A Digital Take on Modern Model Minority: Not So Subtle Asian Traits

Subin Kim, Jihee Choi, Fiona Dai, Chris Ngo

This study investigates social implications of Asian Americans being stereotyped as a model minority. The notion of the model minority basically highlights only positive aspects and successes of a group, while ignoring or downplaying the negative aspects and characteristics. Many Asian Americans have been preconceived as “nerdy” which fits the stereotype of the model minority. To be more specific to this topic, we examined how the idea of the model minority is actually used in Asian Americans’ daily life through the most popular medium of social interaction among adolescents and young adults nowadays– social media. Based on the purpose for this study, data was collected from a Facebook group called Subtle Asian Traits which has more than 1.6 million members and is shared with diverse posts of the discourse styles mostly related to Asian cultures. The posts were then analyzed for content involving the concept of model minority, and divided into two categories. Between the two groups are those fitting the stereotype of model minority, and those in which involve school de-emphasis content and African American Vernacular English (AAVE) slang. Through analyzing this study, the model minority was considered to connect with positive outcomes and reduce some negative effects of discrimination.


Society tends to categorize people based on certain traits and characteristics. These traits are shaped by an individual’s own background through ethnicity, culture, gender, and upbringing. Although most people know that individuals are varied in personality and traits, they tend to have preconceived notions about groups of people due to these categorizations, or stereotypes. Stereotype experiences are often associated with negative discrimination due to their conceptual overlap. However, we want to differentiate the two ideas in this study since previous research has found positive effects of certain stereotypes (Kiang, Witkow, and Thompson, 2015) and therefore stereotypes can be examined with or without negative discrimination. To further distinguish these two concepts, we will define negative discrimination as “biased actions or behaviors toward an individual due to his or her group membership” that are typically adverse based on Fishbein’s study on “Peer Prejudice and discrimination” (1996). Many studies in recent years have demonstrated the efficiency function of stereotypes which reduces one’s cognitive load (Sherman & Frost, 2000). That is, when one is dealing with cognitively demanding duty, such as multitasking, one can rely on stereotypes to save processing capacity and thus increase working efficiency.

We will focus on this stereotype often associated with Asians, the model minority image of Asians as diligent overachievers in this paper. The model minority image was formally identified over a half century ago (Peterson, 1966), and it still persists in social environment today. According to Poon, Squire, Kodama, Byrd, Chan, Manzano, Furr, and Bishundat (2015), the model minority is “a monolithically hardworking racial group whose high achievement undercuts claims of systemic racism made by other racially minoritized populations”. The idea of the model minority highlights the successes of the group, while ignoring or down playing the negative aspects and characteristics. In this specific case, Asians are typically seen as a “good” minority that succeeds and are considered “honorary whites”. It also lends itself regarding racism against Asians becoming normalized due to the fact that the traits of Asian stereotypes are considered desirable. Prior study has suggested that the model minority image of Asians could provoke unfair treatment from peers in the form of negative discrimination (Niwa, Way, & Hughes, 2014).

Due to model minority status highlighting academic success, Kiang et al. have examined the developmental implications of being stereotyped as model minority in Asian American adolescents (2015). The time period from adolescence to early adulthood is worth investigating because one goes through ethnic exploration and identity formation within this time frame and later become stable. Moreover, research has shown that over 99% of Asian adolescents have had at least one in which they perceived as being stereotyped as a model minority (Thompson & Kiang, 2010). Kiang et al. (2015) have measured perceived stereotypes and negative discrimination using self-report surveys, but no study has yet examined perceived model minority through the most popular medium of social interaction among adolescents and young adults nowadays ­– Facebook.

In this paper, we aim to investigate the implications behind Asian adolescents and young adults’ stereotypes and ethnic-based social interaction on social media by virtue of analyzing the model minority concept related posts on the Facebook group called Subtle Asian Traits. Kiang et al., (2015) have found that the model minority experience is connected to positive outcomes and can reduce some of the negative effects of discrimination on academic adjustment through the quantitative method. We will qualitatively test whether their findings still hold true in social media.


We primarily collected data for this study on a Facebook group called Subtle Asian Traits. Facebook is one of the most widely used social networking services in the U.S. Facebook may be used to facilitate new relationships and maintain existing relationships according to Ellison, Steinfield, and Lampe’s study on Facebook “friends” (2007). Similarly, Facebook can be a great tool for groups with common characteristics to express their respective identities. For the same reason, a group of high school students who share the same ethnic identities–Asian Australians–formed the Subtle Asian Traits Facebook group one year ago with the intention of creating a platform for English speaking Asians to exchange their shared cultural experiences.

We wish to investigate the discourse style the population uses in the Facebook group. It has more than 1.6 million members and contains memes–humorous images, videos, microtexts, etc.–that are mostly related to Asian culture. In the group, the largest population was Chinese, followed by Vietnamese, Filipinos, Koreans and Taiwanese. In addition, the group is made of people from all Asian countries. This is including, but not limited to: Malaysia, Japan, Indonesia, Cambodia, Thailand and Hong Kong. The group’s population tend to be from a variety of places, but is mostly composed of Westernized Asians.

The stereotypes that people usually have for Asians is that they portray positive characteristics such as being nerdy and intelligent; however, every group or ethnicity cannot be defined by the same features or characteristics. Therefore, Asians cannot be considered that they conform to a “nerdy” and successful stereotypes. To break this discrimination, we will discuss how the newer generation of English speaking Asians are diverging from the model minority stereotype by creating a counter culture that tends to appropriate African American vernacular English (AAVE). According to the journal “Appropriation of African American slang by Asian American youth”, the author explains that non-African Americans have followed and imitated African American black culture and slang. It is shown that one’s identity is identified by the language and slang that he or she uses, not discriminated by race. Likewise, the group such as the Subtle Asian Trait were created to humorously refine negative images and change stereotypes.

Project Design

The data sample in this study consisted of fifty-six posts from the Subtle Asian Traits Facebook group. We first chose two categories of keywords: 1) academic related such as “study,” “exams”, and 2) AAVE slang, e.g., Finna, Bae, Plug, Finesse. Then we manually selected thirty posts for the academic related category and twenty-six posts for the AAVE category. Furthermore, because the average “likes”– a characteristic of the posts occurred from members’ agreement with the post within this Facebook group–was 1,000 to 2,500, we decided to only include those posts that had over 2,000 “likes” so that the posts we selected were able to represent what was popular in this 1.6 million member Facebook group.

Figure1: an example of a post from the Subtle Asian Traits Facebook group.


The results for our data were divided into two groups. One group showed a tendency to match the characteristics of the model minority. The other group did not fit this mold, but instead showed signs of attempting to break free from it.

In one group, English-speaking Asians express difficulties and hardship about getting stressed when they study. They showed more empathy for their difficulties when involving Asian social content such as Doraemon, Soju, and Bangtan Boys. Some data is still focused on exam and studying, which shows doing well on exams is a popular topic within the group. In the other group, English-speaking Asians attempted to change stereotypes by emulating black culture and AAVE slang. One of the data showed empathy for a new culture using AAVE words such as ‘peeps’ and ‘twerking’. Furthermore, for example, people used AAVE ‘sup’ combing their language ‘wo’ which means ‘I’, so they could make AAVE word ‘wassup’ with their own meaning that ‘say hello to myself’. They tended to be more interested in content regarding partying and general school de-emphasis items. This stands in contrast to the current perception of the Asian model minority of nerdy, submissive, and rule abiding. Overall, our data showed that some of English-speaking Asians stuck to the concept of model minority, but the other tried to change and deviate their stereotypes.

Discussion and Conclusions

The results showed primarily two different distinct groups within Subtle Asian Traits. While these groups have some overlap, there is a significant voice and content difference. Our hypothesis mainly discussed only one of these groups, those conforming to the modern model minority stereotype. Within our research, however, we did encounter this counter group that seeks to diverge from previous notions thought about Asians. This split can be attributed to those forming groups based on shared stereotype-based interactions, and also those seeking to separate from previous ideals and instead relating to other ethnic groups.

For those breaking from the idea of the model minority, it can be seen that many seek identity within other minority groups. A previous study has shown that Asians borrowing AAVE slang terms are often an attempt to be the “Other Asian” and rely on stereotypes of other racial groups to construct an identity of their own (Reyes, 2005). This “Other Asian” changes the view of the Asian model minority and shifts the focus to a new perspective. The model minority is often a binary view that many individuals in this group find themselves forcibly conforming to. Rather than using AAVE to “act black” or as a different race, it can be argued that this new group uses slang to create their own identify, separate from any previous groups. While fashioning a new identity, however, those in this new subgroup can still visibly benefit from the positive outcomes of the model minority.

A previous study has shown that the effect of discrimination on Asian American adults’ distress varied depending on social context and the environment’s ethnic density (Syed & Juan, 2012). However, with the emergence of social media, such as Facebook and the prevalent usage of such platform among adolescents and young adults, we argue that the distress can be attenuated by sharing similar stereotyped-based social experience with each other on social media and thus creating a in-group sentiment. Additionally, Kiang et al. (2015) has suggested potential differences among Asians who reside in traditional settlement areas, such as Los Angeles or New York City, that have a long history of immigration and non-traditional ones that are emerging immigrant communities. Through the increasing popularity of social media usage, we believe that the regional differences will be diminished.

The model minority related posts have shown a system of camaraderie amongst the group users on Subtle Asian traits. As stated before, the model minority experience is oftentimes connected with positive outcomes and can actually reduce some of the negative effects of discrimination. Through the posts analyzed, the in-group sentiments created have shown a positive impact in which members relate to academically based posts. The posts show positive reinforcement towards the idea of high academic achievement, while also maintaining the positive aspects of the model minority stereotype.

Two new forms of Asian identity have emerged as a result of new age model minority. The two orientations have allowed for Asian youth and young adults to connect and establish new identities, while participating in their own new form of subculture. As it takes a discourse approach, social media allows for more analysis on the creation of new identities and formations of previous stereotypes and groups.



Ellison, N. B., Steinfield, C., & Lampe, C. (2007). The benefits of Facebook ‘‘friends’’: Social capital and college students’ use of online social network sites. Journal of Computer-Mediated Communication, 12(4), 2–3. Retrieved 24.11.19 from http://jcmc.indiana.edu/vol12/issue4/ellison.html

Fishbein, H. D. (1996). Peer prejudice and discrimination: Evolutionary, cultural, and     developmental dynamics. Boulder, CO: Westview Press.

Kiang, L., Witkow, M.R. & Thompson, T.L. J Youth Adolescence (2016) 45: 1366. https://doi.org/10.1007/s10964-015-0336-7

Niwa, E. Y., Way, N., & Hughes, D. L. (2014). Trajectories of ethnic– racial discrimination among ethnically diverse early adolescents: Associations with psychological and social adjustment. Child Development, 85, 2339–2354. doi:10.1111/cdev.12310.

Peterson, W. (1966). Success story: Japanese-American style. New York Times Magazine, 9, 20–43.

Poon, OiYan; Squire, Dian; Kodama, Corinne; Byrd, Ajani; Chan, Jason; Manzano, Lester; Furr, Sara; Bishundat, Devita (June 2016). “A Critical Review of the Model Minority Myth in Selected Literature on Asian Americans and Pacific Islanders in Higher Education”. Review of Educational Research. 86 (2): 469–502. doi:10.3102/0034654315612205. ISSN 0034-6543.

Reyes, A. (2005), Appropriation of African American slang by Asian American youth1. Journal of Sociolinguistics, 9: 509-532. doi:10.1111/j.1360-6441.2005.00304.x

Sherman, J. W., & Frost, L. A. (2000). On the Encoding of Stereotype-Relevant Information Under Cognitive Load. Personality and Social Psychology Bulletin, 26(1), 26–34. https://doi.org/10.1177/0146167200261003

Syed, M., & Juan, M. J. D. (2012). Discrimination and psychological distress: Examining the moderating role of social context in a nationally representative sample of Asian American adults. Asian American Journal of Psychology, 3, 104–120. doi:10. 1037/a0025275.

Thompson, T. L., & Kiang, L. (2010). The model minority stereotype: Adolescent experiences and links with adjustment. Asian American Journal of Psychology, 1(2), 119–128. doi:10.1037/a0019966.

