Sociolinguistics

Introduction

Have you ever seen a coach cuss out an athlete for doing something wrong? Coaches will use vulgar language towards their male athletes to try and get the most out of them and make them perform better. The use of vulgar language towards male athletes that coaches use can be seen as gendered as it can perpetuate male stereotypes. Stereotypical male characteristics like being strong and not showing emotion can be perpetuated through the word choice of coaches. This research will give a linguistics view on how sports can further gender stereotypes like being the strongest and most dominant male. This incorporates the dominance model and can be seen through the interaction between coach and athlete. The dominance model is the stereotypical notion that men are above women in society. Coaches word choice and tone affect the interaction between coach and male athletes and how this can incorporate the dominance model and perpetuate gender stereotypes. To look at these aspects the show Last Chance U will be analyzed to give examples of discussion between coach and athlete. The show Last Chance U is about junior college football athletes trying to make it to a Division 1 program and this is their last chance of making it in football.

Methods

The methods that were used to answer these questions were by watching the show Last Chance U. The show gives lots of interaction between coaches and male athletes and how their relationship is on the field. This type of source gives lots of conversations and allows to dissect information about coaches’ word choice and tone. The show also gives multiple perspectives of coaches and athletes which allows for further analyzing from each side. It also gives examples of coaches talking negatively to athletes and what words are used to try and get the best out of the athletes. Through multiple seasons there were a lot of examples and information that gave a good representation of the interaction between coach and athlete.

Different linguistic studies also helped with analyzing the interaction between coach and athlete. The linguistic studies helped identify what the interactions that were going on in Last Chance U meant and how this was shown linguistically. Through different studies on the linguistics of sports it gave a background knowledge of what types of words and tones coaches would use with their athletes. These studies helped in finding answers to how coaches used word choice and tone to perpetuate gender stereotypes.

Results

The results found that linguistically there are a lot of gendered interactions between coach and athletes that incorporate the dominance model. The idea of being mentally tough was found as an aspect that coaches pushed on athletes. This idea of being mentally tough is that an athlete must be mentally tough and be able to handle the mental stress of a sport as well as the physical. Mental toughness seems to improve sports performance based on Chang (2012), “Numerous studies have indicated that mental toughness plays a significant role in successful sports performance”. Therefore, so many coaches push for mental toughness and can even overextend the limits of mental toughness with vulgar language. Through vulgar language and negative word choice coaches can stack this with mental toughness to try and push their athlete. In Feezell (2008) Coach Knight uses vulgar language and justifies it by saying that the athlete should handle it by being mentally tough. Based on Feezell (2008), “Coaches like Knight might think they’re only trying properly to motivate an athlete, but the possible damage done by a coach’s tirade might still be palpable and unacceptable”. Coaches will use vulgar language and harsh word choice to try and push their athlete but this can be confused for mental toughness. This is stacked on top of mental toughness and it is expected for male athletes to be able to handle it because of male gender stereotypes. This type of motivation from a coach is perpetuating the male stereotype that males are not supposed to show emotion.

Discussion/Conclusion

Coaches will use harsh language with their athletes to try and perform better but sometimes this can result in the athlete using it themselves to push themselves. There is a lot of pressure for athletes to do well so they will use vulgar language themselves.  Below is a transcription from Tovares (2010).

 

This transcription is of an athlete talking to themselves while running a marathon and it shows some of the pressure that the athlete is facing. The vulgar language is present in transcription, and it is showing what mental toughness is all about. This is what a coach would push for an athlete to do because it is an example of mental toughness. This language is reflecting the gender stereotype and is a way of establishing masculinity. This falls right into the incorporation of the dominance model because emotions are supposed to be put aside for men and this is an example of bottling up emotion.

Example 1 from the show Last Chance U demonstrates how the word choice of coaches can become gendered and even make them feel marked. Marked is when someone feels like an outsider or does not fit in fully. This example is at practice when the coach Diaz pulls Malik to the side and tells him that he is making him look bad in front of everyone else.

The word choice of “lipstick” and “fuckin’” make this interaction between coach and athlete gendered. The coach is referring to how Malik is making him look like a woman on the football field. This in football is unacceptable for a football coach and is something that coach Diaz lets Malik know. This is an idea of markedness because coach Diaz feels like an outsider with his reference of feeling like he has lipstick on his face. This gendered interaction perpetuates male stereotypes and makes it seem like you can only be a strong and emotionless man to be on the football team.

