frequency of the use of slang in one’s speech might be highly correlated to the limited means of communication that is available to reflect or express emotions, whether it is in written text or on social media (Kim and Ra, 2003). On the other hand, in real life, conveying emotions can come in the forms of facial expressions, voice tones, etc. Future studies investigating the effect of the use of emojis and uppercase letters would be interesting and may be useful to expand on the topic. Based on the data that we collected, our second observation stated that gender has little to no observable variance in our results. Our last observation indicates the conclusion that the topic of the conversation only seems to apply to the usage of speech in specific contexts. For instance, the speech observed of participants during playing a certain online game has sparked specific slang used in that specific game or common around social media platforms.

For future direction, it would be interesting to perform a study relating to specific speech communities. Our study unintentionally tapped on the gamer community, and an in-depth study on the usage of gamer lingo exclusively, correlated to the context in which it’s used (both online and in real life) could provide more detailed insight.

Acknowledgments

We would like to acknowledge everyone who participated in our study, especially the conversation participants who, after agreeing to participate, were told: “We’re gonna record our conversation for a bit. Don’t worry about it, okay? And no, sorry, we can’t tell you why until after. Just talk normally.” and then had to deal with the resulting confusion for a few minutes after.

References

An H. Kuppens (2010) Incidental foreign language acquisition from media exposure, Learning, Media and Technology, 35:1, 65-85, DOI: 10.1080/17439880903561876

Kim Y.S., Ra DY. (2003). Constructing an Internet Chatting Dictionary for Mapping Chatting Language to Standard Language. Lecture Notes in Computer Science, 2713. DOI: 10.1007/3-540-45036-X_72

TEDx Talks. (2014, November 21). What your speaking style, like, says about you | Vera Regan | TEDxDublin [Video]. YouTube. https://youtu.be/jAGgKE82034

Marked, Slang, social media, Speech Style

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

Sociolinguistics

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.

Introduction and Background

LGBTQ college women deserve for women’s spaces to be accepting of their identities. This research concerns how comfortable they are in the groups to which they belong, as measured by which categories of words they use during an interview. The target population was women aged 17-22 who identified themselves as members of women-centric spaces, specifically sororities or cottagecore communities. Cottagecore is a primarily online community catering to LGBTQ identities, which idealizes nature and has a distinct fashion aesthetic. Sororities are a part of Greek life, a space designed for women at universities to meet and support one another through college. Although both these spaces are focused on the wellbeing of women, they are catered to different perspectives. While sororities typically operate under fraternities’ male gaze, cottagecore communities tend to be created by and tailored towards LGBTQ women. We were interested in how the vocabulary of LGBTQ women in these spaces reflected how comfortable they were in their chosen community.

Since individuals’ environments create the framework of how they perceive and are perceived by others (Greco, 2012, pg. 567), we chose two women-centered communities to investigate how the space an LGBTQ woman belongs to affects how they view others and themselves. It has been previously shown that students at campuses without Greek life are more accepting of LGBTQ identities (Hinrichs & Rosenberg, 2002, p. 69), while LGBTQ students in Greek life often feel excluded due to heteronormativity (Fine, 2011, p. 534). LGBTQ students have also faced hostility in residence halls (Evans & Broido, 2009, p. 40), leaving LGBTQ women a small number of communities in which they can feel comfortable. We posited that these women might turn to online communities such as cottagecore, where they can feel more accepted than they would in most in-person spaces. This specific research was informed by a study that showed how women’s words linguistically reflect dependence on men (Lakoff, 1973, pg. 46). We believed that this would be true of sorority women who are counterparts to male fraternities. Much of their identity as a sorority woman likely reflects the opinions and perspectives of those men in fraternities. However, because cottagecore communities have no reason to focus on such a direct male presence, we hypothesized that those women’s vocabulary would not corroborate Lakoff’s results. There is a gap in the literature comparing on-campus women’s spaces to off-campus women’s spaces. However, because cottagecore originated as a safe space for women and those identifying as LGBTQ, we hypothesized more vocabulary surrounding comfort and inclusivity within this community. This directly contrasts sororities, where we expected that LGBTQ women would discuss more exclusion and focus more on out-group perceptions.

Methods

We interviewed two LGBTQ women, each in cottagecore communities and sororities. The researchers asked each participant the same set of interview questions about their identities and perceptions, how represented they felt in their spaces, and how others on campus viewed them and their communities. We divided inclusive and exclusive languages into specific categories, such as stereotyping and out-group vocabulary, and averaged the frequency of each category’s use between the two members of each group, then compared them to the other group’s use.