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Emojis: The 21st Century’s Universal Form of Digital Communication

Elisha Daria, Julia Jacoby, Jocelyn Martinez

Since their inception in 1999, emojis have become essential to how we communicate. Utilizing the iconographetic communication model devised by Christina Margrit Siever (2019), our group wanted to examine and compare how people use emojis within a public sphere, such as Instagram or Twitter, versus a private one, such as SMS. We hypothesized that emojis used in a more public sphere would have a much more structured approach with primarily decorative or aesthetic purposes as a means of marketing a distinct online persona; for more private spheres, we hypothesized that emoji usage would be a lot more broad and relaxed, with more frequent usage overall and less standard forms or unspoken usage rules. Drawing our data from 48 high-school to college-aged individuals from Generation Z, we used a mixed methods approach in measuring intra-user variation from platform to platform. In doing so, we analyzed emoji frequency and usage patterns, and were able to see distinct differences in the ways in which our demographic used emojis to communicate. Our findings indicated that there was indeed an intra-user difference in emoji usage in public versus private spheres, but the ways in which these differences manifested came down to personal preference from user to user.

An Introduction to Our Study

In an increasingly digital world where technological literacy has become a necessary skill, emojis have become an invaluable aspect of digital communication in the twenty-first century. This study seeks to understand the ways in which we use emojis in the context of iconographetic communication in our day-to-day lives (Siever, 2019). From one-on-one conversations with a close friend via text, to an open-ended public conversation initiated by a single Tweet, our team wanted to measure and observe intra-user variation in public versus private spheres. In doing so, we hoped to see the motivations, methods, and reasoning behind the use—or omission—of specific kinds of emojis in daily digital interactions. These practices are especially prevalent in individuals born from the years of 1995 to 2009, known as Generation Z. These individuals have grown up in the age of the smartphone, and play a crucial role in the dynamic nature of online trends. In analyzing high-school to college-aged Gen Z social media users, we hoped to get a look into the linguistic influences of the demographic at the forefront of digital branding.

A particularly interesting element of more public platforms such as Instagram and Twitter is the way in which the user is forced to find a balance between achieving mass appeal and maintaining some semblance of individuality and authentic self-expression. This results in versatile social media users who are skilled at accommodation to a wide audience while also creating and presenting a distinct online persona. By comparing and contrasting the patterns in emoji frequency and usage between platforms, we wanted to see how a public or private audience ultimately impacted the speaker’s choices. As the first study of its kind, we hoped to gain insight into the unspoken common practices that govern digital communication.

Understanding Iconographetic Communication & Gen Z

Utilizing the term coined by Christina Margrit Siever, we focused our study on instances of iconographetic communication (2019) among high school to college-aged social media users of Gen Z; in doing so, we were hoping to find trends in the digital communication practices of this demographic. On average, each member of Generation Z is exposed to thirteen hours of media a day, producing individuals who are “highly creative, constantly adaptive, and have a highly marketable digital mindset” (Vitalar, 2019). In tracing these users’ patterns of usage across SMS, Instagram, and Twitter, we were expecting to see some form of intra-speaker variation, with emojis used as sociolinguistic markers (Van Herk, 2018, p. 54, 120). This means that each individual would use emojis differently from platform to platform, subtly tailoring their specific iconographetic communication methods to fit their public or private sphere. Examples would include the use of certain prestige forms or targeted rhetoric, as well as the use of emojis for stylistics and aesthetics to create and promote a specific brand. Studies have shown that social media influencers use emojis as a means of adding appeal, enhancing and amplifying persuasive content, and encapsulating emotional appeals through marketing rhetoric (Ge & Gretzel, 2018). Tactics such as these are key for maintaining user engagement, which is an integral part of the social media experience for regular users as well.

Siever’s iconographetic communication model was crucial to this study because it gave us a framework for viewing the ways in which emojis are used in communication. With “icono” derived from the Greek for “image” and “graphic” derived from the Greek for “writing,” iconographetic is a catch-all term that combines pictoral and typed characters and gives context to the unique linguistic function of emojis as typed images that convey sentiments worth a thousand words. Figure 1 shows Siever’s model in its entirety, with iconographetic communication branching into its modal and referential functions. The modal function describes an emoji’s ability to complement and modify written messages, while the referential function is used when an emoji replaces words or parts of words as an extension of the linguistic proposition (Siever, 2019, p. 130, 137). When coming up with our digital survey, we tailored our questions to fit either the modal or referential function, and also used this model in analyzing actual texts, Instagram posts, and Tweets submitted by one of our participants.

Figure 1. Functions of emoji in iconographetic communications (Siever, 2019, p.144)

How We Conducted Our Study

To conduct this research, our team used a mixed methods approach by collecting and analyzing both quantitative and qualitative data. We began our data collection by sending out a Google survey to forty-eight participants between the ages of fourteen and twenty-four. The survey consisted of three sets of questions regarding personal emoji usage within text messaging, Instagram, and Twitter. All participants were required to answer the text messaging portion first, then could choose to take either the Instagram or Twitter section—or both—depending on their personal usage habits. As seen on the right in Figure 2, our quantitative data was drawn from Likert scale questions on a scale from 1 to 5 (Likert, 1932, p.17), allowing us to create visualizations such as graphs and charts that helped us recognize emoji usage patterns. For qualitative data, we included short answer questions, as seen in the “short answer text” options of  Figure 2, allowing for individualized feedback. We then analyzed the information we received from these qualitative responses to pick out the themes and patterns we found amongst our participants. Utilizing Siever’s iconographetic communication model (2019), we analyzed both types of data with the objective of finding the differences in the way people use emojis to communicate meaning across different digital platforms.

Figure 2. Screenshots of our Google survey with qualitative and quantitative questions.

What We Found

Figure 3. Survey responses about emoji use and unspoken rules over text messaging.

Taking a look at the results of the survey, the data showed that the majority of participants agreed that they use emojis differently in when texting versus social media (Figure 3). One person said, “On social media I try to keep it more trim and professional, to an extent, whereas with texting I’m more casual, so I use more emojis.” The majority also agreed that there were some unspoken rules about how emojis should be used within text messaging. When asked what some of those rules might be, another person said “Using emojis to convey tone of voice or mood of the conversation (e.g., hey 👋 vs. hey 😏).” Further proving our hypothesis specific to text messaging habits, we saw that a total of 81.3% of participants use 1 or more emojis per text, 79.5% per Instagram post, and 70% per tweet on average. This proves our hypothesis that private platforms have more frequent usage than public ones.

Figure 4. Survey responses about emoji use and unspoken rules over Instagram.

As for the social media portion, users of Instagram and Twitter agreed once again that their emoji use differed compared to texting (Figures 4 and 5). Some of the Instagram respondents said: “I use emojis mainly for aesthetic purposes for Instagram” and “You’re more conscious of how everyone else will perceive what you post instead of just one person.” About one third of respondents agreed on the existence of rules for using emojis in Instagram captions. Responses about unspoken rules varied and were dependent on each individual’s personal preference and self-expression.

Figure 5. Survey responses about emoji use and unspoken rules over Twitter.

Similarly, many Twitter participants claimed to use emojis differently because within this platform, more people would be seeing their Tweets. However, almost half of the respondents did not believe there are unspoken rules for emoji usage within this platform (Figure 5). This may be due to the lack of respondents to the Twitter portion of the survey, which could have skewed our data. Nonetheless, this supports our idea that there is more structure and hidden rules to emoji usages within private communication than there is within public communication. Our hypothesis about aesthetic and decorative usage in public spheres was also proven correct. As seen in Figure 6, only 16.7% of participants said that they use decorative emojis in their texts, while Instagram and Twitter users use decorative emojis nearly twice as much (40.9% and 31.6%, respectively).

Figure 6. Survey responses regarding aesthetic/decorative use of emojis.To see which emojis are most common, we asked participants to list up to 10 of their most frequently used emojis. The laughing/crying emoji held the #1 spot across all three, but the rest of the rankings varied (Figure 7). This leads us to believe that the laughing/crying emoji expresses universal sentiments that are socially acceptable across all platforms, both public and private. Interestingly, the sideways glancing eyes emoji and fire emoji appeared in both Instagram and Twitter data, but not in the text messaging data. This implies the existence of standard forms for public platforms to express emotions specific to Twitter/Instagram culture.

Figure 7. Most frequently used emojis per platform.

This might have something to do with meme culture specifically, as these memes are often used to express sarcasm or a certain aesthetic online.

Figure 8. Left: Text containing example of propositional attitude. Right: Tweet containing example of decorative function.

Lastly, we asked one participant who uses Twitter, Instagram, and text to provide us with screenshots of her personal emoji usage. We took these and compared each example of emoji use with the functions on Siever’s model (Figure 1). On the left of Figure 8, the emojis convey emotion to complement the content of her text. This is likely propositional attitude under modal function because the emojis add comment and evaluation to her stat‍ement. Also in Figure 8, ✨ is used to add aesthetic/decoration to text (modal function). Figure 9 shows an example of the much less common referential function, where an emoji takes the place of an entire part. Here, 🍜 replaces the word “ramen” in the sentence.

Figure 9: Text containing example of referential function.


The results from our survey support our hypothesis partially. The findings demonstrate that there is a difference in emoji usage between public and private forms of communication, as we had originally predicted. However, we found that there are more unspoken rules and structure with the usage of emojis when they are being used through private communication such as text messaging. We believe this is because the personal nature of text messages makes the user feel pressured to put more thought into the messages their emojis might send. Interestingly, our participants also indicated that they are more comfortable using emojis through text messages since they are not out for the whole world to see; this explains the higher frequency of emoji usage through text. Therefore, it appears that users are more comfortable using more emojis over text once they make sure they are using them in a way that ensures their message will be construed correctly. In contrast, on more public platforms, users choose to use more general emojis for descriptive or aesthetic purposes, with less frequent usage overall.



Bell, A. (1984). Language style as audience design. Language in Society, 13(2), 145–204.

Ge, J., & Gretzel, U. (2018). Emoji rhetoric: a social media influencer perspective. Journal of Marketing Management, 34(15-16), 1272–1295.

Herk, G. V. (2018). What is sociolinguistics? Hoboken, NJ: John Wiley & Sons, Inc.

Likert, R. (1932). A technique for the measurement of attitudes. Archives of Psychology, 22(140), 5–55.

Siever, C. M. (2019). ‘Iconographetic Communication’ in Digital Media. Emoticons, Kaomoji, and Emoji, 127–147.

Vitelar, A. (2019). Like Me: Generation Z and the Use of Social Media for Personal Branding. Management Dynamics in the Knowledge Economy, 7(2), 257–268. doi: 10.25019/MDKE/7.2.07

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Love Language: A Sociolinguistic Study on Bilingual Couples Talk

Yiran Li, Ekeme Ekanem, Mary Youngblood, and Nguyenova Dieu Anh – Shelly

Code-switching, where more than one language is integrated into speech, is extremely common amongst bilingual and multilingual speakers. Unfortunately, code-switching is often viewed by society as lazy or unintelligent, creating a negative stigma around speakers of non-standard language, which are often minority groups. This research analyzes the functions and contexts in which bilingual couples code-switch, focusing on the effects, if any, of their language backgrounds. To study this, we gathered data from 90-Day Fiance, a reality show centered around long-distance relationships. We looked at confrontational discussions to find instances of code-switching, comparing couples with same and different language backgrounds. Our results demonstrate the functionality of code-switching as well as its place within couples speech and confrontation, as couples may use code-switching to express certain feelings or to establish a connection. This study helps not only to end the stigma around code-switching but may also provide insight into communication for couples as a tool to strengthen relationships.

Carolina finds another woman’s underwear in Fernando’s closet. She asks him in Spanish, “Whose are these?” and starts the next sentence in English, “Panties girl”, meaning some other woman’s underwear.


Code-switching is a linguistic phenomenon when a speaker switches from one language to another during a conversation. Specifically, we will focus on bilingual conversation. We will analyze two groups of bilingual couples: those who have similar language backgrounds, and those who have different language backgrounds communicating primarily in English as the dominant and common language between them.

The proposed outcome is that bilingual couples, with similar language backgrounds and English as their second language, are more likely to employ code-switching to convey a mood or perspective, while those with different language backgrounds are more likely to use code-switching as a communication strategy to build common understanding, such as borrowing (Van, 2012; Pietikainen, 2014).


