Selected Papers of #AoIR2022: The 23rd Annual Conference of the Association of Internet Researchers Dublin, Ireland / 2-5 Nov 2022 Suggested Citation (APA): Stegeman, H.M., Poell, T., Duffy, B.E., Jokubauskaitė, E., Cotter, K., Velthuis, O. & Meisner, C. (2022, November). Platforms and the precarity of creator (in)visibility. Panel presented at AoIR 2022: The 23rd Annual Conference of the Association of Internet Researchers. Dublin, Ireland: AoIR. Retrieved from http://spir.aoir.org. PLATFORMS AND THE PRECARITY OF CREATOR (IN)VISIBILITY Hanne M. Stegeman University of Amsterdam Thomas Poell University of Amsterdam Brooke Erin Duffy Cornell University Emilija Jokubauskaitė University of Amsterdam Kelley Cotter Pennsylvania State University Olav Velthuis University of Amsterdam Colten Meisner Cornell University Whether vlogging on YouTube, camming on Chaturbate, or hyping products as an influencer on Instagram, creators require platform visibility to monetize their labor (Cotter, 2019). For marginalized creators, digitally mediated visibility may be socially motivated too, bound up as it is with the politics of (self-)representation (Cunningham & Craig 2019). Indeed, what is most visible on platforms (re)produces social norms and generates revenue (Marwick, 2015; Van Doorn & Velthuis 2018). Visibility thus shapes the allocation of money and power in the online attention economy (Duffy 2020; Poell et al., 2021). At the same time, both platforms and creators have a vested interest in keeping some aspects of their activities invisible. A key critique of “Big Tech” is the extent to which platform companies systematically obfuscate their algorithmic curation systems, business models, and moderation practices from the public (Gillespie, 2018). Creators, meanwhile, may try to shield their offline lives and identities from networked audiences (Van der Nagel & Frith 2015). Especially those representing marginalized communities may conceal their identities in an effort to stave off hate, harassment and violence (Banet-Weiser & Miltner, 2016). Other creator categories, such as online sex workers, often hide their ‘real’ identities to avoid stigma and discrimination (Jones, 2015). Such accounts suggest that the stakes for platform (in)visibility are heighted for creators from historically marginalized groups, as well as for those who produce content in socially stigmatized genres of cultural production. This panel examines the tension between visibility and invisibility in the platform-creator relationship with a focus on highly precarious areas of cultural production, including online sex workers, body activists, and LGBTQ+ content creators, among others. Together, the panelists consider: what kinds of creators are made visible on platforms? Conversely, who remains invisible? How do platforms and their algorithms afford (in)visibility to marginalized and stigmatized creators? How do these groups resist top-down (in)visibility regimes? Finally, how do such (in)visibility regimes challenge or reaffirm other forms of social marginalization? Primarily drawing on interviews with creators on a variety of social media and webcam sex platforms, the panelists examine the complex dynamic between visibility and invisibility. The first paper studies the role of algorithmic labor and capital in the wider hierarchies that structure the contemporary creator economy. It critically analyzes ‘who can’ participate in regimes of algorithmic visibility on leading social media platforms and- -conversely—'who can not’. The second paper further develops this inquiry, studying how people of size grapple with their datafied presence online and develop strategies for safely navigating, what the author calls, algorithmically mediated hyper(in)visibility. Focusing on the Body Positive Movement (BPM) on TikTok, the paper discusses how people of size must not only manage discriminatory human responses to their physical bodies, but also harmful machine responses to their data bodies. The third paper, which examines performers on webcam sex platforms, is concerned with the tension between visibility and safety. It shows how these performers engage with visibility beyond that provided by platform algorithms, while simultaneously seeking invisibility, where possible. Finally, the fourth paper—drawing on an interface analysis of 50 high-traffic webcam sex platforms—analyzes the information and affordances available to these performers. Critically exploring the platform-creator information asymmetry, it reflects on how performers can navigate visibility and revenue opportunities on webcam platforms. By bringing together studies on historically marginalized creators, this panel destabilizes the binary of platform (in)visibility. At the same time that the