The hall of mirrors effect could be present in this research because by searching up the topic some of the information that contradicts my claim could be overlooked. The sources that were used fit my claim and therefore no sources that contradicted it were found which is why the paper could fall into the hall of mirrors effect. This research contributes to the larger phenomenon that sports drive a lot of stereotypes that society is trying to rid of but they stay in sports.

 

References

Chang, Yu-Kai, Lin Chi, and Chih-Shiung Huang. “Mental Toughness in Sport: A Review and Prospect.” International Journal of Sport and Exercise Psychology, vol. 10, no. 1, 2012, pp. 79-92. ProQuest, https://search.proquest.com/scholarly-journals/mental-toughness-sport-review-prospect/docview/1011870028/se-2?accountid=14512, doi:http://dx.doi.org/10.1080/1612197X.2012.661202.

Tovares, Alla V. “Managing the Voices: Athlete Self-Talk as a Dialogic Process.”Journal of Language and Social Psychology, vol. 29, no. 3, 2010, pp. 261-277. ProQuest, https://search.proquest.com/scholarly-journals/managing-voices-athlete-self-talk-as-dialogic/docview/758115517/se-2?accountid=14512.

Feezell, Randolph. “Vulgarians of the World Unite: Sport, Dirty Language, and Ethics.” Journal of the Philosophy of Sport, vol. 35, no. 1, May 2008, pp. 17–42. EBSCOhost, doi:10.1080/00948705.2008.9714725.

Introduction

Regardless of their content, social media influencers aim to grow and maintain an audience to ensure their platforms are marketable and profitable. We found there are several linguistic techniques these influencers adopt to build connections with their fans. In particular, they replied to their followers’ comments under their videos to facilitate connection with their audience, and, when doing so, utilized rapport-building linguistic features.

In general, men and women have been understood to communicate differently in the process of forming connections. We wanted to further investigate differences across genders in connection-building communication in the context of online social platforms.

These concepts guided our research, as we examine the linguistic differences of male and female influencers in their written responses to followers’ comments online. 

Background

Due to our scope of interest in influencers’ platform maintenance, we examined existing literature on gendered and computer-mediated communication.

The dominance model suggests that female language use reflects male dominance in society (Lakoff, 1975), whereas the difference model proposes that differences in language between men and women reflect different cultures of conversation (Tannen, 1990). Despite both styles serving the same communicative function, women use rapport-oriented conversation, which is more emotional, while men use report-talk, reporting fact-based information and competing for hierarchy in conversation (Tannen, 1990). Further work has been done on gendered communication differences to see what linguistic features can be attributed to men: in homosocial contexts, men use expressions like “dude” or “bro,” as their way of performing male expectations, indexing their heterosexuality to promote heterosexuality as their preferred orientation. (Van Herk, 2018, p.109).

Since we are looking at social media influencers, we also looked at computer-mediated communication (CMC) studies to see how these patterns reflect in a modern online context. The content of CMC messages by females is more expressive than males, reflecting a female’s social role of being emotionally expressive and collaborative, as mentioned in the Tannen’s model (Fox et. al., 2007, p. 395). Regarding CMC-specific linguistic features like emoticons, studies suggest that females use emoticons as a means of expressing solidarity, support, positive feelings, and gratitude––reinforcing the existing stereotype that females are more emotional than males (Wolf, 200, p. 827). Literature on text-based punctuation in online messages suggests that digital cues, such as excessive punctuation and capitalization, increased the bonding of female friendships (Sherman et. al., 2013). These cues were made frequently by young women to convey emotion in their text-based conversations.

Our main research question is the following: “To what extent does the gender identity of YouTube fitness influencers affect the digital linguistic expressions they use to establish solidarity with their followers?”

In our research, we observed that females used more frequent expressions than males across four different features we examined; however, by narrowing down on more specific sub-categories under the features, we found that even though males and females used different linguistic expressions, both male and female fitness influencers were using different tools to pursue the same purpose of establishing solidarity with their fans.

Methods

We conducted our study by analyzing the computer-mediated communication used by ten female and ten male YouTubers. We narrowed our sample choice by selecting YouTubers who belong to the fitness industry, create fitness content for YouTube, and speak English. Our sample was categorized into two sections: influencers with less than one million subscribers (Table 1) and influencers with over one million subscribers (Table 2). To avoid bias, data were collected from each influencer by randomly selecting ten interaction-based comments from two randomly selected workout videos.

We categorized our data into four linguistic features based on the most salient differences we observed among the comments of male and female fitness influencers. Our data was analyzed based on the frequency of emojis, exclamation points, capitalization, and pet names. In terms of emojis, we looked at the types of emojis that were being used differently by males and females. The specific emojis were grouped into three distinct categories: facial expressions, gestures, and non-human symbols (fire, stars, sweat, etc).