Code-switching has been proposed as a way to establish an identity (Piller, 2002). For example, De Fina (2007) looked at groups of Italian American men to reveal their code-switching patterns. De Fina found that the linguistic behaviors of an individual often influence those around them, so it may be that one partner may be influenced by the code-switching patterns of their partner. This theory was supported by Code switching in Mixed Couples that Code-switching has a specific reason for it. One of the reasons of code according to the findings was politeness. One of the partners would often accommodate to the other by switching the language. Some Code switching happens because of solidarity to establish a closer relationship. Lastly code-switching also shows confirmation (Dumaning 2015). Pietikainen also mentioned that lexical gap might be the reason for code switching. When a partner cannot successfully recall or produce the English word, they will sometimes use their partner’s first language (L1) instead of their L1 to ensure their partner understands (Pietikainen 2014).

Yodanis et al. (2007) studied the effects of bilingualism on couples’ relationships and found that having different native languages did not create more communication problems in couples with the same native languages. This suggests that these couples work harder and more effectively on their communication. The article ELF Couples and Automatic Code-Switching focuses on ELF couples and how code-switching influence their communication. The more common languages the couples shared the more they code-switched during their communication. Pietikainen found out that code-switching is automatic and unconscious and concluded that code-switching is the result of ELF couples’ relaxed atmosphere in their lives (Pietikainen 2014).


The methods used in this experiment were transcription analysis taken from clips of the popular show, 90-Day Fiancé. 90-Day Fiancé is a reality show that features Americans and their partners through their journey through the K-1 visa process (fiancé visa). 90-Day Fiancé follows couples during their first 90 days living together before they will be required to either get married or head back to their home country. We examined several bilingual couples from the show within two groups: different language background and the same language background Carolina and Fernando, Chantel and Pedro, and Devan and Jihoon. Carolina is a L1 Spanish speaker and acquiring English as her L2; her fiancé, Fernando, is a heritage Spanish speaker and also has native proficiency in English. Chantel is an American L1 English speaker and is Spanish as an L2; her partner, Pedro, is a Dominican L1 Spanish speaker, and acquiring English as his L2. Both Chantel and Pedro are acquiring their L2 to be able to communicate with each other, however, their language backgrounds are different, as they were not exposed to learning their L2 until after their relationship began. Devan is an American L1 English speaker and Jihoon is a Korean L1 English speaker who speaks very little English.

The clips analyzed were taken from moments of conflict and confrontation between the couples. For example, we use a clip from featuring Carolina confronting Fernando about a pair of women’s underwear she found in Fernando’s closet. Once a clip was selected, the dialogue was recorded, and the code-switching was marked and translated. Once properly transcribed, the code-switching events were categorized and compared with the results from the other couples (Piller 2002).


We found that Carolina and Fernando were more likely to use code-switching as a way to convey their mood or perspective as well as stylistic purposes (such as comedy), when faced with confrontation. Their similar language backgrounds allow them to navigate communication in both languages while code-switching. Contrastingly, Devan and Jihoon used code-switching sparingly.

Devan:     Ok, get out.

Jihoon:    Yeah, sorry.

Jihoon:    피곤한다 피곤해

           Pigonhanda  pigonhae

           Frustrated  (very) frustrated.

In this exchange between Devan and Jihoon, the code-switching event happens after a long pause (about 2 seconds) after his initial phrase. Devan does not understand this utterance as she does not speak Korean and Jihoon knows this as he says it. It is not a continuation of the conversation he was having with Devan, but more of a mumbled expression to himself.

In Carolina and Fernando’s case, Carolina uses Spanish and English within the same phrases, as shown below.

Carolina: Fernando!

Carolina: What is this?

Fernando: uh?

Carolina: Sube. 

          Come up (the stairs)

Carolina: De quién son esos? Panties girl. De quién son?

          Whose are these?                Whose are (these)?

Fernando: Did you find ‘em in my closet?

Carolina: In your closet.

Fernando: I- That has nothing- That’s nothing from recent. That’s 

old, old. I’m serious.



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May I Speak Now? Examining Gendered Turn-Taking in Televised Debates

Daniel Li, Jennifer Moon, Ming Liang, Minh-Khoa Tran

The present text explores turn-taking by focusing on two prominent models which describe gender differences in communication — the dominance model and the difference model. The idea of deep interruptions is also employed in this study to better measure turn taking during interactions. We are focusing on inter-gender and same-gender interaction by observing talk show hosts engaging in turn-taking with their guests. Our goal is to determine whether or not there is a statistically significant difference between the number of interruptions utilized by men and women during debates in talk shows. Our empirical study analyzes the difference in the average number of interruptions per minute from men and women by using four thirty second clips. Overall, we found that men and women do not differ in terms of the frequency of interruptions but hosts tend to interrupt guests more. It was unexpected that without extensively interrupting, men still try to maintain power and discredit the female speaker through facial expressions or gestures.


A lively debate on television is always fun to spectate. When the discussion gets heated, it appears as if power shifts at a moments notice — whoever’s speaking at the time seems persuasive and commanding. Sometimes the structure starts to break down, and the speakers begin interrupting each other in a bid for more speaking time.

Research on turn-taking in conversations has been conducted before. In this blog post, we hope to contribute to the scientific literature on turn-taking by investigating what sort of impact gender may have on the dynamic between talk show hosts and their guests. We’re curious to find out whether or not there is a statistical difference between the frequency of interruptions in mixed gender debates when compared to single gender debates on live television. We are also interested in whether the host and guest relationship will have any impact on the frequency of interruptions and if this plays any role in the dynamic between men and women. 

Background Information

There have been numerous studies and established theories about the power dynamic between men and women, and how this manifests itself in spoken language. We expect that the frequency of interruption between men and women will be explained by one of the three major models.

In Deborah Tannen’s difference model, men and women are members of their own distinct culture. This separation between male and female culture means that women and men will take noticeably different approaches to verbal communication (Tannen 1990). These cultures aren’t intrinsically bad or good. Women’s culture isn’t inferior to men’s culture, and it follows that their speech isn’t inferior to men’s speech. Deborah Tannen suggests that the clash of disparate cultures poses problems for respectful and equal communication.

Robin Lakoff proposed a dominance model which asserts that men and women’s unequal places in society’s power structure results in necessarily different communication styles, including how people of different gender are expected to speak and are spoken of (Lakoff 1973). Lakoff suggests that real-world imbalances and inequities will be reflected by linguistic imbalances. These two models both suggest that women and men tend to have distinct speech style and the higher social status of men makes it more likely for men to interrupt in interactions than women.

A very crucial idea that will be utilized in our study is deep interruption, which is an interruption that starts “two or more syllables before or after the last syllable of a place where you’d expect to switch speakers” (West & Zimmerman 1983).  West and Zimmerman predict that men are more likely than women to employ deep interruptions. In other words, it is more likely that men would forcefully change the subject of discourse to their desired topic regardless of the previous speaker’s intention.

However, despite the fact that males have been hypothesized to employ interruptions for domination and control, James and Clarke had very contradictory findings; they found an absence of significant differences in the amount of interruptions between women and men (James & Clarke 1993). Therefore, our first hypothesis is that men and women will average a similar number of interruptions per minute towards the other speaker. We also predict that hosts will tend to interrupt more than guests because of power dynamics between hosts and guests.


To collect data on turn-taking, we first needed to choose clips of debates to analyze. To make sure that we cover each relevant permutation of genders and speakers, we found four video clips. We have two video clips where the genders are the same (man-man, woman-woman). Then we have two more video clips where the genders are different (man-woman, woman-man). We needed two separate gender video clips to see if their status as host or guest on the show would significantly change our results.

Once we gathered our clips, we transcribed a climactic point in the debate where we found the most interruptions occurring. For each clip, we transcribed 30 seconds of debate. We transcribed a video with a man host and man guest which included Piers Morgan and Ben Shapiro. For the woman host and the man guest, we chose a clip with Cathy Newman and Jordan Peterson. For the man host and woman guest, we chose a clip with Tucker Carlson and Cathy Areu. Finally, for the woman host and woman guest, we chose a clip with Poppy Harlow and Janet Porter.

To transcribe the various videos, we used a transcription method of conversation analysis that included turn-taking markers to clearly indicate the turn of the speaker. This also showed whether the host or guest interrupted each other and if the interruptions occurred, we could see at what point the interruption was made during the speaker’s turn. We were also able to see if men had indeed interrupted women more, or if the level of interruptions were similar from both sides. Through this transcription process, we counted token instances of deep interruptions that were made by each speaker. Once the analysis of the deep interruptions was completed, we were able to collect our metrics and form a conclusion based on the amount of deep interruptions that occurred.


To help visualize our results, we plotted interruptions onto a rough timeline. The reasoning for these visual aids was to help us in our qualitative analyses of the clips. The timeline of interruptions helps to capture the flow of conversation and which ideas the most fiery clashes were centered around. Placing them onto a linear timeline also made it clearer which side of the debate was responsible for most of the interruptions. Here are the visualizations for all four of the clips that we analyzed, were time flows from left to right and only the interruptions are marked on the timeline. We have collated our results from the collection of token instances of deep interruption in Table 1.1 and 1.2 that count up these instances of interruption as a function of interruptions per minute.

Figure 1: Rough Timeline of Interruptions in Video 1.

Figure 2: Rough Timeline of Interruptions in Video 2.

Figure 3: Rough Timeline of Interruptions in Video 3.

Figure 4: Rough Timeline of Interruptions in Video 4.

Table 1.1 Interruptions per minute from host to guest.

Table 2.2 Interruptions per minute from guest to host.





To summarize, men as hosts interrupted their man guests 8 times per minute and their woman guests 4 times per minute. Women interrupted their man guests 8 times per minute and their woman guests 6 times per minute. Men as guests interrupted their man hosts 2 times per minute and their woman hosts 4 times per minute and women interrupted their man hosts 2 times per minute and their woman hosts 4 times per minute.

Discussion and Conclusion

To touch once more on the focus of this study, we are primarily interested in two dimensions of power in a verbal exchange, the gendered differences between men’s and women’s place in society and social normative expectations, and the dynamic between a host and their guest specifically in the arena of debate talk show. Our predictions based on our original hypothesis following the seminal work of James and Clarke in 1993 stated that we were expecting to not find a significant difference between the rate of interruption from man to woman and woman to man. Our results from analysis of these four clips support this hypothesis, wherein as hosts, people interrupt their guests the same number of times per minute regardless of the guest or host’s gender. In fact, both men and women are less likely as hosts to interrupt their women guests than their man guests, specifically men interrupt their women guests half as often as they interrupt men. However, this is not to say that they were showing their women guests more respect by interrupting less often, rather in these situations the man host controlled the conversation with the woman guest nonverbally.

Qualitative analysis of the accompanying videos show that when a man host is not interrupting a woman verbally he is still displaying physical behaviors that express disbelief or disinterest, which still disrupts the flow of conversation albeit less explicitly. Further, as guests, both men and women interrupt their hosts at very similar rates as well. However, in this situation, both men and women as guests interrupt women hosts more often than they do man hosts (twice as often). To analyze this particular facet of the study we first have to discuss our two big foci of power — man/woman and host/guest. It appears that the more salient dimension of power when it comes to producing significant differences in the rate of interruption is actually host/guest dynamics. As hosts, both men and women produce four times as many interruptions per minute in same gendered interactions.

From here we turn back to our original finding that men and women interrupt at similar rates. While as hosts men and women interrupt their man guests more often than they do their woman guests. As guests they interrupt their woman hosts at twice the rate they do their men hosts.  If this host guest dynamic is the most important consideration, this statistic suggests that as the guest and weaker in the dynamic both women and men consider a woman host to hold less power over the course of the debate than a man host as evidenced by their greater willingness to interrupt their host when she is a woman. These observations are preliminary and speculative however and would require much more rigorous control over the interactions including but not limited to: combing through a much greater amount of raw data, controlling for having the same person acting as both guest and host, and developing better accounting methods for nonverbal interruptions.



Block, D. (2002). Language and Gender and SLA. Quaderns De Filologia: Estudis Lingüístics, VII, 49–73.

Hoey, E. M. & Kendrick, K. H. (2017). Conversation Analysis. In A. M. B. de Groot & P. Hagoort (Eds.), Research Methods in Psycholinguistics: A Practical Guide (pp. 151 -173). Wiley-Blackwell

James, D. & Clarke, S (1993). Women, Men, and Interruptions: A Critical Review. Critical Reviews of the Literature, 231-279.

Kendall, S., & Tannen, D. (2015). Gender and Language in the Workplace. Gender and Discourse Gender and Discourse, 81–105. doi: 10.4135/9781446250204.n5

Lakoff, R. (1973). Language and Woman’s Place. Language in Society, 2, 45–80.