panelists show how platforms shape the kinds of (in)visibility afforded to precarious laborers, the panelists also show how they seek to resist this erasure and challenge the vulnerabilities of being “seen.” Highlighting that these creators’ vital resistance to erasure gives rise to various vulnerabilities not faced by those groups we already always ‘see’. References Banet-Weiser, S., & Miltner, K. M. (2016). #MasculinitySoFragile: Culture, structure, and networked misogyny. Feminist Media Studies, 16(1), 171–174. Cotter, K. (2019). Playing the visibility game: How digital influencers and algorithms negotiate influence on Instagram. New Media & Society, 21(4), 895–913. Cunningham, S., & Craig, D. (2019). Social Media Entertainment: The New Intersection of Hollywood and Silicon Valley. In Social Media Entertainment. New York University Press. Duffy, B. E. (2020). Algorithmic precarity in cultural work. Communication and the Public, 5(3-4), 103-107. Gillespie, T. (2018). Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions That Shape Social Media. In Custodians of the Internet. Yale University Press. Jones, A. (2015). Sex Work in a Digital Era. Sociology Compass, 9(7), 558–570. Marwick, A. E. (2015). Instafame: Luxury Selfies in the Attention Economy. Public Culture, 27(1 (75)), 137–160. Poell, T., Nieborg, D. B., & Duffy, B. E. (2021). Platforms and Cultural Production. Polity. Van der Nagel, E., & Frith, J. (2015). Anonymity, pseudonymity, and the agency of online identity: Examining the social practices of r/Gonewild. First Monday. Van Doorn, N., & Velthuis, O. (2018). A good hustle: The moral economy of market competition in adult webcam modeling. Journal of Cultural Economy, 11(3), 177–192. THE PURSUIT OF PLATFORM VISIBILITY: ALGORITHMIC LABOR AND CAPITAL IN THE CREATOR ECONOMY Brooke Erin Duffy Cornell University Colten Meisner Cornell University In late 2021, when UK-based analytics firm MIDiA Research predicted that 2022 would emerge as the “Year of the Creator” (Mulligan, 2021), it was perhaps an expression of industrial self-interest. After all, as an intermediary in the entertainment industry, MIDiA has vested economic stakes in forecasting the landscape of digital media markets. But such prognostications also attest to the continued ascent of the creator economy, wherein platform-dependent artists, entertainers, gamers, and influencers are reconfiguring the values and logics of cultural production (Cunningham & Craig, 2019, Poell et al., 2021). Both within and beyond the internet studies community, research into these creator subcultures and creative practices abounds (e.g., Abidin, 2016; Bishop, 2020; Meisner & Ledbetter, 2020). Collectively, this work illuminates how various social actors may enable or constrain structures of success or—perhaps more aptly—visibility. In this presentation, we present data from in-depth interviews with 30 social media content creators to explicate the role of algorithmic labor and capital in the platforms’ visibility hierarchies and, consequently, the wider creator economy. To be sure, studies of gig workers (e.g., Rosenblatt & Stark, 2016) and creative entrepreneurs (Klawitter & Hargittai, 2018) have helped draw attention to the lopsided nature of algorithmic knowledge systems; workers’ relationships--both to each other and to the platforms on which they rely for income--are staggeringly asymmetrical. Studies of particular creator- influencer communities, including those vying for audiences on Instagram (Cotter, 2019); YouTube (Bishop, 2020; Caplan & Gillespie, 2020) and TikTok (Duffy et al., 2020), among others, highlight some of the algorithmic dynamics and inequities distinctive to platform-dependent creator communities. Indeed, many creators and aspirants are impelled to engage in what Cotter (2019) productively describes as the “visibility game,” wherein participants are “acutely aware of algorithmic power and [strategically] pursue visibility”--often by working in concert (p. 908). Our study, which draws upon interviews with content creators representing socially marginalized groups, demonstrates that the “algorithmic game” is likely rigged against non-normative creators and cultural expressions. Methods Empirically, we draw upon a secondary analysis of interview data that we compiled for a study of platform governance and resistance in the creator economy (Authors, under review). During the summer of 2021, we interviewed 30 part- or full-time social media content creators about their experiences with platform visibility and content moderation (e.g., censorship, demonetization, shadowbanning). Informed by accounts of algorithmic bias and discrimination circulating in wider popular culture, we purposefully sampled individuals representing historically