Results and Analysis

To analyze the linguistic data we collected, we created seven different language categories to analyze what each woman said about the community to which she belongs. The first was the inclusive language, which was any word or phrase representing inclusion. For example, referring to their space as a “community.” The second was warmth/comfort, which was a word or phrase referencing a feeling of safety or happiness in their space. Examples used were “comfortable” or “kind.” The third was any mention of the LGBTQ community, such as “lesbian” or “LGBTQ community.” These were the categories that demonstrated inclusivity and comfort in their space.

The more negative or exclusive categories were the following: exclusive language, any word or phrase representing exclusion of a particular person or group of people, such as “bigotry.” The next was stereotyping or labeling, any language referencing or creating a stereotype or label. Some examples we heard were “dumb party girl” or references to a “curated image.” The next was out-group language, anything referencing a group’s perception that the woman herself did not belong. Some examples were “fraternities” and “art hoes.” The last category under exclusive language was mentions of heterosexuality or cisgender people. This is not a negative concept, but since we measured comfort and confidence in LGBTQ women, we looked for positive references to their own identities rather than those of other people. We noted mentions of homophobic exclusivity. These references included phrases such as “heteronormativity” or “cisgender ideals.”

In Graph 1, each category is represented by a double bar graph for the two groups’ average frequency of use. We calculated the number of words from a specific category to the total words they used in every category and averaged it between the two women in each group. We found that cottagecore women used inclusive language at an average frequency of .401, and sorority women at .165. Cottagecore women used warmth/comfort language at an average frequency of .305, sorority women at .08. Cottagecore women mentioned the LGBTQ+ community at an average rate of .125 and sorority women at .015. Cottagecore women used exclusive language at an average rate of .075, and sorority women at .265. Cottagecore women mentioned stereotypes and labels at a rate of .045, and sorority women at a rate of .195. Cottagecore women used out-group references at an average of .02 and sororities women at .2. Lastly, we found that cottagecore women mentioned heterosexuality and cis-gendering at a rate of .02, with sororities at .03.

Graph 1 – Use of linguistic variable terms

We also noted phrases that did not fit into a specific inclusion/exclusion category but were telling about the interviewees’ perceptions. One example is that, when we interviewed the participants, we referred to both cottagecore and sororities as “communities” explicitly. However, the sorority women consistently referred to their space as an “institution” or “organization” (Fig. 1), whereas the cottagecore women-only referred to their space as a “community” (Fig. 2). We found it interesting that the sorority women chose to use words that, instead of indicating closeness to the space they belonged to, were indicative of something separate from themselves.

The sorority women were also more hesitant to critique their space. For instance, when asked about the exclusivity of their space, one sorority woman stated the following:

Additionally, both sorority women answered the question regarding a feeling of acceptance and representation as an LGBTQ woman in their space with uncertainty (Fig. 4). In contrast, the cottagecore women answered the same question with definitive yeses (Fig. 5).

Finally, we concluded that cottagecore women were much more comfortable in their community than the sorority women were. They used inclusive language at almost 2.5 times the rate of sorority women and warmth/comfort language at about 3.8 times the rate of sorority women. They also referenced out-groups much less and tended not to stereotype others. Sorority women used exclusive language at 3.5 times the rate of cottagecore women and referenced out-groups (usually groups relating to men such as fraternities) at ten times the rate of cottagecore women.

Discussion and conclusions

Based on our data, the sorority women were much more focused on others’ perceptions of them, likely because they are conditioned to focus on an image as counterparts of male fraternities. In contrast, the cottagecore women felt freer to focus on their own safe space within their inclusive community. We concluded that cottagecore women’s common mentions of being LGBTQ show that they are more comfortable than sorority women, who acknowledged heteronormativity and never mentioned being LGBTQ after the initial statement of their identity. This demonstrated that cottagecore women are less likely to internalize bias against the LGBTQ community, while sorority life normalizes these biases. Repeating this study with larger sample sizes at different universities might also yield more nuanced information regarding regional and cultural differences concerning this phenomenon.

Following this, we believe that college-age LGBTQ women need more spaces geared towards them to feel comfortable and safe because sororities do not seem to give LGBTQ women the room to explore and express their LGBTQ identities. Colleges should look towards cottagecore as a model for on-campus casual interactions between LGBTQ women in a space that is acceptable but not focused on being LGBTQ. As campus communities try to be more inclusive of all identities, it could also be beneficial for sororities to undergo more implicit bias and diversity training.

Although our research focused on LGBTQ women because we assumed marginalized identities would express more apparent feelings of exclusion, it could be that women in sororities might speak a certain way due to the space to which they belong. It might not be related to their sexual identity. For further research, it would be informative to look at whether there is a difference in how straight women and LGBTQ women, both in sororities, use these same vocabulary categories.