Results and Analysis

First, we looked at the use of emojis in YouTube comments from female and male influencers. Symbol and facial expression emojis were popular for women, using a variety of faces such as 🤪 and hearts 💖.

Men also used emojis frequently but the specific emojis they used differed from what females used––males instead opted for symbols like 🔥 or 💦. 

Overall, women used more expressive facial emojis along with many gesture emojis (Figure 1). In general, women used facial expression, symbol and gesture emojis more frequently than men.

Second, we analyzed punctuation by examining the use of exclamation points in influencers’ responses to comments. Women tended to use exclamation points in most of their replies, often using several exclamation points in a row. In contrast, males did not use exclamation points as frequently, and when they did, they only used 1-2 per comment.

While men did use exclamation points, they did not use them to the same extent as women. Women used exclamation points more frequently, totaling over 100 times throughout comments compared to just over 25 by men (Figure 2). The number of comments in which these features appeared was identical for male and female. Females typically used digital cues including excessive punctuation to better convey emotion online.

Next, we considered influencers’ usage of capitalized words within sentences. We found that female influencers were more inclined to capitalize individual words or phrases when replying to comments. Women often capitalized words of encouragement like “good job” yay” or “yesss”. Conversely, men rarely utilized the capitalization of words.

The capitalization of words (particularly for emphasis) was used by females at a higher rate than their male counterparts. Whereas women did this over 100 times throughout our data, men did not even reach a count of 5 (Figure 3).

Finally, we looked at the pet names influencers used when responding to comments. Female fitness influencers often used words such as “girl,” “queen,” and “babe” to address their followers, whereas males used terms like “man,” “buddy,” and “mate” to address their followers in a similar supportive fashion. Figures 5 and 6 display the overall comparisons of the four linguistic features’ rate of appearance in 10 comments written by the male and female social influencers.

We observed that females used pet names more frequently than males, but the difference was not as large (Figure 7). 

 

Discussion and Conclusions

There are four key insights that summarize our research results.

First, by taking a closer observation at the types of emojis male influencers used, we found that males usually used bicep emojis whereas females did not use them at all. Females generally used more emojis across all emoji categories. However, by narrowing down to a more specific sub-parameter within symbolic emojis, we observed that males were in fact using a different tool to strengthen their relationship with their predominantly male fans. 

This insight suggests that in CMC, both males and females likely strive to establish solidarity with their fans but through different linguistic tools. On social media platforms, influencers of all genders are driven by financial motivations to attract viewers by crafting themselves as more responsive and supportive than their competitors.

Second, the nature of the male influencers’ comments was more action-oriented than that of female influencers’ comments. For example, males often posted comments such as “Keep going” and “Well done!”, whereas females often posted gratitude-expressing, emotional comments such as, “Thank you!!!” and “ILY MY QUEEEEN”.

This second point illuminates that most of these interactions took place between same-sex followers and influencers. This phenomenon could be attributed to the fact that the fitness objectives of the videos were inherently geared to target the followers of the same sex as the influencers. For example, we noticed that the majority of the videos created by female fitness stars tend to have titles such as, “Intense Glute Workout”, but males posted videos with titles like “Build a Bigger Chest”. These fitness videos align the body areas that respective genders tend to visually prioritize when developing their body muscles. In general, females are more self-conscious of their leg and glute areas whereas males typically focus on building the size of their upper body.

Third, we observed a dichotomy of ‘emotional vs calm’ which is a digital adaptation of the ‘report vs rapport” model. In particular, male influencers’ average length of comments is significantly less than female influencers’ average length of comments. The male influencers’ responses were calmer than those female influencers. We think this ‘emotional vs calm’ dichotomy is a formal theorization of what computer-mediated gender communication can look like in the context of our digital influencer study. Our research also invites further studies by future socio-linguistic scholars interested in the intersection between gendered communication and online social media platforms.

Lastly, with male influencers specifically, we observed that those with a smaller following responded more frequently to comments than those with a larger following. Once reaching a certain level of popularity (over 1 million), males responded less frequently. We theorize this is because males use more feedback only before their platform grows to a certain level of popularity. Given that our study only examines 20 social influencers on YouTube, we’d like to invite future researchers to conduct more studies in these areas.

In short, our research study shows that females are predominantly more expressive than males across all 4 linguistic categories, but males have more frequently used bicep emojis in particular. In addition, even though females’ responses were visibly more expressive in terms of the frequency and variety of emoji usage, both males and females were pursuing the same purpose of establishing solidarity with their fans, by using different tools.