marginalized social identities (e.g., racial/ethnic minorities, women, LGBTQ+) and/or stigmatized content genres (e.g., cosplay, sex work, political art). Each of our interviewees produced content on one or more of the following platforms: Instagram, TikTok, YouTube, Facebook, and/or Twitch. Although we did not initially set out to study the knowledge and information-sharing practices linked to visibility, the topics of algorithmic labor and capital emerged as salient categories during the coding and analysis stages of research (Glaser & Strauss, 1967). Algorithmic Labor and Capital “The algorithm…is the main problem. You want your stuff to be seen by people. That way you can monetize it.” -Tristan, educational TikTok/YouTuber “If [the algorithm] ain’t showing it to no one, you ain’t getting paid.” -Caroline, Trans TikTok creator As the above interview quotes make clear, creators were keenly aware of the instrumentality of being seen and, more specifically, the role that algorithms played in the allocation of such visibility. Interviewees thus described “countless hours” devoted to algorithmic sense-making along with the felt need “to educate [themselves] on the algorithm.” Such expositions of the time and energy devoted to learning and sharing information about platforms’ (perceived) ranking and optimization systems underscores the requirements for algorithmic labor (Rosenblat & Stark, 2016). Of course, creators were not just oriented toward achieving visibility; they also sought to stave off punishments that might render them invisible. Many thus invested time in strategies believed to evade platform and/or algorithmic “punishments” (i.e., shadowbanning, censorship, demonetization). While trans creators detailed the use of coded terms like “seggs” (instead of “sex”) or “g@y” (instead of “gay”) in hashtags, others managed multiple professional accounts in anticipation that any of them could “get banned suddenly without warning.” As such, we contend that the algorithmic laboring demands of the creator economy are ramped up for those communities most likely to be deemed “controversial” by platforms’ regulatory systems. Not only do platforms’ hazily defined guidelines increase the labor for particular communities but, moreover, markers of capital and privilege shape algorithmic knowledge structures in disparate ways. Some of our interviewees admitted to being able to leverage privileged insider information or social capital--much of which amounts to algorithmic imaginaries (Bucher, 2017). While one creator described themselves as “super lucky to be able to have a contact” at TikTok, another discussed how their degree in software engineering afforded them insight about “how an algorithm works.” The majority of our participants, however, lacked access to such insider information and had little recourse when faced with punitive platform action. As one of our interviewees, a queer artist, put it,“ algorithms are built by people and tech is still largely cis, white males, and they code their biases.” In the end, then, we consider how the pursuit of platform visibility among content creators is shaped by wider systems of inclusion and--more pointedly--exclusion. References Abidin, Crystal. "Visibility labour: Engaging with Influencers’ fashion brands and# OOTD advertorial campaigns on Instagram." Media International Australia 161.1 (2016): 86- 100. Bishop, S. (2020). Algorithmic experts: Selling algorithmic lore on YouTube. Social Media + Society, 6(1), 1-11. https://doi.org/10.1177/2056305119897323 Bucher, T. (2017). The algorithmic imaginary: Exploring the ordinary affects of Facebook algorithms. Information, Communication & Society, 20(1), 30-44. https://doi.org/10.1080/1369118x.2016.1154086 Caplan, R., & Gillespie, T. (2020). Tiered governance and demonetization: The shifting terms of labor and compensation in the platform economy. Social Media + Society, 6(2), 1-13. https://doi.org/10.1177/2056305120936636 Cotter, K. (2019). Playing the visibility game: How digital influencers and algorithms negotiate influence on Instagram. New Media & Society, 21(4), 895-913. https://doi.org/10.1177/1461444818815684 Cunningham, S., & Craig, D. (2019). Social media entertainment. New York University Press. Duffy, B. E., Pinch, A., Sannon, S., & Sawey, M. (2021). The nested precarities of creative labor on social media. Social Media+ Society, 7(2), 20563051211021368. Glaser, B. (1992) Basics of Grounded Theory Analysis (California, Sociology Press). Klawitter, E., & Hargittai, E. (2018). “It’s like learning a whole other language": The role of algorithmic skills in the curation of creative goods. International Journal of Communication, 12(21), 3940-3510. Meisner, C., & Ledbetter, A. M. (2020). Participatory branding on social media: The affordances of live streaming for creative labor. New Media & Society. Advance online publication. https://doi.org/10.1177/1461444820972392 Mulligan, M. (2021, December 20). 