References

Evans, N. J., & Broido, E. M. (2002). The Experiences of Lesbian and Bisexual Women in College Residence Halls. Journal of Lesbian Studies, 6(3-4), 29-42. https://doi.org/10.1300/J155v06n03_04

Fine, L. E. (2011). Minimizing heterosexism and homophobia: constructing meaning of out campus LGB life. Journal of Homosexuality, 58(4), 521-546. https://doi.org/10.1080/00918369.2011.555673

Greco, L. (2012). Production, circulation and deconstruction of gender norms in LGBTQ speech practices. Discourse Studies, 14(5), 567-585. https://doi.org/10.1177/1461445612452229

Hinrichs, D. W., & Rosenberg, P. J. (2002). Attitudes Toward Gay, Lesbian, and Bisexual Persons Among Heterosexual Liberal Arts College Students. Journal of Homosexuality, 43(1), 61-84. https://doi.org/10.1300/J082v43n01_04

Lakoff, R. (1973). Language and woman’s place. Language in Society, 2(1), 45-79. https://doi.org/10.1017/S0047404500000051

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

Sociolinguistics

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.

Introduction and Background

2020—American society finds itself at the thrilling forefront of technological innovation, yet is still plagued by racial inequality and systematic racism. Our study aimed to tackle a portion of this American duality from a sociolinguistic standpoint. We investigated AI speech recognition systems and the identification differences while processing speech of the standard American English dialect (SAE) versus African American Vernacular English (AAVE). SAE is most broadly defined as the most uniform, accepted, and understood language in the US. On the other hand, AAVE is not solely slang or a lesser form of SAE; rather, it is a language variety that has systematic linguistic patterns and carries social, cultural, and historical stigmas. We believe AI speech recognition systems will reflect our country’s racial biases by misinterpreting AAVE far more often than SAE. As these technologies become more commonplace and embedded in society, our study’s goal is to shed light on whether AI speech technology is inclusive of the AAVE dialect and its speakers.

Methods

An overview of AAVE linguistic features can be found in a paper discussing the matter by Erik Thomas (2007), though we narrowed our focus on the most defining features of AAVE in this study. These included G-dropping, the th sound (as in bath) becoming the f sound (as in fast), reduction of consonant clusters at the ends of words (wes side versus west side), and the use of the verb be (Singler, 1998).

The analysis of the main facets of the verb be included the dropping of be, the habitual be (Collins, 2006), the use of BIN and be done. The heavy focus on be was due to the fact that it is one of the biggest syntactic differences between SAE and AAVE (Lanehart, 2015). As a result, the variants of be were most likely to affect the transcription performance of voice recognition software when processing AAVE.

This study used the abundant linguistic resources available from YouTube to exhibit the use of SAE and AAVE dialects. We compiled audio clips of authentic, normal speech in both of these varieties and fed the audio clips into an AI transcription software, specifically dictation.io /speech, as well as examined YouTube’s AI-generated closed captions. It was important to ensure that the captions we used on YouTube were the auto-generated ones, and not captions that had been manually entered by the uploader.

To determine what percentage of speech and what types of features of the dialects were misidentified by the AI speech recognition systems, we quantified the data by categorizing the accuracy of the AI transcriptions into four groups: “All,” meaning All of the words were picked up, “Most,” “Few,” or “None.” If every single word in the audio recording was transcribed correctly by the AI speech recognition system, then that audio clip was placed in the “All words picked up” group. If most words or only a few words were transcribed correctly by the AI speech recognition system, then that audio clip was placed in “Most” and “Few” groups, respectively. And if nothing in the audio clip was transcribed correctly by the AI speech recognition system, then that audio clip was placed in the “None” group.

Our hypothesis was that the percentage of interpretation inaccuracy in AI speech recognition systems while interpreting AAVE would be significantly higher than the inaccuracy recorded by speech recognition software while interpreting SAE (i.e. more AAVE audio clips would land in the “Few” and “None” transcription accuracy groups). This would lead to an expectation that voice recognition software is globally less effective for speakers of AAVE.

Results/Analysis

The results of this study were overall in line with the hypothesis—the transcription software picked up more SAE words than AAVE words. The vast majority of AAVE video samples collected had “Few” words transcribed correctly. Figure 1 shows the amount of AAVE words picked up by the transcription software. Our study found that for all of the clips of AAVE:

- 16.7% “All” words were picked up
- 16.7% “Most” words were picked up
- 56.7% “Few” words were picked up
- 10.0% “None” of the words were picked up

It is noteworthy that the transcription software was unable to pick up the majority of AAVE speech when the video clips were similar to the SAE video clips with regard to background noise, speaker volume, etc. We believe this is largely due to AAVE phonetics rather than AAVE syntax and word choice, which is what we were focusing on in this study.

