Friday, December 13, 2013

Revised Polished Analysis

I chose to use Facebook as a means of studying adolescent girls and their interactions online, more specifically how speech acts online help shape individual identities.  I argue that Facebook is an affinity space (Ellcessor & Duncan) in large form, and as such it allows for the participants to create a group lead space where they can construct their online identities.  Just as people make deliberate choices regarding speech use in face to face interactions, those same choices are made in online conversations.  Individuals, specifically adolescent girls, are aware of their audience in an online environment, and they code-switch (Sankoff & Poplack) and use language differently depending on with whom they communicate.  I started this research by identifying the participants who I would follow on Facebook.  The participants in my group are teenage girls between the ages of 16 and 18 years old.    They all live in a small town (Palmer, Alaska—approximate population of 5,900 in 2010), and they attend the same high school.  All of the participants are friends, move in the same social circles, and most are members of the same extracurricular activities (student government, band, etc.).  I address each participant by their initials.  I am Facebook friends with MS but not the other participants, and, therefore, I can only view the information the other individuals have made public.

My first data set is from a post that originated on MS’ wall.  See Figure 1 for the original data set.  The thread started as a meme on which MS was tagged along with three other individuals, so a total of five participants.  The thread then elicited 25 comments.  The data set includes comments from five people, one male and four females.  This particular thread helps me with my research because it includes multiple instances of code-switching.  I have noticed the following—typographical differences between comments, the use of internet vernacular and emoticons, and numerous uses of non-standard written English.  For instance, one of the comments in the first data set is from SH who writes, “Oh we are going to have soooooooo much fun” (Figure 1a).  Likewise, another participant, AL, writes, “Not me?  I’m gonna go home now (QQ)” (Figure 1a).  The first example shows non-standard written English because of the misspelling of the word so, while the second example shows the use of emoticons.  At first I was not sure what to make of this data, and it did not become completely clear until I had collected and analyzed the second data set.

My second set of data is slightly different from the first set in the sense that it includes far more items to study.  See Figure 2 for original data set.  One of MS’ friends made a video about the most influential teacher she had in high school.  She chose the music teacher at the high school, and then she tagged all the other students who were in the same program.  All the students from the first data set were tagged in the second data set.  MS did not comment on this particular post, but there were many other people who did.  There are also some adult influences in this post that were not present in the first data set.  This adult influence made me realize how intrinsic audience awareness is, particularly to adolescent girls.  I focus specifically on the difference in the language used in the presence of adults, even if it is in an online community.  The internet speak and use of vernacular is significantly curbed in my second data set.  Likewise, there are zero instances of emoticons, and the non-standard written English is significantly decreased.  

The second data set revealed that the high school students changed the way they use language when they are communicating with adults.  The use of code-switching, or switching between two distinct and different ways of speaking with different groups, appears to be intentional.  The participants make specific linguistic choices within the second data set.  The second data set is interesting because it revolves around a teacher, a teacher who participates in the conversation.  The majority of the conversation comes from current and former students who are reminiscing about certain neologisms and humorous anecdotes that the teacher uses in his classroom.  Therefore, most of the instances of non-standard written English come from those parts of the data.  For instance, DM quotes the teacher, “MAY THE BIRD OF PARADISE LAND ON YOUR LEFT EAR” (Figure 2a) and later CR gives a different quote “Stop masticating the stands, they are only for music” (Figure 2a).  While both examples are linguistically sound, they are both also rather nonsensical.  The examples show the ability of the participants to understand their audience and then write specific comments to that audience.  If the teacher who was profiled in the video had not been present in the conversation, it is presumable that the conversation would have developed quite differently, simply because the audience was different.

My major argument is that adolescent girls invent an online identity, specifically on Facebook, and that identity is tied to the language used on their profiles, posts, and comments.  I also assert that the identity one establishes or creates can be modified depending on the audience with which one interacts.  For instance, in my second data set there are a large number of adolescent girls interacting in a thread that is related to their school environment.  The language used in this data set is different from the first, and I posit that it is different because the audience has changed.  The first set is smaller and only includes five members.  The first set also ends up being about school, but it does not start out that way.  The second set was much larger and originated as a post that was already school related.  The second set also included adults, which in turn made the audience and the language used different from the first.  The informal nature of the first set was gone and had been replaced by a more standard approach to writing English.  I argue that nature of the writing gives the writer certain linguistic capital (Merchant), or credit in terms of reputation, depending on who an individual is speaking to and if the individual is seeking linguistic capital.  In the case of Facebook, the linguistic capital also leads to social capital, or the resources one has available due to the social relationships one has cultivated.  These two ideas are particularly important in relation to the second data set in which the participants are interacting with adults, or those who can measure their linguistic and social capital.  

In terms of actual data, the majority of my analysis comes from my second data set.  While the first data set is interesting, its main focus is to situate the reader/viewer into the conversation, and most importantly, it allows the reader/viewer to understand how this particular cohort of adolescents interact with one another on a smaller scale.  Then the second data set allows the reader to see how they interact on a larger scale with different people present in the conversation.  For instance, the information presented in the first data set shows nine instances of emoticons and three instances of overt use of non-standard conversational written English in just 25 comments.  However, the second data set has zero emoticons and only three instances of of overt use of non-standard conversational written English in 45 comments.  I use overt in the sense that these adolescents are making language choices that are outside the norm of standard conversational written English.  This information leads me to assert that adolescents are aware of their audience and they purposely code-switch depending on to whom they are communicating.  The information also shows that the adolescents are taking language risks among their peers, while using standard forms of writing when interacting with a mixed audience.  This form of code-switching is rather impressive in a group of young people, especially since the code-switching appears to be intentional and purposeful rather than automatic.

I researched three terms that I thought would be the most important—gender, language use, and identity.  I chose these particular terms because they yielded the best results.  I found gender to be the least important to my particular research; however, it is still important in the sense that the participants are impressionable adolescent girls.  I focus on language use, principally standard versus non-standard written English, the level of usage of emoticons, and the amount of code-switching between the two. I also determine how language functions on Facebook, specifically in posts, tags, and comments, and how the participants use the language online to build their online identities.  The articles I chose to use focus on identity and how individuals express their own identities online, as well as how particular language use can function as a form of identity.  

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