2022 MIDiA predictions: the year of the creator. Retrieved from: https://www.midiaresearch.com. Poell, T., Nieborg, D. B., & Duffy, B. E. (2021). Platforms and cultural production. John Wiley & Sons. Rosenblat, A., & Stark, L. (2016). Algorithmic labor and information asymmetries: A case study of Uber’s drivers. International journal of communication, 10, 27. https://doi.org/10.1177/2056305119897323 https://doi.org/10.1080/1369118x.2016.1154086 https://doi.org/10.1177/2056305120936636 https://doi.org/10.1177/1461444818815684 NAVIGATING ALGORITHMICALLY MEDIATED HYPER(IN)VISIBILITY IN THE BODY POSITIVITY MOVEMENT Kelley Cotter Pennsylvania State University A key interest of the Body Positive Movement (BPM) is normalizing body diversity through making bodies of all sizes and shapes visible. This interest is complicated by what Gailey (2014) calls hyper(in)visibility. Hyper(in)visibility refers to how non- normative bodies, paradoxically, receive greater scrutiny than others (they are hypervisible), while often being overlooked (they are hyperinvisible). In the digital realm, algorithms mediate the experience of hyper(in)visibility, as they regulate who and what becomes visible. Being visible online opens people of size up to potential judgment, ridicule, and violence, but they may just as soon not be seen at all. People of size must confront platform algorithms that reinforce hegemonic perspectives (Noble, 2018), suppress content created by members of marginalized groups (Blunt et al., 2020; Noble, 2018; Smith et al., 2021) and create opportunities for hate to propagate (Daniels, 2018). The rise of platforms has produced a new plane of political struggle for the BPM: they must not only manage harmful human responses to their physical bodies, but also harmful machine responses to their data bodies (Petty et al., 2018). This study seeks to understand how people of size grapple with their datafied presence online, particularly on TikTok, and develop strategies for safely navigating algorithmically mediated hyper(in)visibility. For this, I build on Cotter’s (2019) analogy of the “visibility game” to account for the relevance of audiences’ gazes in pursuits of visibility. Visibility and Social Media While visibility of different bodies in mainstream society does not guarantee or certify access, equality, or empowerment, it does crucial work at a symbolic level. As Gailey wrote, “[v]isibility is intimately related to acknowledgment or recognition. […] To be seen by a significant other means that we exist” (2014). The historical erasure of certain bodies in media representations constitutes a kind of symbolic annihilation: (mainstream) media representations tend to omit, trivialize, and condemn non- normative bodies (Tuchman, 2000). The depiction of fat people as lazy, unattractive, and unhealthy serves to justify or legitimate direct and structural violence against them (Galtung, 1990; Prohaska & Gailey, 2019), including harassment, bullying, abuse, and violence (Gailey, 2014; Royce, 2009). Social media has afforded new opportunities for rendering non-normative bodies more visible (Lupton, 2017; Sastre, 2014), which could “open up spaces of comfort” for people of size (Hynnä & Kyrölä, 2019, p. 9). Cotter (2019) previously observed that the pursuit of visibility online resembles a game constructed around rules established by platforms and embedded in their algorithms. The visibility game captures the interplay between algorithms, platforms, and creators that shapes how the latter formulate visibility tactics. However, Cotter’s original conceptualization did not account for the role of other users in shaping how creators “play the game.” This study aims to extend the nascent body of work on the online BPM, while complicating the notion of the visibility game. Whereas the goal of the visibility game is to move from invisibility to visibility, for BPM activists online, the goal is to find a liberatory space between algorithmically- mediated hypervisibility and hyperinvisibility—to achieve their political ends while being seen with respect, dignity, and humanity. Method With TikTok’s rapidly rising popularity, the BPM has found both community and new challenges on the platform (Russo, 2022). Currently, I am collecting TikTok videos produced by BPM creators that explicitly address the platform’s FYP algorithm. To identify relevant content, on a weekly basis, I am collecting content from searches that combine sets of keywords related to body positivity and “algorithm” (e.g., “’body positive’ algorithm,” “’fat positive’ algorithm”). This