Example 1: This clip was categorized under “Few” words being picked up; in the beginning of the segment, the speaker actually says *it’s a whole buncha people in New York that we know from.* (“African-American English in North Carolina”, The Language & Life Project)

On the other hand, all video samples of SAE fell under “All” or “Most” transcription accuracy. The SAE video clips had the majority of their words picked up and transcribed correctly. According to Figure 2:

- 31.6% of the clips had all their words picked up
- 68.4% had the majority of their words picked up

The results of transcribing SAE versus the results of AAVE are drastically different; we believe this is largely due to the phonetic differences between the two English varieties. The only time SAE was not picked up was when the speaker was speaking faster than normal. All of the words could have blended together, and as a result, the transcription software was not able to distinguish what was being said.

Another unexpected observation for both AAVE and SAE was that women speakers were not picked up as often as male speakers; AAVE female speakers made up 66.7% of the “None” category, despite comprising less than 20% of our AAVE samples.

Limitations

We had some limitations in our project as well. Due to COVID and time restrictions, our sample size was regrettably small at only 49 clips. We also had a few clips become unusable between the time of our finding them and analyzing them. As a result of the small sample size, we ended up noticing a huge, unexpected difference between the transcription accuracy of male versus female speakers of AAVE, but we weren’t able to draw any solid conclusions as to whether this difference is representative of a larger population. However, the difference was quite apparent within the random pool we gathered, so a study with equal amounts of male and female AAVE speakers could address this issue more equitably.

Speakers that were present in the room would also have enabled a more accurate representation of AAVE and voice recognition software’s real-world interaction. Our computers and phones were limited due to the differing microphone quality, which could have influenced the amount of words the voice recognition software picked up. In addition, we had planned to use our phones’ dictation softwares as the primary method of data acquisition, which ended up being wholly impossible, so we only had the time to find and use one transcription site.

Many of the video clips were filmed in neighborhoods, with background noise included, such as cars going by, wind, and people talking. This could also have limited our transcription site and created another layer of ambiguity. However, SAE clips with similar amounts of background noise that were played were transcribed (mostly) correctly. So it is unclear if the background noise is fully a limitation or an example of voice recognition software not picking up AAVE.

Lastly, we began to wonder if the phonetics of AAVE had more of an effect on our voice recognition software than the syntax did—that is, we wondered whether the sound of AAVE was more impactful than the sentence structure. A few of us used the software to speak sentences that were syntactically AAVE, but not phonetically, as none of the researchers of this study were AAVE speakers. Those sentences were transcribed correctly, including AAVE syntax. However, we did not have enough time to pursue that avenue of research, but it would be a promising starting point for any future projects.

Discussion and Conclusions

This investigation was intended solely to pursue the question of whether there is a racially-based difference in accuracy of voice recognition software. Despite the unexpectedly small scope of our study, we believe our results are sufficient to prompt further investigation into the reasons as to why there is such a stark difference in accuracy.

To that end, a question that arises naturally is whether closing the gap in accuracy is as simple as writing and including a few more lines of code. If it is, then what’s preventing this from happening now? And if the voice recognition software instead needs to be re-constructed from the ground up, then that, in turn, spawns a whole host of follow-up questions (e.g. Who’s paying for this development? Who’s working on it? How long will it take?).

It is, as with any discussion of racially-based imbalances, also worth considering systemic variables at play. Another question worth pursuing is whether there is a higher average level of inaccessibility to the technology sector for speakers of AAVE compared to speakers of SAE, which would contribute to a broad range of consequences—one of which might be the discrepancy in accuracy of voice transcription softwares demonstrated in this study.

In 2016, Rickford and King cited Schneider (1996) to describe AAVE as “the US English dialect most examined by linguists for quite some time.” In the academic world, it’s common knowledge that the prejudices held against AAVE — and its speakers — have no basis in fact. The drastic difference in voice recognition software transcription accuracy between SAE and AAVE is not only one more imbalance to correct in pursuit of a fully equitable society, but also a symptom of the systemic racism that influences all aspects of daily life. Studies like this one which stop at identifying the problem are only the first step; the next is to examine why the problem exists in the first place, so that work to resolve the inequity can begin.

References

Collins, C. (2006). A fresh look at habitual be in AAVE. CREOLE LANGUAGE LIBRARY, 29, 203.

Koenecke, A., Nam, A., Lake, E., Nudell, J., Quartey, M., Mengesha, Z., Toups, C., Rickford, J., Jurafsky, D., & Goel, S. (2020). Racial disparities in automated speech recognition. Proceedings of the National Academy of Sciences, 117(14), 7684-7689.

Lanehart, S. (Ed.). (2015). The Oxford Handbook of African American Language. Oxford University Press.

Lippi, R., Donati, S., Lippi-Green, R., & Donati, R. (1997). English with an accent: Language, ideology, and discrimination in the United States. Psychology Press.

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.

Singler, John Victor. “What’s not new in AAVE.” American Speech 73.3 (1998): 227-256.

Thomas, E. R. (2007). Phonological and phonetic characteristics of African American vernacular English. Language and Linguistics Compass, 1(5), 450-475.

Language, Race, Technology

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