Tannen, D. (2012). Turn-Taking and Intercultural Discourse and Communication. The Handbook of Intercultural Discourse and Communication, 135–157. doi: 10.1002/9781118247273.ch8

West, C., & Zimmerman, D. (1987). Doing Gender. Gender and Society, 1(2), 125-151. Retrieved from http://www.jstor.org/stable/189945

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Gen Z, Slang, and Stuff

Anonymous author, Daniela Vega, Giselle Chan,  Yuxiao Li

This study provides an analysis on the use of general extenders within Generation Z (Gen Z) online discourse. Utilizing qualitative analysis methods on social media dialogue (e.g. Youtube comments, Tweets, Spotify playlists, etc.) allows us to demonstrate how Gen Z members have created a new general extender (i.e. “and idk”). Where previous research studies on general extenders were narrowed to in-person discourse and interactions, this study examines the language pattern in the larger context of the internet across different social media discourse facilitators. It was a new context we were interested in providing research for because Gen Z is the first generation to grow up with the mass media culture, brought to them by the aforementioned social media outlets. Aptly so, Gen Z has created this new form of general extenders to expand their lexical inventory and engage in online discourse, as a pragmatic tool to index their emotions and stances. The interesting sociolinguistics findings on Gen Z and the use of general extenders are reflected on how this particular generation is constantly creating new slang terms (e.g. and idk), which builds intragenerational unity (with mutuals) but also causes intergenerational confusion (with the baby boomer generation referred to as the boomers); nonetheless, nuanced research is complicated with the lack of a corpus focusing on online discourse.


Slang fosters in-group relationships and creates a recognizable framework of social discourse structure to identify fellow group members with. Slang is known to change regularly, generation to generation, and trend to trend. Currently, technological advances have played a huge role in the development of new stylistics in language and the creation of new lexical items. It is a language phenomenon that has been studied to showcase sociolinguistics impacts. Here we are also looking for the sociolinguistic impact, but on the focus of general extenders found in the Generation Z (Gen Z) every-day internet discourse stylistics. The speaking style of our target population, Gen Z, employs Internet slang in computer-mediated discourse, especially through videos on social media websites like YouTube, Twitter, and TikTok. We are looking to examine how they utilize it with intergenerational and intragenerational group members. We are hoping to understand if it is used to separate themselves from other groups or if there is a new development in general extenders with the same functions as previous research has defined. 

Background Information

There is previous research on the text messaging stylistics of Gen Z. There is also research on previous generations creating new slang. We will be following up these research focuses with the use of a specific linguistic event, the general extender (GE). GEs are phrases added to the end of a sentence indicating the previous word is part of a set, extending its meaning. For example, when being asked, “What did you find at the tide pools?”, one could respond by saying, “I found starfish, sea urchins, sea anemones, and stuff.” The “and stuff” is the GE, signifying the previous items were part of a larger set, in this case, of sea creatures. Researchers Tagliamonte and Denis analyzed these types of phrases and verified them to have important functions in spoken language. Those functions include referring to a set of the previous word, creating vagueness, sending a signal of unsureness, or creating solidarity between speakers. GEs can also be used to mark the end of a speaker’s turn or indicate that the previous word could become a topic of the conversation.

Common GEs used by previous generations include “and stuff” and “and things.” The oldest uses of general extenders date back to the 14th century with the GE, “and such” (Tagliamonte/Denis). GE use and types further develop with new additions appearing in 1957 with “and shit”. This includes general extender particles like “and that kind of thing” (see Figure 1).  We saw no other data on recent GEs in the late twentieth century or the current twenty first century. We are interested to find any new GEs and if possible, how they are coined and popularized by Gen Z. If there is a continuance in use of GEs from a previous generation, we will be documenting occurrences. When referring to Gen Z, we refer to individuals who are currently between 4 and 24 years old, born between 1995 and 2015. Gen Z constitutes an estimated 70 million of the population of the United States as of 2019. In looking at their screen time behavior, Gen Z watch about 68 videos per day across 5 social media platforms, including Snapchat, TikTok, Twitter, Facebook, and YouTube. This social behavior is new because of the invention of technology but does relate to multiple age groups. Gen Z, millennials (ages 18-34), and Generation Xers (ages 35-54) use social media more than baby boomers (ages 55 or more) (see Figure 2.). However, social media use is the highest in Gen Z than in other generations. This gives them the highest opportunity to host dialogue between themselves on Twitter, Facebook, YouTube, and TikTok.

Figure 1


Figure 2


Using the qualitative method, Conversational Analysis (CA) for this study, we observe how Gen Z uses slang and GEs in their online conversations. Applied frequently in Sociology and Sociolinguistics to study social interactions, CA allowed us to analyze, compare, and document instances of GEs. The data used in our transcribed CAs were collected mostly from YouTube and Twitter, which have the highest Gen Z activity. Twitter caused difficulties because without category subjects to search for to find GE use, we were forced to scroll and read many tweets, hoping for an occurrence of a GE. We were able to search for the keywords of GEs like “and stuff”, which led to some results. We watched the most viewed YouTube videos from the popular Gen Z YouTube personalities (YouTubers) like James Charles and Kylie Jenner to begin a lexical choice analysis. When identifying the new GE, we returned to both Twitter and YouTube to search the phrase to find more instances and examples that supported our evaluation.


We continuously saw a continued use of GEs including “and shit”, “and stuff, “and things”, and “or whatever”. These are continued in use from even the early 1600s. We did not witness an “and so forth” from the 1500s in a Tweet, YouTube video, YouTube comment, or TikTok made by a Gen Z community member or influential personality. We conclude because online discourse is not formal, the use of a Shakespearean aged GE would not be expected.  These previously identified GEs are not used to separate themselves from a generation but as a normal stylistic feature all generations use. We found no unique change in use for already established and identified GEs.

Through our CA data collection efforts we have successfully identified a new GE, “and idk”. This new GE is a fascinating finding sociolinguistically because it is a hybrid of already established linguistic phenomena and recently developed Internet-related acronyms. In looking at the function of “and idk,” we see it follows the same patterns of previous GEs. It continues to successfully indicate that words in the clause are part of larger set.

However, “and idk” deviates in the sense that it encodes pragmatic meaning. It contributes sentiments of vulnerability, insecurity and disconnect (see Figure 3.) In this example of “and idk”, we see it is identifying the users’ response to her lack of Twitter followers to interact with. Overall, our results serve as an expansion on previous research for GE, and we ultimately want more linguists to join in on this conversation of navigating the sociolinguistic landscape of the Internet to gain a more nuanced understanding of Gen Z-related discourse.

Figure 3. Screenshots of one of our tweets.

Discussion & Conclusion

We found GE uses allows any generation to be identified as the current younger generation because they are more typically found in informal speech. Social media discourse has allowed for changes in communication to facilitate the speed of communication speed online. While the use of the same tone you would have with friends and your in-group is also preferred because of the opportunity of anonymity and profile curation online. The research on general extenders does not include the increase, decrease, or appearance of usage at certain ages. However, we speculate if there should be research done on this, there could be identified a transition period between ages where GE use appears. We expect the GEs we identified to be added to a timeline representing its introduction, like the one we included in our research. We would hope there could be a timeline more specific for each GE within every time period, such as our focus here on Gen Z and social media. We would be interested in seeing the peaks and heights of use within the generation and the time periods where the GEs manifest. Thanks to archiving efforts for internet dialogue such as the Library of Congress Twitter Archive, the availability of data has increased. This type of data collection will allow for even further detailed research and we expect further sociolinguistic analysis.



A.O., Abusa’Aleek. (2015). Internet Linguistics: A Linguistic Analysis of Electronic Discourse as a New Variety of Language. International Journal of English Linguistics. 5.10.5539/ijel.v5n1p135.

Cheshire, J. (2007, April 17). Discourse variation, grammaticalisation and stuff like that. Retrieved from https://onlinelibrary.wiley.com/doi/10.1111/j.1467-9841.2007.00317.x.

Cox, Toby. (2019, July 2). How Different Generations Use Social Media. from The State of Tech. Retrieved from https://themanifest.com/social-media/how-different-generations-use-social-media

D.W. Maurer. (2013, August 16). Slang. Encyclopædia Britannica, inc. Retrieved from https://www.britann ica.com/topic/slang

Levey, & Stephen. (2012, March 1). General Extenders and Grammaticalization: Insights from London Preadolescents. Retrieved from https://academic.oup.com/applij/article/33/3/257/220662.

Sue. (1970, January 1). Young people’s language and stuff like that. Retrieved from http://linguistics-research-digest.blogspot.com/2011/10/young-peoples-language-and-stuff-like.html.

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Bilingualism in TV: When and why does code-switching happen?

Zoe Willoughby, Anton Nogin, Isaiah Sandoval, Maria Becerra

As bilingualism becomes increasingly prevalent in a wider variety of television shows, sociolinguistic analysis of what code-switching entails and why it is used becomes even more important to look at. We delve into an analysis of the shows Dora the Explorer and One Day at a Time to explore what types of code-switching are used for audiences of different ages. We hypothesized each show would differ in its most frequent type of code-switching – metaphorical or situational – because of the different language complexity levels depending on each intended age group. However, we realized these labels may not be as clear as expected. As we analyzed the data, some instances could fit under both of those categories or did not fit under either. Since the language use was more complex in One Day at a Time, so was the categorization of the reasons why code-switching was used. We ultimately determined cut-and-dry labels such as “situational” and “metaphorical” are not sufficient enough to classify why people code-switch. In order to recognize code-switching as a tool used to demonstrate language mastery and not convenience, our analysis of the results looks to offer possible solutions to further classify these instances of code-switching in TV shows.

What’s this all about?

Along with the growing bilingual population in the United States, there has been a shift in the way bilingualism is represented in the media, especially in television shows (Grosjean 2018). In this study, we wanted to look at the layers of language complexity of bilingual speakers through bilingual shows. Given bilinguals might have another layer to add to the technicality of their speech – code-switching – it is important to see how the media portrays that. Code-switching itself has layers of complexity when it comes to explaining why a speaker might do so.

Bilingual shows are becoming more mainstream for viewers of a wider age range, producing shows such as Dora the Explorer (DE) and One Day at a Time (ODT), the two shows we focus on in this study. We used these two shows as a lens through which we viewed the reasoning behind why bilinguals might use code-switching and the context in which these switches occur. By distinguishing between the two intended age groups for each show, we decided to focus on the different types of code-switching that may be more specific to each age group: metaphorical (using code-switching as a resource to enhance meaning) and situational (using code-switching depending on topic or other speaker) (Van Herk 2018, pgs. 151-152).

We hypothesized situational code-switching would be more frequent in DE, the show for younger audiences, and metaphorical code-switching would be more frequent in ODT, the show for older audiences. After analysis, we proved this true but found that classifying reasons for code-switching would require more categories to encompass its complexity.

What’s the context?

Our research focuses on bilingual television shows, specifically ones whose characters code-switch (CS) between Spanish and English. Despite incorrect assumptions that bilingualism always means total proficiency in two languages, this neglects to acknowledge the complexity of language use for those who are multilingual (Grosjean 1994). This definition is unclear, but CS helps to show bilingualism and is indicative of a speaker’s command over the language they are speaking (Bullock & Toribio 2009).

The types of code-switching used in these shows are our central linguistic variables. Metaphorical CS is a resource used to supplement the meaning of a certain word or phrase by tapping into the associations with a certain language (Woolard 2004). Situational CS occurs when a speaker uses a different language for a certain conversation topic or with a certain speaker (Gumperz 1977). Situational CS tends to occur intersentential (within a sentence) and metaphorical CS tends to occur intrasentential (over multiple sentences), and since intrasentential CS is linked with a better mastery of a language, metaphorical CS is implied to be a sign of that as well (Bullock & Toribio 2009).

How did we go about it?          

In the first three episodes of each show’s first season, we recorded each instance in which the speakers switched from English, the main language of each show, to Spanish, the secondary language. Each show has a different age recommendation, and Common Sense Media, an advocacy group that reviews the appropriateness of media for families and children, stated that Dora the Explorer (DE) is intended for audiences ages three and up, and One Day at a Time (ODT) is intended for audiences ages 12 and up (Herman 2004, Slaton 2016).

Our first step in collecting the data was to keep a running list of each instance of code-switching that occurred in each show. Along with the timestamp of the occurrence, we also included a brief description of the context in which we found the example. Once each instance was recorded from all of the episodes, we created six categories that grouped the different contexts in which CS happened. These contexts included insults, names (character called by relationship name), emotions, imitation (copying what someone else said in a different manner), commands, and labeling (calling certain things with their corresponding names in one language).