data will be paired with data from interviews with 20 BPM TikTok creators, strategically sampled to represent the intersectional nature of body ideals and the struggle for fat liberation (Strings, 2019). Preliminary analysis is underway. The analysis is exploratory in nature and follows an iterative process with multiple cycles of coding, per Saldaña’s (2012) approach. While I largely employ an inductive approach, my analysis is informed by Gailey’s conceptualization of hyper(in)visibility and Cotter’s conceptualization of the visibility game. Through a round of initial coding, I construct a provisional, thematic understanding of how BPM creators make sense of and respond to the FYP algorithm vis-a-vis their visibility. In a second cycle of coding, I conduct pattern coding to identify higher-level categories and axial coding to explore how categories and subcategories relate to one another. Finally, in a third cycle of coding, I read the categories through the lens of hyper(in)visibility and the visibility game. By exploring the algorithmically mediated hyper(in)visibility of people of size, this paper will contribute a more textured accounting of the role of power relations between platforms, creators, and other users at play in pursuits of visibility online. References Blunt, D., Wolf, A., Coombes, E., & Mullin, S. (2020). Posting into the void: studying the impact of shadowbanning on sex workers and activists. https://hackinghustling.org/posting-into-the-void-content-moderation/ Cotter, K. (2019). Playing the visibility game: How digital influencers and algorithms negotiate influence on Instagram. New Media & Society, 21(4), 895-913. https://doi.org/10.1177/1461444818815684 Daniels, J. (2018). The algorithmic rise of the “alt-right”. Contexts, 17(1), 60-65. https://doi.org/10.1177/1536504218766547 Gailey, J. (2014). The hyper (in) visible fat woman: Weight and gender discourse in contemporary society. Springer. Galtung, J. (1990). Cultural violence. Journal of peace research, 27(3), 291-305. https://doi.org/10.1177/0022343390027003005 Hynnä, K., & Kyrölä, K. (2019). “Feel in your body”: Fat activist affects in blogs. Social Media+ Society, 5(4), 1-11. https://doi.org/10.1177/2056305119879983 Lupton, D. (2017). Digital media and body weight, shape, and size: An introduction and review. Fat Studies, 6(2), 119-134. https://doi.org/10.1080/21604851.2017.1243392 Noble, S. U. (2018). Algorithms of oppression: How search engines reinforce racism. NYU Press. Petty, T., Saba, M., Lewis, T., Peña Gangadharan, S., & Eubanks, V. (2018). Our data bodies: Reclaiming our data. https://www.odbproject.org/tools/ Prohaska, A., & Gailey, J. A. (2019). Theorizing fat oppression: Intersectional approaches and methodological innovations. Fat Studies, 8(1), 1-9. https://doi.org/10.1080/21604851.2019.1534469 Royce, T. (2009). 17. The Shape of Abuse. The fat studies reader. https://doi.org/10.18574/9780814777435-020/html Russo, G. (2022). For plus-size creators, TikTok presents a new wave of challenges. https://www.nylon.com/life/tiktok-body-positivity-plus-size-creators Saldana, J. (2012). The Coding Manual for Qualitative Researchers. SAGE. Sastre, A. (2014). Towards a radical body positive: Reading the online “body positive movement”. Feminist Media Studies, 14(6), 929-943. Smith, S., Haimson, O. L., Fitzsimmons, C., & Echarte Brown, N. (2021). Censorship of marginalized communities on Instagram. https://saltyworld.net/product/exclusive-report- censorship-of-marginalized-communities-on-instagram-2021-pdf-download/ Strings, S. (2019). Fearing the black body: The racial origins of fat phobia. NYU Press. Tuchman, G. (2000). The symbolic annihilation of women by the mass media. In Culture and politics (pp. 150-174). Springer. https://doi.org/10.1007/978-1-349-62965- 7_9 SHOWING/HIDING PARTS: HOW MANAGING (IN)VISIBILITY MANAGES RISK IN WEBCAM LABOR Hanne M. Stegeman University of Amsterdam Mediated visibility is a double edged sword, it comes with both risks and opportunities (Thompson, 2005). This is especially true for precarious and stigmatized creators, such as webcam performers. In this paper, based on in-depth interviews with 34 webcam performers living in the Netherlands, I explore how these creators manage the risks and opportunities associated with their (in)visibility. Webcam performers–walking the line between sex workers and influencers —livestream sexual shows for audience compensation. Economic necessity, as well as experiences of stigmatization, shape the decisions these performers make as to what parts of their identities, availability, personalities, and bodies they show online. Being seen is simultaneously dangerous and profitable, encompassing both the potential for representation and stigmatization (Theunissen & Favero, 2021). In the light of these tensions, this paper highlights how creator (in)visibility concerns more than just algorithmic ranking by platforms. Webcam performers, make individual decisions on what to show and monetize. As is the case with algorithmic (in)visibility, these choices are constrained by oppressive social and platform structures. Creators, algorithms and (in)visibility labor A lot of valuable research has already been done on how creators, ranging from social media influencers to webcam performers, engage with platform ranking-systems to increase their visibility (e.g. Cotter, 2019; Velthuis & Van Doorn, 2020). Across the creator economy, algorithmic curation tends to reproduce social inequalities. Usually only dominant socio-economic groups often actually benefit from online visibility (Duffy & Hund, 2019). On webcam platforms thin, (apparently) able bodied, white, middle-class cis women tend to populate the upper ranks (Jones, 2020). Being seen, at least in part, depends on matching dominant norms. Current research also suggests that online visibility comes with vulnerability, especially for marginalized groups (Duffy & Hund, 2019). Taking into account the sex work stigma webcam performers face (Koken, 2012), merely existing in public online space is an additional risk too (e.g., Jones, 2020; Theunissen & Favero, 2021). Simultaneously, it is clear that social media and webcam audiences demand authenticity and intimacy (e.g. Duffy & Hund, 2019; Jones, 2020). These types of online labor thus require workers to (over)share and, at the same time, to withhold information. This paper advances discussion of (in)visibility decisions made by online creators irrespective of their positioning on their platforms. I also complicate discussions of what online creators are willing to share, by showing how structures such as platforms and stigma constrain these individual choices. Webcam performers engage with visibility beyond algorithmic visibility and seek necessary invisibility, where possible, simultaneously. Webcam performers’ (in)visibility management Interviews with 34 webcam performers in the Netherlands show the importance of (in)visibility management in precarious online labor. I conducted these interviews either online or offline in performers’ homes, only after ethical approval and testing the interview guide with an experienced performer. Anonymity preferences and compensation were discussed for every interview (Jones, 2020). The analysis, in this case a thematic analysis of risk and opportunity management strategies, and the reporting of these interviews respects the visibility, pseudonymity, and anonymity decisions taken by the interviewees. Based on the analysis of these interviews, I observe that webcam performers manage their (in)visibility primarily through decisions on what parts of their personality, availability, body, and identity they want to show. Additionally, performers outlined how these choices are constrained by platforms and social contexts. Tactics for (in)visibility management include strategies familiar to influencers. When it comes to ‘personality’, decisions are made about, for instance, which life events to share with audiences (Duffy & Hund, 2019). Yet, webcam performers, like other sex workers (Jones, 2020), also construct entirely separate personas that attract clients and protect the performers’ intimate life. Management of apparent ‘availability’ is a counterintuitive measure whereby performers limit their visibility to create artificial scarcity and thus higher demand. Showing less, as interviewees maintained, often creates an incentive for clients to return to a performer. As an extension of this tactic, decisions on what parts of the ‘body’ to show can be considered both an income and risk management strategy. Many performers never show their genitals in cheaper ‘performances’ for larger audiences, not only to remain socially acceptable, but also to create a situation in which clients are willing to pay the highest price to persuade performers to do so in more expensive one-on-one shows. Similarly, a significant portion of performers do not show their face on cam to control the visibility of their ‘identity’, but this also encourages clients to buy private shows in which some performers feel more comfortable revealing more of themselves. These aspects of (in)visibility management all shape potential income and harm for performers. Strikingly, in contrast to what has been observed on social media influencers (e.g. Cotter, 2019), webcam performers were often unaware of their ranking on platforms. Thus, because of the opaqueness of ranking algorithms, they did not attempt to influence their position. The interviews also demonstrate that showing more