Selected Papers of #AoIR2022: The 23rd Annual Conference of the Association of Internet Researchers Dublin, Ireland / 2-5 Nov 2022 Suggested Citation (APA): Homant, E. and Russell, A. (2022, November). Coming Out and Coming Up: Quantifying Queer Immaterial Labor. Paper presented at AoIR 2022: The 23rd Annual Conference of the Association of Internet Researchers. Dublin, Ireland: AoIR. Retrieved from http://spir.aoir.org. COMING OUT AND COMING UP: QUANTIFYING QUEER IMMATERIAL LABOR Ellie Homant Cornell University Aspen Russell Cornell University Literature Review Since YouTube’s inception in 2006, users have carved out a variety of genres and niches on the platform. One of the earliest was the beauty space, made up of users who posted beauty vlogs that included makeup tutorials and videos showing off new or favorite products. Although the beauty vlogosphere has been racially diverse since its inception—with Asian American Michelle Phan widely cited as the first beauty vlogger— like the broader beauty industry, YouTube’s beauty space has historically been dominated by gender- and sexually normative White women. However, in recent years a new class of queer-identified beauty vloggers have risen to fame on the platform, including creators like Jeffree Star, Manny MUA (Manny Gutierrez), James Charles, Patrick Starrr (Patrick Simondac), and NikkieTutorials (Nikkie de Jager). These creators encompass a range of gender, sexual, racial and ethnic identities. Although these vloggers have “made it” on YouTube—i.e., they all have millions of subscribers, regularly traffic in hundreds of thousands of video views, and engage in paid corporate sponsorships—there are still significant barriers to entry on online platforms like YouTube for creators who occupy non-normative identities (i.e., those who are not cisgender, heterosexual White men). Previous scholarship has identified the specific type of labor that queer online content creators undertake as “queer immaterial labor,” which recognizes that (1) immaterial labor is not performed in the same way by creators in online spaces, but rather is structured by complex interplay between a variety of social identities; and (2) that queer influencers undertake labor that is specific to the queer community (e.g. performance of the coming-out narrative, employing queer cultural resources) (Homant and Sender, 2019). Queer immaterial labor is a specification of Maurizio Lazzarato’s (2006 [1996]) prescient concept of immaterial labor, which he argues is a shift to prioritize information and communication jobs over manufacturing, increase valuation of cultural content, and to produce “a ‘social relationship’ (a relationship of innovation, production, and consumption)” (p. 137). While Lazzarato and his contemporaries primarily focused on the impact of class on immaterial labor, feminist scholars have reinterpreted Lazzarato’s concept with greater emphasis on gender and race. Much of the labor that online content creators undertake is specifically affective, where influencers are trying to assert their authenticity and sincerity in order to cultivate community with their audience and keep viewers coming back. Gender and racial hierarchies add additional complexity in the likelihood of success when undertaking this kind of labor. This means that content creators who occupy non-normative identities (e.g., queer people, people of color), must shoulder additional work. Moving forward, there are clear opportunities to investigate queer immaterial labor on YouTube, specifically to test the boundaries of claims made. Our goal is to use a computational approach to measure its two primary facets: amount of labor and unique labor practices. This can be accomplished by returning to YouTube and expanding the sample of queer and non-queer beauty vloggers with broader metrics for comparison. We will use a mixture of comments, transcripts, and video metadata. While the subject of the videos informed much of the prior research on this subject, there is much to learn from the reception of these videos. In an analysis of 67,000 YouTube videos and 6 million comments, Siersdorfer, Chelaru, and Nejdl (2010) discovered that comments, and their associated scores of likes and dislikes were highly indicative of the level of polarization of content. Guo and Fussell (2020) investigated the influence of user comments in a live stream chat compared to the transcripts of live streamers on YouTube. They found that user comment sentiment was a better predictor of future chat sentiment than that of the streamer. Alongside comments, metadata (likes, shares, reactions, etc.) have been influential in learning about the impact of content (Ferrara and Yang, 2015). All of these studies point to the importance of user’s metadata and comments in shaping the community and response to YouTube content. Methods Each study points us toward important data that we can use to measure our constructs. To gain access, we will be using the Google’s YouTube application programming interface (API) and the “tubeR” package for the R programming language. There are a variety of data and metadata about the individual videos and channels of these content creators that these tools allow us access to. To characterize the amount of labor performed when comparing queer and non-queer vloggers, we will use video length, transcript length, number of replies to user comments, length of video description, and number of tags. While we never know exactly who is performing these tasks, aside from the video subjects, these measures do point toward an investment in the channel and reflect overall labor. The beauty vlogging space varies immensely in channel size and content format. In an effort to manage this variance, we will normalize these metrics by comparing the ratio of video and transcript length to the number of videos on the channel. This can be further broken down over the lifetime of the channel. To contextualize labor performed solely by queer vloggers, we will focus on video titles and transcripts. Using a dictionary-based approach, we will compile a list of words commonly associated with queer identity and assess the context of their use in videos and channels. From the sample that contain queer-identity words, a qualitative text analysis approach will be used categorize the common contexts that these terms are used in the video titles. Our sample consists of already popular beauty vloggers. To assess criteria, we will use YouTube’s creator award system, which award channels plaques based on the number of subscribers they have amassed. We will use the lowest threshold of silver (100,000 subscribers) to be considered “popular.” To be considered a makeup YouTuber, the channel must produce YouTube videos about makeup and adjacent products and processes. Videos and comments must also be primarily in the English language. We will conduct this comparison between a sample of 500 queer-identified beauty vloggers and 500 equivalent non-queer beauty vlogger channels. Finding these channels will be based on a snowball sampling technique based on the YouTube recommendation algorithm. Our goal is to provide a series of descriptive comparisons across this large sample of beauty vloggers on YouTube. Insights will allow us to directly point to areas of potentially inequitable output and differences in labor practices across these two groups. References Ferrara, E., & Yang, Z. (2015). Quantifying the effect of sentiment on information diffusion in social media. PeerJ Computer Science, 1, e26. https://doi.org/10.7717/peerj- cs.26 Guo, J., & Fussell, S. R. (2020). A Preliminary Study of Emotional Contagion in Live Streaming. Conference Companion Publication of the 2020 on Computer Supported Cooperative Work and Social Computing, 263–268. https://doi.org/10.1145/3406865.3418309 Homant, E., & Sender, K. (2019). Queer Immaterial Labor in Beauty Videos by LGBTQ- Identified YouTubers. International Journal of Communication, 13, 19. https://doi.org/1932–8036/20190005 Lazzarato, M. (1996). Immaterial labor. Radical Thought in Italy: A Potential Politics, 1996, 133–147. Siersdorfer, S., Chelaru, S., Nejdl, W., & San Pedro, J. (2010). How useful are your comments? Analyzing and predicting youtube comments and comment ratings. Proceedings of the 19th International Conference on World Wide Web, 891–900. https://doi.org/10.1145/1772690.1772781