Originally, we planned to use these contexts to help us divide the CS occurrences into situational and metaphorical explanations, but this proved much more complex than expected. In order to compensate for the lack of coverage the explanations of situational and metaphorical CS gave, we created additional categorizations, which included lexical gaps (when speakers use words in one language that cannot be directly translated with the same weight), overlaps (combine multiple explanations), and exceptions (where CS did not serve as significant of a sociolinguistic purpose).

What did we find?

As we had predicted, there were more instances of situational CS in DE than there were in ODT, and there were more instances of metaphorical CS in ODT than there were in DE. For example, in ODT episode two (18:05 – 18:20), Penelope (in bold) is talking to her coworkers and boss in the office (none speak Spanish):

But maybe you didn’t hear because you were on your phone like now  
Uh, what?  

Yo voy a matar a este hombre    [I am going to kill this man]

Huh, what does that mean?

I am just thinking about lunch.

In this example, Penelope is angry at the situation at her work and chooses to switch to Spanish to express her anger. This is a metaphorical instance because Penelope is trying to create distance between her and her coworkers.

On the other hand, DE provides clear instances of situational CS. For example, in episode two, when Dora met with Baby Blue Bird (who is monolingual), she has to switch over to Spanish to be able to communicate with him (Dora in bold):

Where do you live baby bird?

¿Que?              [What?]

Oh, Baby Bluebird speaks Spanish         (at the audience)  

¿Donde vives?      [Where do you live?]

This example is situational CS because one of the speakers is only able to understand Spanish. Therefore, the situation calls for Dora to switch over to Spanish to be able to communicate.

Our hypothesis was based on the fact that DE would have less complex language usage and that ODT would have more complex language usage because of their respective intended audiences. This observation was confirmed when collecting data. We have summarized the contexts of CS for each of the shows per episode in Table 1.1 and Table 1.2 below.

Table 1.1 Contexts of CS in ODT

Table 1.2 Contexts of CS in DE

From observing the contexts of CS for both shows, it is noticeable that ODT has more complexity in CS than DE. Because of the lower level of language complexity, DE has no instances of insults, emotions or imitations, meaning that classifying the reasons for CS for ODT was not as clear as for DE. In some cases, a given instance of CS did not fall neatly into metaphorical or into situational. This is when we resorted to our additional categories of why a speaker might code-switch: lexical gap, an overlap, or not clear enough for any category (exception). For example, in ODT episode one, the grandma says:

You need to do something about this little sinvergüenza

[You need to do something about this little ___________]

In this case, the word “sinvergüenza,” refers to a person who is not ashamed of doing something that is seen as shameful. However, there is no direct translation in English that has the same meaning, and therefore we have labeled it as a lexical gap.

Another example of the categories we have created is in ODT episode three when the grandma says:

She thinks she’s tan fancy because her four grandsons are altar boys. Guataca.

[She thinks she’s so fancy because her four grandsons are altar boys. _______]

Here, the grandma is using an insult in Spanish. This word has no direct translation to English so we have labeled it as a lexical gap. However, it is also an instance where the speaker (the grandma) is trying to create distance between her and the person she is insulting, therefore it is metaphorical CS. Since this CS instance has aspects of both lexical gaps and metaphorical CS, we labeled it an overlap.

Sometimes we couldn’t fit the CS instances into any of the categories because there seemed to be no significant sociolinguistic purpose. An example of this is when Dora repeats a word in Spanish and English a few times to teach a new word, which we labeled as repetition. Another instance is mixing, which is done often by the grandma in ODT. To clarify, we defined mixing to be when a word is used in a different language (usually intrasentential) but can be directly translated into English; CS might have happened just out of convenience. For example, in one episode, the grandma says “café con leche” which translates directly to “coffee with milk.” Also, when calling someone by their relationship name in Spanish, such as “mami” or “abuelita,” there is little to no sociolinguistic meaning behind it (we labeled this naming). Imagine this: If CS did not happen in any of these examples, would it make a sociolinguistic difference? In these cases, the speakers chose to CS for a reason that can’t be explained by sociolinguistics. In other words, the sociolinguistic difference was not enough to warrant another category.

What does this all mean?

If we only look at the scope of our hypothesis, then we proved the main points: There is more metaphorical CS in ODT, and more situational CS in DE, both suitable for each show’s recommended age group. However, the hypothesis itself did not allow us to take into account the other types of language use related to CS that came up in data collection and analysis. In every instance, whether it be a simple word or phrase repetition in DE, or more dynamic instances of CS in ODT, the words or phrases from Spanish utilized in English (or vice versa) were grammatically sound, even if they had no direct equivalent in the other language. This phenomenon shows that bilingual individuals who utilize CS are, at the very least, capable in both languages. Characters in both shows were able to switch seamlessly from one language to the other, maintaining grammaticality and conversation flow at the same time. What this means is that depending on a speaker’s frequency of CS usage, CS itself could act as a marker of high proficiency in, or even mastery of two languages.

Our hypothesis and initial conclusion stemmed from the narrow definitions of CS provided in our class textbook. The textbook provided context and examples for how metaphorical or situational CS each functioned in an interaction. However, it presumes these categories are limited by where in the phrase or sentence the CS instance occurs. The book defines situational CS as only occurring from one sentence to the other, and metaphorical CS as occurring within a single sentence (Van Herk 2018, pg. 151-152). Although the book connects language proficiency and emotional states to CS usage, it uses narrow descriptions for the phenomenon itself.

The textbook also assumes metaphorical and situational CS are the only two categories of CS worth noting. While the textbook works to dispel language myths by defining the sociolinguistic reasons for CS, it could confuse readers that there might only be two main reasons, just like it did for us. To that end, the exceptions and overlaps from our data are defined as such because we couldn’t place them in either category. It’s up to readers like you to go back and define these instances however you see fit.

What’s next?

Since there is a wide variety of other bilingual media out there (reality TV, movies, podcasts, etc.), more data could be collected to find different examples of diverse bilingual speech patterns. We could even compare media across time periods to measure frequencies of CS across several years or decades. This all ties back into one observation – that bilingual individuals are being engaged with in mass media and are being positively validated.



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Grosjean F. (2018). Psychology Today, The Amazing Rise of Bilingualism in the United States. https://www.psychologytoday.com/us/blog/life-bilingual/201809/the-amazing-rise-bilingualism-in-the-united-states.

Grosjean, F. (1994). Individual bilingualism. The encyclopedia of language and linguistics, 3, 1656-1660. http://www.signwriting.org/forums/swlist/archive2/message/6760/Indiv%20bilm.rtf.

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Herman, J. (2004, December 14). Dora the Explorer – TV Review. Retrieved from https://www.commonsensemedia.org/tv-reviews/dora-the-explorer.

Slaton, J. (2016, December 19). One Day at a Time – TV Review. Retrieved from https://www.commonsensemedia.org/tv-reviews/one-day-at-a-time-0.

Van Herk, G. (2018). Multilingualism. In Gerard Van Herk (Ed.): What is sociolinguistics? (2nd edition: pp. 146 – 157). Hoboken, NJ: John Wiley & Sons, Inc.

Van Herk, G. (2018). Glossary. In Gerard Van Herk (Ed.): What is sociolinguistics? (2nd edition: pp. 220 – 237). Hoboken, NJ: John Wiley & Sons, Inc.

Woolard, K.A. (2004). Codeswitching. In A. Duranti (Eds). A Companion to Linguistic Anthropology (pp.73-94). Oxford, Blackwell. https://onlinelibrary.wiley.com/doi/pdf/10.1002/9780470996522#page=92.

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Dorothy wants to know: How can television influence language development?

Looking at Child-Directed Speech on Sesame Street

Tania Aguilar, Alex Ferguson, Thomas Gerard, Matthew Pham

In today’s highly advanced and technological world, access to media through the Internet is not a challenge for any age group. Most children have their eyes constantly glued to a screen, whether that be a television, tablet, computer, or mobile device. Children are able to view their favorite television shows at any time of the day on their preferred social media platform. The days of having to sit in front of the t.v. at a specific time or using a VCR to record missed shows are a thing of the past. However, does an increase in accessibility and viewing time correlate to cognitive and language development among youths? This pilot study dissects episodes from the well-loved television show, Sesame Street, to analyze 9 features of child-directed speech strategically placed in the show to incite learning. Our data and results exhibit extensive use of Child-Directed Speech strategies to maintain (i) child engagement, and (ii) nurture child language development, all while avoiding certain features that diverge from the acquisition of Standard American English. This study examines the effectiveness of techniques and tactics employed by Sesame Street so that future research may compare other children’s television shows such as Blue’s Clues, Clifford the Big Red Dog, Arthur, and Dragon Tales to further explore the influence in language acquisition and development.


Children watch more television than ever. With new reports suggesting that children ages 2-5 spend up to 32 hours a week in front of a screen (Debczak, 2019), it only makes sense that television would have some effect on language development for these young minds. The “Peppa Pig Effect” recently made news when American parents found their children who watch the show speaking with a slight British accent and using certain British English terms (Debczak 2019). Sociolinguistically, as we are building our own identity, we are often influenced by the people around us. Ultimately, we learn how to express ourselves in order to fit in with those we like and aspire to be like (Podesva, 2011). The children watching television do the same by emulating the speech of characters from the shows they often enjoy, like Peppa Pig.

Figure 1: Peppa Pig phenomenon

See also: “Peppa Pig Is Corrupting America’s Youth” jokingly rants Stephen Colbert

While television is often cast in a negative light in child development, television may actually present a good opportunity to play a positive role in a child’s language development– particularly through a program’s use of Child-Directed Speech paired with high-quality nurturing language-development activities. Since television is a presence in many children’s daily lives, it is essential that we explore the qualities of a well-done implementation of Child-Directed Speech (CDS) in children’s television to simultaneously engage the viewer and encourage language development. By evaluating Sesame Streets use of Child-Directed Speech, we hope to find out whether the show exhibits the traits of a program that would be successful in aiding child language development.

Background Information

Created in 1969 by former documentarian Joan Ganz Cooney, Sesame Street was intended to be not only entertaining but educational as a children’s show. The show was created with the aim of helping lower-income 3-5 year-olds without access to preschool prepare for kindergarten, with the hope that the show’s bright colors and catchy songs would help viewers ultimately advance their speech and language (“Sesame Street” Debuts, 2009). The years leading up to a child’s beginning of school is a critical part of their language development. A child is developing in response to various stimuli (American Speech-Language-Hearing Association). This 5-year span results in varied experiences and therefore variations in the quality of language exposure, which in turn affects pronunciation, communication, and comprehension abilities (American Speech-Language-Hearing Association).

See also: Is my child on track linguistically? Take this quiz from the NIH!

The use of Child-Directed Speech is shown to aid in bonding with parents or caregivers, but also in language development in infants and toddlers (Green et al., 2010). CDS is a nonstandard form of natural speech when talking to infants or toddlers, is often slower, with more pauses and repetition, higher in pitch, more melodic and exaggerated, and may use simpler words than found in generic adult speech (Grin, 2016). Child Directed Speech also contributes to vocabulary development– recent studies have pinpointed key elements of CDS that are more likely to influence children’s word learning abilities: the use of the same words in adjacent sentences, the use of isolated words, and discourse continuity (Schachner & Hannon 2011).


In order to explore whether Sesame Street specifically exhibits positive language development traits through its Child-Directed Speech, we analyzed two selected episodes of Sesame Street: “Elmo’s Pretend School” (2006) and “Earth Day” (2019). These recent episodes have carried the traditional educational style of Sesame Street, therefore exemplifying ideologies and methodologies Sesame Street values in its teachings. We specifically analyzed conversations in lesson-based contexts and recorded the methods used to teach that lesson with a focus on 9 features of CDS: diminutives, 3rd person usage, simplified vocabulary, questions, exaggerated intonation, repetition, clear turn-taking, slowed pacing, and music. Most data was collected through simple manual transcription, however for certain features we recorded exchanges with formal conversation analysis techniques as well as the acoustic software Praat.

We aim to understand how Sesame Street uses Children-Directed Speech to keep children engaged and how the quality of it can possibly affect children’s speech as well. In order to measure the quality of CDS, we analyzed based on the methods used in “Specific Structural Features of Child-Directed Speech Support Young Children’s Word Learning,” by Jessica Feigenbaum Schwab. We focused on the prosody of CDS to examine how it engages the viewers while also looking out for repetition, slowed pacing, turn-taking and more to determine the quality of CDS. Sesame Street was created with the intention to help children develop communication skills, so we expect to find many high-quality instances of Child-Directed Speech– meaning a prominent use of CDS with high frequency of the foci listed above.