of your identity and body was especially an option for performers with strong social networks and resources. Platforms also shaped (in)visibility decisions as some do not allow performance without showing your face or intimate acts. Finally, economic need, most strongly expressed by performers in precarious positions, shaped (in)visibility decisions. If, for example, your children need clothes for school and you earn more when revealing all of yourself - what do you do? These findings of (in)visibility labor in webcam work show the urgency of looking beyond algorithmic visibility or general efforts of creators to enhance their visibility. Instead, the four (in)visibility management tactics outlined here show how, beyond platform curation mechanisms, both platforms and social structures constrain what and who is seen. References Cotter, K. (2019). Playing the visibility game: How digital influencers and algorithms negotiate influence on Instagram. New Media & Society, 21(4), 895–913. https://doi.org/10.1177/1461444818815684 Duffy, B. E., & Hund, E. (2015). “Having it All” on Social Media: Entrepreneurial Femininity and Self-Branding Among Fashion Bloggers. Social Media + Society, 1(2), 2056305115604337. https://doi.org/10.1177/2056305115604337 Jones, A. (2020). Camming: Money, Power, and Pleasure in the Sex Work Industry. NYU Press. Koken, J. A. (2012). Independent Female Escort’s Strategies for Coping with Sex Work Related Stigma. Sexuality & Culture, 16(3), 209–229. https://doi.org/10.1007/s12119-011-9120-3 Theunissen, E., & Favero, P. S. H. (2021). Veiling the image/framing the body: The labour of enduring ephemerals in the context of trans* male adult camming practices on Chaturbate. New Media & Society, 23(4), 780–795. https://doi.org/10.1177/1461444820954189 Thompson, J. B. (2005). The New Visibility. Theory, Culture & Society, 22(6), 31–51. https://doi.org/10.1177/0263276405059413 Velthuis, O., & Van Doorn, N. (2020). Weathering winner-take-all. How rankings constitute competition on webcam sex platforms, and what performers can do about it. In D. Stark (Ed.), The Performance Complex: Competition and Competitions in Social Life (pp. 169–185). Oxford University Press. https://doi.org/10.1177/1461444818815684 https://doi.org/10.1177/2056305115604337 https://doi.org/10.1007/s12119-011-9120-3 https://doi.org/10.1177/1461444820954189 https://doi.org/10.1177/0263276405059413 WHAT IS (UN)SEEN IN THE WEBCAMMING INDUSTRY: BUSINESS MODELS, COMPANIES, SIZE, PAY Emilija Jokubauskaitė University of Amsterdam Thomas Poell University of Amsterdam How do performers generate revenue on webcam sex platforms and what information is available to them in doing so? Like work in other parts of the sex industry, camming is stigmatized and precarious (Vlase and Preoteasa 2021). This precarity is further enhanced as webcam platforms, just as social media platforms, tend to obfuscate their curation mechanisms and business models. Thus, performers only have limited insight in the economics of their work environment. This paper examines what information is available to webcam performers and what remains hidden. In other words, how is the platform-creator information asymmetry organized in the webcam sex industry? Information asymmetries have been observed throughout the gig economy. Similarly to Uber drivers (Rosenblat and Stark 2016) and social media creators (e.g. Youtube (Bishop 2020; Twitch (Partin 2020)), webcam performers experience a large gap in information available to them versus the information available at the platform operators’ disposal. Van Doorn and Velthuis (2018, p. 189) argue that such information asymmetries lead to uncertainty among performers concerning the competitive environment on webcam platforms. Most webcam platforms provide an algorithmically ranked homepage, which is largely opaque to performers and can reinforce social inequalities (Jones 2015). In addition, webcam models have to also deal with ambiguous moderation practices (Stegeman 2021). With regards to generating revenue as a performer, ranking and moderation systems are not the only elements that matter. To perform well, one also needs to be knowledgeable of: the type of show that succeeds on a platform, how many people one competes against, and what percentage of total revenue they receive. This paper examines to what extent performers have insight in these elements that shape their work environment. Exploring platform (in)visibilities Via a three-tier methodological approach, this paper examines what is (in)visible to performers in their daily work practices. This study is based on an interface analysis of 50 high-traffic webcam sex platforms, identifying their key characteristics in terms of: ownership, user base, financial compensation, interface