We argue that the Tannen model is being applied differently in the context of computer-mediated communication and the nature of social media, as social influencers, whether male or female – are in positions to appeal to their general audience. We also propose the “emotional vs calm” dichotomy observed from gendered communication in online platforms and invite further research to be done in this area.

Among several, one limitation of our research is that we did not incorporate the nature of the followers’ comments that the influencers responded to. In general, we observed that most viewers’ comments were positive, grateful, and supportive. This research invites future studies to undertake how the responses would look different towards comments that are hateful or negative. In addition, more studies on how non-famous males and females differ in their digital communication on social online platforms are needed.

 

References

Bamman, D., Eisenstein, J. and Schnoebelen, T. (2014), Gender identity and lexical variation in social media. J Sociolinguistics, 18: 135-160. https://doi.org/10.1111/josl.12080

Fox, A. B., Bukatko, D., Hallahan, M., & Crawford, M. (2007). The Medium Makes a Difference: Gender Similarities and Differences in Instant Messaging. Journal of Language and Social Psychology, 26(4), 389–397. https://doi.org/10.1177/0261927X07306982

Herk, G. V. (2018). Gender. In What Is Sociolinguistics? (pp. 96-116). Wiley Blackwell.

Lakoff, Robin. 1975. Language and woman’s place. New York: Harper Colophon Books.

Sherman, L. E., Michikyan, M., & Greenfield, P. M. (2013). The effects of text, audio, video, and in-person communication on bonding between friends. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 7(2), Article 3. https://doi.org/10.5817/CP2013-2-3

Tannen, D. (1990). “Put Down That Paper and Talk to Me!”: Rapport-talk and Report-talk. In You Just Don’t Understand: Women and Men in Conversation (pp. 74-95). HarperCollins.

Wolf, A. (2000). Emotional Expression Online: Gender Differences in Emoticon Use. CyberPsychology & Behavior, 3(5), 827–833. https://doi.org/10.1089/10949310050191809

Background

At UCLA, undergraduate students who have excelled in certain STEM classes can return to those courses and help teach current students as part of the Learning Assistant program. As part of the Learning Assistant program, undergraduate Learning Assistants attempt to foster an inclusive learning environment where students can better understand the topics covered in their respective courses. They are trained to promote an interactive environment, even through Zoom, as well as use a Socratic questioning style which encourages students to think independently (Talbot et. al 2015). As seen in this video, Learning Assistants are encouraged to let students figure out the answer themselves rather than feeding it to them. Promoting interaction and encouraging independent thinking, according to the program, force students to think more critically about the content they have to learn, and, as a result, develop a more solid understanding compared to if one were to just passively teach it (Furtak and Kunter, 2012).

However, a problem potentially arises when looking at the interactions between Learning Assistants and students themselves. In order to promote inclusivity, LAs are encouraged to act with kindness and politeness towards the students (Sellami et al., 2017). Creating a warm, supportive atmosphere has been shown to improve with facilitating student learning (Jardine et al., 2020). However, the kinds of polite statements which the Learning Assistants use may differ based on their gender, as well as potentially even the gender of the student who they are addressing. This may potentially lead to differential learning outcomes based on gender due to different levels of support from the Learning Assistants. This study sought to answer one major question: do LAs tend to use a certain kind of politeness more than another when students identified as the same gender as themselves?

Methodology

This study collected data from ten Learning Assistants for an organic chemistry class, five male and five female, who agreed to participate. These Learning Assistants recorded their discussion sections, which were held over Zoom, and sent them as attachments in emails for this study’s analysis.

When analyzing the conversations that Learning Assistants had with students, this study looked for two main kinds of politeness: positive and negative politeness. Each of these was categorized in the following ways (Brown and Levinson, 1987):

Positive Politeness: Words of affirmation towards a person who is part of an in-group or considered a friend. A very casual and friendly statement with the intent of encouragement or support.

Negative Politeness: A more formal and avoidant strategy used to maintain distance between the user and the target. There is more restraint with negative politeness and, occasionally, apologetic wording. It may involve hedging, which is used to add uncertainty or decrease assertiveness to a particular statement.

When analyzing each of the Zoom recordings, this study categorized Learning Assistant-student interactions based on the LA’s gender, the student’s gender, and whether the student’s provided answer was correct or incorrect. The number of instances of each kind of politeness was tallied for each recording. This was then divided by the number of minutes the LAs spent talking to the students in order to obtain frequencies for each kind of politeness. The frequencies for each LA were averaged, and then analyzed as data below.