After collecting our data, we were initially surprised to find that not all 9 aspects of CDS were present in our excerpts of Sesame Street. We’ll break down each one here:

Diminutives are words that are changed or affixed to make them appear small, cute, or reduce their meaning, or to put that in diminutives, appear “itsy-bitsy,” “cutesy,” and “meaningful-ish” (Merriam Webster, 2019). Despite being very common in parent-child speech, we observed that Sesame Street characters use no diminutives, likely because the writers would not want to introduce children to words outside of the Standard American English lexicon.

The use of the 3rd Person is marked when a speaker refers to themselves without personal pronouns like “I” and “me”. Sesame Street uses the 3rd person fairly often, especially with their star character Elmo who exclusively refers to himself in the 3rd person. You’ll never catch Elmo saying the pronoun “I,” though many other puppets might.

Figure 2: Transcript of conversation between 2 well-known Sesame Street characters, Elmo and Big Bird.







Speakers using Exaggerated Intonation will raise and lower the pitch of their voice greatly within a single sentence to emphasize keywords. In Figure 3, we have an example spectrogram of Grover asking the rhetorical question: “Did you know that in some places, children ride camels to school?” The blue line shows his wild variation in pitch!

Figure 3: Spectrogram of phrase “Did you know that in some places, children ride camels to school?” Audio file attached below.

Most CDS uses very Simplified Vocabulary, but in our observations, Sesame Street doesn’t shy away from using some pretty hefty words! We caught Grover using words that most adults would rarely use including “plethora,” “myriad,” and “kit and caboodle.”

Repetition is incredibly common in CDS and in Sesame Street. In the show, we observed puppets overwhelmingly repeating or over contextualizing new words to help viewers learn their meaning. The video below showcases the introduction of new vocabulary, stethoscope, via repetition.

With CDS, speakers will often try to engage the listener with Questions. Big Bird likes to ask the viewer questions to invite them to practice their speaking skills.

Conversations will often adopt Slowed Pacing between adults and children so that children can clearly hear the boundaries between different sounds, words, and phrases. When introducing new vocabulary, Elmo, for example, likes to sound words out: It’s not just a “pediatrician”; it’s a “pe-di-a-trician” (see the video clip below)! Some characters, however, talk quicker than even most adults.

In every conversation, the roles of speaker and listener will switch off in what we call Turn-Taking. Sesame Street characters will have extra long pauses between utterances to give other puppets or the viewer time to respond, just like CDS use would predict!

Finally, Music and rhythmic speech are other clear ways that CDS tries to get the attention of children and also acts as a way of creating more solid memories. In Sesame Street, it’s rare to get through an entire segment without a puppet bursting into a musical number about the lesson they just learned.

Out of all 9 of these CDS features, diminutives and simplified vocabulary are the only ones we never found continued evidence of. Furthermore, the use of the 3rd person and slowed pacing were only consistent features for a select population of muppet characters. Otherwise, the other 5 features are heavily employed throughout the show to help foster an engaging language-learning experience.

Figure 5: Overview of 9 child-directed speech features employed in Sesame Street.


Discussion and Conclusions

Looking at an overview of the observations, we were able to find a general pattern that could explain why certain features were or weren’t used. Since the primary goal of Sesame Street is to prepare children for kindergarten, we found that the show often sacrifices certain CDS features that could possibly hinder a child’s development of standard language conventions used in school. Diminutives are out because if children use these very personal words in school, their teachers and peers may not understand what they’re referencing. Not using an overly simplified vocabulary also exposes children to situations where they don’t know what a particular word means, something that often happens in early schooling. The techniques Sesame Street does use tend to be more about supporting understanding of new concepts by making speech extra clear. The use of CDS is able to maintain a child’s engagement while also exemplifying nurturing language development activities, therefore we expect the show to have a positive impact on children’s language development in this context.

As for further use of our research, we plan to conduct similar analyses on similar television programs including Blue’s Clues, Clifford the Big Red Dog, Arthur, and Dragon Tales to establish a comparison matrix. By investigating the varying uses of Child-Directed Speech in each, we hope to find which television shows do a good job of fostering language development…and which ones fall flat.

See also: Ready to read? 11 shows to improve your child’s literacy. You’ll never guess what #7 is!



American Speech-Language-Hearing Association. (n.d.). Speech, Language, and Hearing Services for Children: A Smart Investment. Retrieved from https://www.asha.org/uploadedFiles/Speech-Language-Hearing-Services-for-Children.pdf

Debczak, M. (2019, February 13). The Peppa Effect: U.S. Kids Who Watch Peppa Pig Are Developing Slight British Accents. Retrieved from https://www.mentalfloss.com/article/574050/peppa-effect-kids-developing-british-accents-peppa-pig.

Diminutive. In The Merriam-Webster.com Dictionary. Retrieved December 9, 2019, from https://www.merriam-webster.com/dictionary/diminutive

Ferguson, A., Pham, M., Gerard, T., & Aguilar, T. (2019). PROJECT PROPOSAL.

Green, J. R., Nip, I. S. B., Wilson, E. M., Mefferd, A., & Yunusova, Y. (2010). LIP MOVEMENT EXAGGERATIONS DURING INFANT DIRECTED SPEECH. Special Education and Communication Disorders Faculty Publications. Retrieved from http://digitalcommons.unl.edu/cgi/viewcontent.cgi?article=1053&context=specedfacpub

GRIN Verlag. (2016). Child-directed speech and its role in language acquisition. Munich.

McDonough P. TV viewing among kids at an eight-year high. Nielsenwire. October 26, 2009. Available at: http://blog.nielsen.com/nielsenwire/media_entertainment/tv-viewing-among-kids-at-an-eight-year-high/. Accessed 11 November 2009.

Podesva, R. J. (2011). The California Vowel Shift And Gay Identity. American Speech, 86(1), 32–51. doi: 10.1215/00031283-1277501

Schachner, A., & Hannon, E. E. (2011). Infant-directed speech drives social preferences in 5-month-old infants. Developmental Psychology, 47(1), 19–25. https://doi.org/10.1037/a0020740

Schwab, J. F. (2018). Specific structural features of child-directed speech support young children’s word learning (Order No. 10813697). Available from ProQuest Dissertations & Theses A&I; ProQuest Dissertations & Theses Global. (2057297773). Retrieved from https://search.proquest.com/docview/2057297773?accountid=14512

“Sesame Street” Debuts. (2009, November 24). Retrieved from https://www.history.com/this-day-in-history/sesame-street-debuts.

Speech and Language Services in Schools. (n.d.). Retrieved from https://www.asha.org/public/speech/development/Speech-and-Language-Services-in-Schools/.

ROWE, M. (2008). Child-directed speech: Relation to socioeconomic status, knowledge of child development and child vocabulary skill. Journal of Child Language, 35(1), 185-205. doi:10.1017/S0305000907008343

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Okay So…Vloggers You Know?

James Beasley, Mahta Marefat, Betsy Wo

The present article focuses on identifying how YouTube content creators shape their material and influence viewers’ language through storytelling. The evident popularity of YouTube among younger generations leads to the hypothesis that linguistic variants displayed by content creators subtly influence the conversation styles of young adults. This study was designed to gauge the correlations between YouTube viewing, storytelling frequency and variant usage among young generations through survey responses. The survey sample consisted entirely of UCLA students, who indicated high amounts of YouTube viewing and storytelling. Additionally, respondent data showed that many linguistic variants used by vloggers are also commonly used by respondents. Previous research on the impact and practices of vloggers also align with our results. The takeaways from our results suggest that the prevalence of YouTube viewing and personal storytelling among respondents are similar. Furthermore, the linguistic variants used by respondents match those used by YouTube vloggers, implying a subtle influence of vlogger language practices on viewer variant usage.

The Innovative Internet

The world we exist in is dynamic. Constantly shifting and advancing, everything observed in our lives reflects the rapidity with which information is transmitted and trends rise to popularity. The internet is a large contributor to this speed, and allows for instant communication and advances that have enabled the growth of Western society. 

Since its inception, the internet has marked itself as a necessity — vast, collaborative, and informative — it is able to interweave itself into our daily lives. It has paved the way for innovative forms of communication, networking, and socialization; social media as an entity exists solely due to the impact and presence of the internet. 

One such social media platform that has gained enormous popularity is YouTube, a web-based service that allows users to both create and view videos online. If you have access to the internet, you have access to the billions of videos that are housed on YouTube. Viewers are able to utilize this in a plethora of capacities, whether that be for educational, motivational, or entertainment purposes (and these are only three basic categories in which people are able to interact with these videos). Content creators (people who record, edit, and post videos) are able to construct YouTube Channels, which act as a centralized location for all of their videos. They are able to design their own aesthetics and target specific audiences and populations through the videos they post. While there are many facets to creating popular content (performing certain acts, including other YouTubers in videos, interacting with viewers and outsiders, making specific types of videos such as lifestyle or beauty), it is through utilizing specific language and editing styles that these creators are able to attract their audiences and impact viewers’ lives. 

What do we know about vlogs?

To narrow down our investigation, we decided to focus on one video form: the vlog. While there are at least a dozen genres that exist on YouTube, we anticipated that nearly every college-aged student would have had exposure to vlogs in some way or another. 

First, we started off by finding a basic definition of vlogs, which we found in a study by Heather Molyneaux, Susan O’Donell and Kerri Gibson (2009):

“‘Vlogs,’ also known as video blogs, are video web logs[… ]Vlogs are a form of online publishing, allowing everyone with web access[…] to create and post content. Most vlogs are authored by individuals and focus on personal themes.”

This further proves our idea that multitudes of youth have connections to these types of videos, as they are created with the intention of fostering personal connections and sharing intimate details of a vlogger’s life. 

Yet settling on this genre of video is not narrow enough in its scope to truly observe any sociolinguistic phenomena. To rectify this, we decided to specify a target population — to identify not only a type of vlog, but also a similar group of vloggers. 

It is evident that YouTube is a social media platform where hundreds of millions of videos are posted telling or portraying the image of women (Molyneaux et al. 2009). Vlogging videos are not only showcasing women, but also influencing the women watching these videos. When focusing on the context of storytelling, there are certain methods vloggers use to keep their audience interested. In scientific terms, vloggers use a participation framework and language that reflects audience design through things like terms of address, questions, and directed language (Frobenius 2014). This basically means that vloggers produce their speech to create an environment where the viewers aren’t actually speaking but are still a part of the conversation. In a sense, there is an audience involvement resembling a face-to-face conversation, and more specifically, a storytelling conversation. When a vlogger tells a story, there is a lack of turn taking since the viewer isn’t actually able to reply, which is what frames the way vloggers speak (Frobenius 2014). If a viewer frequents a certain vlogger’s video, then a community-based knowledge system is formed, which results in a mutual understanding on many topics. In this situation, the fanbase becomes acclimated to the vlogger’s language variants, and may adapt these variants into their own speech. This is how we came to the conclusion that the linguistic variants displayed by these content creators must influence the conversation styles of young adults. Our study will help reveal the impact vloggers (strangers who feel like virtual friends) have on their audience. We were most interested in observing the speech trends in the young women of today. To do this, we needed to evaluate the effects the internet has on not only stylistic and linguistic changes among a specific population, including its reach, speed, and popularity, but also on the use of these variants in people’s lives outside of the internet.

How did we do it?

We took inspiration from an interesting prior study – a content analysis of vloggers, and audience response to these vloggers (Molyneaux et al. 2009). In this study, the subjects, who were all in their teens to 20s, were given a demographic background check and shown a few vlogs. A survey was then conducted to investigate insight into the influence of gender. We employed their survey method to collect and analyze data on the variable of speech variants. Our survey was distributed it to our peers online (through email, Facebook, and other social media), polling students on their personal demographic information as well as their entertainment preferences online. We also provided key phrases that exemplified the linguistic variants we investigated and asked that they self-report how they first came across the phrases, and how frequently they estimate their use weekly. In order to make our survey interesting, the second portion included fun stills and clips of the variants we were investigating in order to ensure the self-reported data was an accurate reflection of the variants (this way participants would be aware of which motions and phrases we were explicitly asking about, and could then compare to their own behaviors). We expected that the responses to the second part of our survey would garner more explicit and clear results exemplifying the variants in question. Here are some of the variants that we observed in vloggers, and used in our survey:

    1. Okay so…

    1. You know?

    1. Hand gestures

    1. The ‘welp’ expression

    1. Repeating words

    1. Pause and awkward smile 

What did we find?