elements related to business models, filtering mechanisms, and performers’ revenue. This analysis allows us to observe and compare the kinds of information available to performers on each platform. Particularly important in terms of revenue is the type of performance primarily hosted by the platform. Generally a distinction is made between private (a.k.a premium) brothel- like and public (a.k.a freemium) shows. Most webcammers prefer one type of performance over the other (Hamilton 2018), as each requires particular skills, offers specific revenue opportunities, and suits particular personalities. While it in principle seems easier to distinguish private from public-show platforms, our analysis suggests that such a hard distinction between business models is often difficult to make in practice. We consider seven interface elements as constitutive of freemium or premium platforms and observe that the majority of platforms contain elements of both. The blurring between private and public show business models creates an unstable environment in which performers need to constantly adapt their performances. Similar to business models, another opaque aspect of webcamming are payout rates. Large platforms rarely indicate upfront what percentage of the revenue they share with performers. To find this information, (prospective) performers often seek help on forums or community sites. Our study systematizes the payout rate information available on such sources for the 50 webcamming platforms. Here, we observe that there is frequently a wide range, sometimes larger than 30%, in payout rates listed for individual platforms, with little to no actual information on where in this range a performer may end up. We also notice huge differences in payout rates between platforms. Moreover, platforms do not always offer rationales for why they may share only 30 to 35% of the revenue, while other platforms allocate 75 to 85% to workers. Finally, most webcam platforms withhold how many performers are active and how many users visit the platform. This is a challenge for performers, who need to make an informed decision about revenue opportunities on different platforms. While each platform advertises itself as the best place to work, for performers it is difficult to identify the most popular and profitable platforms. Navigating an opaque working environment costs time and effort for performers, who are often already in a highly precarious position. As in other industries, it is in the platform’s competitive interest to obfuscate their business model, revenue sharing schemes, curation practices, and exact traffic numbers. Yet, they do so at the cost of performers. References Bishop, Sophie. 2020. “Algorithmic Experts: Selling Algorithmic Lore on YouTube.” Social Media + Society 6 (1): 2056305119897323. https://doi.org/10.1177/2056305119897323. Doorn, Niels van, and Olav Velthuis. 2018. “A Good Hustle: The Moral Economy of Market Competition in Adult Webcam Modeling.” Journal of Cultural Economy 11 (3): 177–92. https://doi.org/10.1080/17530350.2018.1446183. Hamilton, Steve. 2018. “Freemium vs. Premium Cam Sites.” XBIZ, October 10, 2018. https://www.xbiz.com/features/238718/freemium-vs-premium-cam-sites. https://doi.org/10.1177/2056305119897323 https://doi.org/10.1080/17530350.2018.1446183 https://www.xbiz.com/features/238718/freemium-vs-premium-cam-sites Jones, Angela. 2015. “For Black Models Scroll Down: Webcam Modeling and the Racialization of Erotic Labor.” Sexuality & Culture 19 (4): 776–99. https://doi.org/10.1007/s12119-015-9291-4. Jones, Angela. 2020. Camming: Money, Power, and Pleasure in the Sex Work Industry. New York: NYU Press. Partin, William Clyde. 2020. “Bit by (Twitch) Bit: ‘Platform Capture’ and the Evolution of Digital Platforms.” Social Media + Society 6 (3): 2056305120933981. https://doi.org/10.1177/2056305120933981. Rosenblat, Alex, and Luke Stark. 2016. “Algorithmic Labor and Information Asymmetries: A Case Study of Uber’s Drivers.” International Journal of Communication 10 (27): 3758–84. https://doi.org/10.2139/ssrn.2686227. Stegeman, Hanne Marleen. 2021. “Regulating and Representing Camming: Strict Limits on Acceptable Content on Webcam Sex Platforms.” New Media & Society, November, 14614448211059116. https://doi.org/10.1177/14614448211059117. Vlase, Ionela, and Ana Maria Preoteasa. 2021. “Flexi(Nse)Curity in Adult Webcamming: Romanian Women’s Experiences Selling Digital Sex Services under Platform Capitalism.” Gender, Place & Culture 0 (0): 1–32. https://doi.org/10.1080/0966369X.2021.1878114. https://doi.org/10.1007/s12119-015-9291-4 https://doi.org/10.1177/2056305120933981 https://doi.org/10.2139/ssrn.2686227 https://doi.org/10.1177/14614448211059117 https://doi.org/10.1080/0966369X.2021.1878114