Analysis

We can use the technique of discourse analysis (Janks, 1997) to analyze the conversation in Example 1 between a male Learning Assistant and a male student. 

Transcription Key:

MLA indicated Male Learning Assistant

MST indicates Male Student

FST indicates Female Student

(time) indicates a pause for [time] seconds

- indicates a short, untimed pause

: indicates a stretched syllable

(.h) indicates an inhalation

Underline indicates positive politeness phrases


Italics indicates negative politeness phrases

For the purposes of confidentiality, the actual names of the students and LA will be replaced in conversations with MST/FST/MLA, respectively.

In Example 1, the male Learning Assistant uses four instances of positive politeness with the male student. When looking at this conversation with the idea of a student’s perceived levels of support in mind, one can see how this student would feel very supported. The casual language which the Learning Assistant uses, such as “dude,” as well as the phrases of encouragement such as “You got it!” and “Exactly!” would most likely make this student feel as if his answer was validated and that he belonged in such a learning environment. The four instances of positive politeness, marked using an underline, reaffirm the student that he is correct. The Learning Assistant’s action of making this student more comfortable in the discussion section may, in turn, make him feel more confident about his ability to succeed in the class.

Now let’s take a look at an example of a conversation where the same male Learning Assistant interacts with a female student:

Much like the male student from the previous example, the female student in Example 2 also answered the male Learning Assistant’s question correctly. However, the Learning Assistant’s response towards her answer was not as comforting and casual as it was with the male student. For example, he only used two instances of positive politeness compared to the four he used in the previous example. Additionally, he now uses two instances of negative politeness, indicated in italics, when asking her to further explain her answer.

Similar overall trends to the two examples above are seen when looking at the combined data of this study. This data is pictured in the two graphs below:

There are two major points of interest in Figure 1. First, when looking at each interaction, every correct response yielded more frequent positive politeness than negative politeness. However, more importantly, one can notice that male LA/male student and female LA/female student interactions have more frequent use of positive politeness than the other two kinds of interactions with different LA/student genders. This finding supports the idea that positive politeness is more commonly used when an LA interacts with a student of the same gender.

The results for incorrect responses in Figure 2 are not as conclusive as they were for correct responses. Once again, this data shows that positive politeness is used more frequently when a student and LA have the same gender compared to when they have different genders. More importantly, however, is the fact that female/female interactions had more instances of negative politeness than female/male interactions here. This finding disproves the idea that negative politeness is dependent on gender in interactions. Unlike positive politeness, which was higher in same-gendered interactions for both examples, negative politeness does not have any trend with relation to gender.

Conclusion

After analyzing the data, the study ultimately concluded two things. The first conclusion reached was that positive politeness is used more frequently when Learning Assistants interact with students who share the same gender as them. In addition to this, however, the study also concluded that the frequency of negative politeness is independent of the Learning Assistant and student’s genders.

The finding that positive politeness is higher in same-gendered interactions may have implications with regard to perceived student support: students may feel more supported if they interact with LAs who identify with their same gender compared to LAs who do not. Increasing awareness of this issue amongst LAs may allow them to be cognizant of the existence of such a bias, and therefore, would help them know to be just as supportive towards differently-gendered students. Similar future studies should include non-binary participants in order to extend the results past the gender binary.

 

References

Brown, P., & Levinson, S. C. (1987). Politeness: Some Universals in Language Usage. In Politeness: Some universals in language usage (pp. 311-323). Cambridge: Cambridge University Press.

Furtak, E. M., & Kunter, M. (2012). Effects of autonomy-supportive teaching on student learning and motivation. The Journal of Experimental Education,80(3), 284-316. doi:10.1080/00220973.2011.573019

Janks, H. (1997). Critical discourse analysis as a research tool. Discourse: Studies in the  Cultural Politics of Education,18(3), 329-342. doi:10.1080/0159630970180302

Jardine, H., Levin, D.M., & Cooke, T. (2020). Group Active Engagement in Introductory Biology: The Role of Undergraduate Teaching and Learning Assistants.

Sellami, N., Shaked, S., Laski, F. A., Eagan, K. M., & Sanders, E. R. (2017). Implementation of a learning assistant program improves student performance on higher-order assessments. CBE—Life Sciences Education,16(4). doi:10.1187/cbe.16-12-0341

Talbot, R.M., Hartley, L., & Wee, B. (2015). Transforming Undergraduate Science Education With Learning Assistants: Student Satisfaction in Large Enrollment Courses. The journal of college science teaching, 44, 24.