Utilizing the data from our survey, we were able to generate the following figures that summarize results to key questions. As seen in Figure 1, all participants indicated they tell stories at least once a week, with over 60% reporting casual story-telling on a daily basis. This information is useful as it ensures the data we collected is actually applicable to our study. 

Additionally, Figure 2 also helps validate our results as it shows that all participants watch YouTube videos at least once a week. These statistics are crucial for our analysis as they confirm that students who responded to our survey are representative of our target population.

Some of the most used variants, as indicated by survey participants, include hand gestures, saying “you know” and “okay so,” using the ‘welp’ expression, and smiling awkwardly for effect. The total percent usage of each variant is summarized in Figure 3. 

In an effort to acquire more reliable data (as self-reported information can easily be inaccurate or falsified), we also analyzed recordings of people telling stories offline as well. We counted all instances in which the variants in question appeared in these exchanges and generated a frequency chart, as seen in Figure 4. This analysis revealed that the most commonly utilized variants offline include hand gestures, making faces, repetition, and smiling awkwardly for effect. 

A comparison of the trends observed from survey responses and the video recordings shows that overall, the most popular and widely-used variants are saying key phrases such as “okay so” and “you know,” giving an awkward smile, and using hand gestures and facial expressions. 

The big picture

The apparent growth of YouTube’s content creators and their following base led to our hypothesis that linguistic variants are used by YouTubers to draw and influence their audience. Our results support our hypothesis that linguistic features associated with popular YouTubers are utilized by our sample in their own personal conversations. First, the sample data indicates that almost the entirety of respondents (94.5%) engage in storytelling at least multiple times a week. Building on this, the entire sample watches YouTube at least once a week. The combination of these results imply that our sample was a strong representation of YouTube viewers, such that the respondents are evidently influenced to some degree by YouTube content. This combination of results also aligns with Zappavigna’s assertions (2012) regarding the substantial influence of internet content in our lives, since our sample’s frequency of watching YouTube is roughly the same as the frequency of their story-telling. In other words, our respondents spend almost as much time being influenced by YouTube content as they do influencing others with their own personal content.

Our results continue to illustrate the interaction between YouTube viewing and personal story-telling, and show that respondents often use linguistic variants associated with vloggers. The usage of each linguistic variant across our sample is at least 25%, with usage rates above 50% for seven out of the 12 variants. When compared to the frequency in which variants were used in the casual conversation recordings, each variant was used at least once, with three variants appearing at least 10 times. In sum, people do not just use vlogger linguistic variants in their own personal conversations, they use them frequently.

Some limitations to the conclusions of our study are based on potential ambiguity of our survey questions and influences from our respondents’ social backgrounds. The first question about story-telling frequency may be confusing to respondents, causing some to indicate more or less than they actually tell stories. This may explain the group of respondents that only tell stories once a week, or the group that tells stories multiple times a day. Additionally, some respondents may identify with social groups that frequently use linguistic variants that happen to overlap with the variants used by vloggers. For instance, our sample consisted of UCLA students, and UCLA language culture may have similar variant usage to vloggers. In this case, respondents are influenced by at least two separate entities, but the amount of influence from each entity varies. That is not to say that those respondents are not influenced by vloggers, but that the influence is possibly less than the results suggest. Overall, the limitations of our study may show that the influence and draw of vloggers’ linguistic variants are less than the results suggest, though still present.

Future research on this topic could focus on the influence of vlogger language variants on those who identify as men. A study focused on the differences in vlogger influence based on gender may reveal some factors that gender identity have on certain linguistic variant uses. Another topic that could be explored further is the growing influence of vloggers relative to traditional celebrities (e.g., actors, musicians, models). Influence on viewers may be stronger with vloggers due to relatability, but the extent of this effect should be investigated. Finally, the ability to which a popular vlogger can use their influence to market products may reveal that vloggers have more responsibility than they think.



Bestdressed, director. My last weekend in LA…. YouTube, 24 Oct. 2019, www.youtube.com/watch?v=IJcJEMAOJA4&t=53s.

Center for Health and Safety Culture. (2011, January). Validity of Self-Report Survey Data. Retrieved from https://www.minnetonkaschools.org/uploaded/Documents/Dist/Tonka_Cares/Reveal_What%27s_Real/Validity_of_Self_Report.pdf

Frobenius, Maximiliane. “Audience Design in Monologues: How Vloggers Involve Their Viewers.” Journal of Pragmatics, vol. 72, 2014, pp. 59–72., doi:10.1016/j.pragma.2014.02.008.

Frobenius, Maximiliane. “Beginning a Monologue: The Opening Sequence of Video Blogs.” Journal of Pragmatics, vol. 43, no. 3, 2011, pp. 814–827., doi:10.1016/j.pragma.2010.09.018.

Molyneaux, H., O’Donnell, S and Gibson, K. (2009). YouTube Vlogs: An Analysis of the Gender Divide.

Ur Mom Ashley, director. Forcing a GLOW UP the Day before College (FAST Transformation)YouTube, 8 Sept. 2019, www.youtube.com/watch?v=3YCM-Gj1b4w&t=50s.

Ur Mom Ashley, director. My first week of college VLOG *junior year*. YouTube, 13 Sept. 2019, https://www.youtube.com/watch?v=wZr4k_Z6APw&t=124s.

Zappavigna, M. (2012). Discourse of Twitter and Social Media: How We Use Language to Create Affiliation on the Web.

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Why do people interrupt? It depends on the situation you are in.

Mariane Bangui, Oi Kei Cheung, Oscar Franco, Yunjae Lee

We have all been interrupted by others while saying something. Being interrupted is a universal experience, but have you ever hypothesized what contexts affect how we interrupt? Here we present a project investigating how dynamics in negotiations can be reflected through the use of interruptions (N=100) under familial and political contexts.

We hypothesized that (1) family members use interruptions to build rapport and politicians use interruption to exert power, as well as (2) belonging to a culture, whether to individualistic or collectivistic culture, contributes to which type of interruption one prefers to use in a negotiation. To see whether our hypotheses could be justified, we found the frequency of each type of interruption and applied a conversational analysis that examined the influence of culture and context on the use of interruptions in a conversation.

After all data was collected and analyzed, we found that our data did not fully support our initial hypothesis. Even though people in the familial context use rapport interruption to maintain a harmony within negotiations, the results showed that members also use power interruptions just as frequent as in a political context to exert authority. On the other hand, we discovered that the fact of being raised in a collectivistic culture does not affect a person using more rapport or neutral interruptions than power interruptions. Other factors, such as carrying out self-perceived role in a negotiation, contributed much more to the occurrence of our findings.


We have all been interrupted by others while saying something. Being interrupted is a universal experience, but have you ever thought of what factors affect how we interrupt? In the following, our team will present to you in what ways dynamics in negotiations can be reflected through the use of interruptions under familial and political contexts. 

By comparing U.S. politicians to American families of Asian descent, we will first examine which type of interruptions (power, rapport, or neutral) people prefer using in these two situations respectively. We assume family members may mainly use interruptions to build rapport, while politicians interrupt for the purpose of exerting power. 

After taking a closer look at the composition of each interruption, we are going to analyze the cultural background of why some interruptions emerge in the conversations to search for potential generalizations about the connection between contexts and the use of interruptions. We hope that by considering the influence of individualistic/collectivistic culture (American vs countries like Korea and the Philippines), we can understand why certain interruptions occur. 

Background information

Conversation is usually organized in a way, such that only one person speaks at a time. Speakers take turns talking, and use a wide range of linguistic cues indicating the end of their turn (Sacks, Schegloff, & Jefferson 1974 p.670). However, norms of turn-taking can be violated when a speaker “starts up in the midst of another’s turn at talk[ing]” as well as “disturbs his/her finishing” (Jefferson 1984 p.16). 

Interruption in turn-taking has been regarded as a sign of possessing power over others in past literature. Natale et al.’s discovery in 1979 shows the more confident a person is about his/her position with regard to the conversation partner(s), the higher the rate of successful interruptions he/she imposes (p.874). Besides displaying social power, interruptions can serve other functions. According to Goldberg’s study (1990), interruptions are not only acts of conflict but also acts of collaboration. On some occasions, the speaker interrupts so as to demonstrate that he/she understands the conversation and relates to his/her listeners. Yet, there are interruptions that neither aim at building rapport nor displaying power — neutral instances where “repair[s], repeat[s], or clarification[s] of the prior, interrupted utterance” take place (p.888). Building on Goldberg’s findings (i.e., how to identify three main types of interruption), our team finds the relationship between interruptions and contexts is worth further discussion.

Another question we would like to answer is why certain groups use certain interruptions. Our potential explanation will be based on the collectivism-individualism theoretical framework. The framework classifies cultures into two categories: individualist (e.g., the U.S.) and collectivist (e.g., China, Korea, and the Philippines). The former prioritizes individual attainment above team goals, creating a sense of competition among people. In collectivist cultures, on the other hand, people place more focus on achieving group goals than satisfying personal desires (Triandis and Gelfand 2012 p.513). 

Indeed, some research provides empirical evidence to support the idea that culture plays a significant role in determining the occurrence of interruptions. For instance, Li mentioned in his study that Americans use more intrusive interruptions in conversations than Japanese (2001 p.261-262). We therefore aim to extend people’s understanding of the relationship between contexts and interruptions by applying the cultural theory of individualism and collectivism. 


Investigating interruptions within a familial situation, two of our group members voice-recorded a couple of (around 5-10) negotiations in casual dialogue between the parent and children in those families. In both the Filipino and Korean households, our group members left their recording devices in the rooms where each family negotiated about their certain topics. The members then uploaded the recordings they collected to Youtube and pinpointed the exact times that interruptions occurred in the conversations. They transcribed all the labeled interruptions (as well as a few utterances and spoken sentences before/after the interruption) for future analysis. 

In order to explore interruptions within a political environment, we analyze the 2016 presidential debates. This debate featured Republican nominee Donald Trump and Democratic nominee Hillary Clinton. The debates’ transcripts that we based on and added more details into can be found here.

With the data we gathered, we identified all interruptions occurring in the negotiation. Then, we categorized them into three types of interruptions: power, rapport, or neutral interruptions. To see whether or not our hypotheses could be justified, we not only counted the frequency of each type of interruption but also a conversational analysis that examines the influence of culture and context on the use of interruptions in a negotiation.


Figure 1: Percentage of different interruption types in political and familial contexts.


Figure 1 reflects the percentage of each interruption type (i.e., power, neutral, and rapport) occurring in political and familial contexts. We discovered 50 interruptions within the families and throughout the debates from the political aspect respectively. Among the politicians, 68% of interruptions is used to exert power while 32% of them belongs to neutral interruption; in the extreme, no rapport interruption could be found. Unexpectedly, the most frequently-appeared type of interruption in familial context is power (25 out of 50). Rapport interruption only accounts for 14% of the total interruptions in familiar negotiation, and the percentage of neutral interruption exceeds it by 22%.

Table 1: Interruption types in political vs familial contexts.







The most significant difference between political and familial negotiation is the frequency of rapport interruptions. As you can see in Table 1, the data that we have collected from the political dialogue only had power and neutral interruptions. It means rapport interruption did not occur in the political negotiation. On the other hand, we can find all three types of interruptions in the familial negotiation. We initially assumed that family members mainly use interruptions to build rapport; meanwhile, politicians interrupt for the purpose of exerting power. Yet, now it seems like what we assumed is not the case in reality.

Discussion and Conclusions

As mentioned earlier, we discovered that family members and politicians interrupt for the purpose of exerting power most times. But unlike politicians, family members occasionally use interruptions to build rapport. So why is there such a difference? Let’s try to answer this question by first looking at this excerpt adapted from one of the audio recordings that depicts a negotiation among family members:

1  S:   I don’t drink mom (0.8) or smoke (0.3) so you’re good.

2  S:   I don’t do neither of those

3  S:   I know better than that

4  M:   We don’t:::

5  D:       [Can I chaperone mom?=

6  M:   =Eh that’s if you go with him

7  D:                         [Yes, I will go with him

8  S:   (1.0) To drop me off (chuckles)

9  D:   I, I

10 M:      [we::ll

11 D:           [I:: will spy on him 

12 M:                  [then you’re gonna leave him

13 D:   No I won’t leave him

In this negotiation, the 17 year-old boy (S) is trying to convince his “strict” mom (M) to let him go to a birthday party. The mother is hesitant and skeptical of letting her son go due to the family’s upbringing and cultural background. When the mother is giving a lecture to the son, the daughter (D) interrupts the mother and offered if she could chaperone her brother to the party. Her suggestion created a middle ground between the worried mother and the son who wants to go to the party. What the daughter did is an example of rapport interruption in familial context.

But in political context, none of the interrupters empathizes with the interruptee and/or the speech content. Before explaining why this is the case, take a look at the following excerpt (a modified version of the original transcript):

1  W:   Hold on [hold on wait Hold on, folks.

2  T:           [nono that

3  W:   Because we… this is going to end up getting out of control
        Let’s (0.2) try to keep it quiet so 

4  W:   (0.5) for the candidates and for the American people=

5  T:   =So just to finish on the borde[rs

6  W:                                  [Yes

7  T:   She wants open borders (0.2) People are going to pour into 
        our country 

8  T:   People are going to come in from Syria. She wants 550 
        percent more people than Barack 

9  T:   <Obama, and he has> thousands and thousands of people
        They have no idea where they come from...

This excerpt shows an example of neutral interruption in political context. The moderator Wallace (W) acted as a participant who held a neutral stance, and tried not to let candidates like Trump (T) move away from the topic throughout the negotiations. We all noticed that the role that Wallace played in the negotiation is moderator, a role that requires him to adopt a neutral stance.

Not only was the stance that he can adopt restricted but also his use of interruption types was limited to neutral and power. On the contrary, the daughter in the familial context was inclined to maintain harmonious relationship among the two participant who were in a direct confrontation. Being a mediator, she could pick side and choose to help the son to persuade the mother for letting him to go to the party. Factors like this play a significant role in creating situations where certain type of interruption happens (more frequently) in one context but not the other.

Moreover, the fact of being raised in a collectivistic culture does not affect a person using more rapport or neutral interruptions than power interruptions. Rather than cultural reasons, other factors (such as carrying out self-perceived role in a negotiation) contributed much more to the occurrence of our findings. Therefore, in the future, if we are given a chance to continue our study, we will identify more factors behind the way of how political and familial contexts affect speakers’ preference in which type of interruptions they use. And hopefully, we will seek another theoretical framework to explain all the differences we discover.


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Gender Differences in Written Language

Jasmine Murphy, Hannah Hong, Kyungjo Kim, Omar Balawag

This study investigates gender differences in written language. Particularly, by analyzing cover letters for women-preferential language indicators, we find how gender is enacted in formal, written contexts. Furthermore, we investigate how usage of women’s language indicators is affected when discussing subjects deemed to be masculine or feminine. Therefore, by surveying participants for their attitudes towards STEM and Humanities fields, we find that identification of a subject as masculine or feminine did not have as significant an effect on language usage as whether participants perceived the fields to be female or male-dominated. Therefore, this study finds that considerations of social and audience expectations plays the greatest role in whether writers use of gender-linked language indicators.


While people know that there are differences in the way men and women think, there has been an influx in research that proves that these differences extend to the communicative level, that is, the way that people use language. Studies focused on the ways that biological sex impacts language fail to understand that the key factor in gendered language is in the performance of these differences (Fitzpatrick, Mulac, & Dindia, 1995). In other words, people behave in ways that follow their perceptions of gender and masculine and feminine identities (Janssen & Murachver, 2004). In these situations, the question becomes: how do people perform gender?

‘Performance’ within language can be seen in the ways that people phrase things, their word choices, tone, voice pitch, etc. All these factors and more can influence how people express their gender identity. However, the ways that these factors indicate femininity or masculinity differ between spoken and written forms of communication. Additionally, these factors might be affected by different external influences. For instance, writers do not receive immediate feedback based on the reactions of the person they are communicating with; hence, they are not directly faced with social and audience pressure to behave in alignment with gender stereotypes (Janssen & Murachver, 2004). Furthermore, writers have more time to consciously construct their content and thus are able to modify the degree to which they reveal gender (Janssen & Murachver, 2004).

Background Information

Robin Lakoff’s findings in Language and Woman’s Place (1973) indicates the existence of ‘women’s language,’ which is characterized by an identifiable group of linguistic features, including hedges (e.g. sort of), fillers (e.g. well, you know), tag questions (e.g. She’s very nice, isn’t she?), empty adjectives (e.g. charming), and intensifiers (eg. I like him sooo much.), among others. According to Lakoff, using these “women’s speech” features function to express femininity, lack of assertiveness, and subordination in a male-dominated society (Lakoff, 1973). The difficulty in using these features to support Lakoff’s claim about subordination and dominance can be seen in the way multimodality of these features within communication. For instance, tag questions can be used as a form of passive-aggressive speech and as a way to show subordination. Lakoff called these features “women’s language indicators” because of their increased presence within women’s speech. Additional research has also reported the existence of similar phenomenon. In a study by Fitzpatrick et al. (1995), women reportedly used more polite forms of speech, made references to emotion, and used communication to develop and maintain rapport, whereas men were more likely to report facts, solve problems, and debate issues. In this case, the phenomenon might be the same, but the reasons for their appearance differs.

However, this understanding of women’s language indicators within speech has generated a few questions. Are these really feminine language indicators or are they product of something deeper? Does society influence the way that people communicate, not just within speech, but also in their writing? Can this affect how people are perceived when applying for jobs? While the possibility of language indicators actually affecting the success of job applicants is the debate of another study, this research attempts to understand if there is a certain degree of transfer in these features across mediums and the extent to which these indicators are present in written works.

If Lakoff’s (1973) observance of the dominance model is correct, it suggests that people – in particular, women – use subordinate speech to ingratiate themselves with the people around them. This is particularly relevant in environments like the workplace, which have hierarchical positions of dominance and subordination that might influence an individual’s chance of success or failure beyond their conversational capabilities. In addition to the dominance model, there are other reasons why feminine language indicators might appear in written forms of communication. For instance, when speaking about topics deemed to be feminine, individuals tend to use more feminine language indicators and vice versa (Janssen & Murachver, 2004).


The objective of our research is to determine if women’s language indicators also occurs in written, formal works, particularly within cover letters, where the usage of hedges and fillers, may be considered inappropriate. We will identify which linguistic features are indicative of women’s language in formal written contexts and how frequently they occur. In addition, we will analyze whether the interpretation of topics — like STEM or Humanities fields— as masculine or feminine affects the frequency of women’s language features used in writing and the degree of influence in comparison to personal gender identities. 

As we conduct our research, we hypothesize that gender-associated linguistic features will be evident in formal written forms of communication. Additionally, we predict there will be an inclination to use features associated with women’s language when discussing subjects determined to be feminine and an aversion from using such forms when the subject is interpreted as masculine.

Based on these hypotheses we have generated several predictions about our research. First, while the perceptions of the gender divide – as well as expression of the participants’ gender identity – will have the most influence on indicator usage, topics deemed masculine or feminine will also have an impact on language use. Since we predict that STEM fields will be considered masculine and Humanities fields feminine, that women will have more indicators than men, and that individuals in the humanities fields will use indicators more often than STEM, we predict that the greatest number of these feminine language indicators will be present in cover letters written by women/feminine identities in the Humanities field.

The usage of these indicators can inform us on how and why gender differences in language are performed in different contexts. It will also help to explain why perceptions of STEM and Humanities fields work in correlation with feminine language features. Based on our results, we might be able to find implications for whether linguistic features can be used by both men and women as they modify their usage in specific contexts to align with particular expectations, which would have implications for how we perceive gender.


Our participants in this study were student volunteers between eighteen and thirty years old. Through the use of a questionnaire, we received cover letters from the four populations were comparing: Humanities women/feminine identities, STEM women/feminine identities, Humanities men/masculine identities, and STEM men/masculine identities. Our goal was to receive an even number of cover letters from each of our participant groups, of which we received a total of sixteen cover letters.

This study was conducted through surveys and analysis of the submitted cover letters. Participants were asked to fill out a survey asking several questions related to their background and views on related topics, like how they perceive STEM and Humanities fields. See Appendix for details. They were also asked to upload a sample copy of their cover letter. This sample was then analyzed through a series of software (called Atlas.ti Cloud and Voyant) that are designed to create ‘codes’ qualitatively and quantitatively code for particular language features. Particularly, we coded for five women’s language indicators: 1) Hedges: which we defined as being indirect, vague, cautious (perhaps, maybe); 2) References to Emotion: such as happiness, sadness, love; 3) Uncertainty Verbs: like think, believe; 4) Empty Adjectives: which we defined as adjectives that “soften” speech without contributing meaningful information beyond a slightly positive meaning (adorable, gorgeous); and 5) Intensifiers like really, so, extremely. For example, the indicators analyzed are shown in the table below. 

We also calculated the average frequencies between each of the categories that we were looking at: women, men, STEM, Humanities, Humanities women, Humanities men, STEM women, and STEM men. Additionally, we found the standard deviation for each of these groups to better understand the amount of variance found amongst them. 


The responses we received from our participants made it clear that from both a societal and personal perspective, most people perceive the STEM fields as being masculine/male-dominated. In contrast, the perceptions of the Humanities fields received mixed results, as participants seemed to be unable to reach a consensus about the field being either feminine/female-dominate or masculine/male-dominated. See the tables below for more details.


We discovered that women overall used more language indicators than men, but also that participants in the STEM field used indicators more frequently than individuals in the Humanities majors. After breaking down the groups further, we discovered that STEM women use the language indicators more frequently than any of the other groups, but also that STEM men use these indicators more often than Humanities men, although slightly less often than Humanities women. 

In terms of Standard Deviation amongst participant results, we discovered that between the Humanities, STEM, women, and men, the groups with the least amount of variation are women and STEM. Between Humanities men, Humanities women, STEM men, and STEM women, the groups with the lowest Standard Deviation are Humanities women and STEM men. This indicates that while STEM women have the most frequent usage of women’s language indicators, Humanities women have the most consistent frequencies, along with STEM men. See the Standard Deviation table below for more details.



The central finding of this work is that STEM women and STEM men use feminine language indicators more frequently than their Humanities counterparts. While we predicted that Humanities women would have the highest frequency of feminine language indicator usage, women in general used these indicators more frequently than men, regardless of their fields. This suggests that there are additional factors that might have influenced the results.

After tallying the responses to the survey (see Appendix for more details), it is clear that people’s impressions of the STEM and Humanities fields had an impact on their usage of feminine language indicators. As mentioned previously, while the participants’ perceptions of the language divide were mixed for Humanities, they consistently perceived a divide within STEM, which indicates that they might have been influenced by these perceptions when writing cover letters. In accordance to Lakoff’s model of dominance and subordination, their higher frequency within the STEM fields also implies that participants are more sensitive to the ways that gender and power interact within STEM fields.


While this study contributed to our understanding of the interaction between gender and language, there are several limitations. The small scale of the study makes it difficult to determine if the phenomenon observed extends beyond our small population. The results of this study imply that it is inaccurate to call these linguistic features ‘women’s language indicators’ as they are present in both genders. Instead it would be better to say that these language features are the result of the perceived dominance divide between men and women, but also that these are ‘subordination indicators’ and not necessarily restricted to women’s language. To claim otherwise ignores their presence within men’s speech and written forms of communication.



Argamon, S., Koppel, M., Fine, J., Shimoni, A. R. (2003). Gender, genre, and writing style in formal written texts. Text, 23, 321-346. Retrieved from http://writerunboxed.com/wp-content/uploads/2007/10/male-female-text-final.pdf

Fitzpatrick, M. A., Mulac, A., & Dindia, K. (1995). Gender-Preferential Language Use in Spouse and Stranger Interaction. Journal of Language and Social Psychology, 14(1–2), 18–39. https://doi.org/10.1177/0261927X95141002

Janssen, A. & Murachver, T. (2004). The Relationship between Gender and Topic in  Gender-Preferential Language Use. Written Communication. 21.344.10.1177/0741088304270028.

Lakoff, R. (1973). Language and Woman’s Place. Language in Society, 2(1), 45-80. Retrieved from http://www.jstor.org/stable/4166707

Mulac, A., Studley, L.B., & Blau, S. (1990). The gender-linked language effect in primary and secondary students’ impromptu essays. Sex Roles, 23, 439-470.

Newman, M. L., Groom, C. J., Handelman, L. D., & Pennebaker, J. W. (2008). Gender differences in language use: An analysis of 14,000 text samples. Discourse Processes, 45(3), 211-236. Retrieved from http://dx.doi.org/10.1080/01638530802073712

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