Abstract
Webcam sex platforms represent an important sector of the creator economy. Yet, there is a great scarcity of research on revenue flows and performer income. To address this, we collected and analyzed a dataset comprising 2 weeks of tips received by performers on Chaturbate, a prominent camming platform. Our findings reveal that income is highly unevenly distributed, with only a small group of mostly female performers collecting earnings resembling a living wage. Countering the commonly held notion that algorithmic front-page visibility is the main indicator of performer “success,” we demonstrate that “alternative visibility” modes are sustainable and performers find their niche audiences. However, they often precariously rely on a few generous tippers, that is, “whales.” We conclude that while various pathways to financial success exist, performers remain reliant on platform affordances and audiences they have little control over.
Keywords
Introduction
Online platforms have become an increasingly central component of the contemporary labor market, reflecting broader trends in the current unstable economic and political climate (Scholz, 2016; Srnicek, 2016). A recent survey by the Pew Research Center reveals that 16% of Americans have engaged in work or tasks through online platforms (Anderson et al., 2021), highlighting the growing role of these platforms in diverse labor activities. Online content creation on platforms like YouTube, Twitch, or OnlyFans has come to serve as a primary source of livelihood for some workers and their families. For others, it offers supplementary income, opportunities for fame, or personal fulfillment (Anderson et al., 2021). Webcam streaming is a prominent sector within the creator economy. It experienced significant growth during the COVID-19 pandemic due to the forced transition of many in-person sex workers to digital platforms and the search for additional income sources by others (Rubattu et al., 2023). However, the opacity of platform operations results in significant gaps in our understanding of the income dynamics faced by these “platform-mediated workers” (Jarrett, 2022), as well as the revenue streams of the platforms and the competitive conditions they create. This article investigates large-scale income data, tying it to visibility on the platform.
Journalistic coverage of the adult webcam industry often highlights rare instances of exceptionally high earners, citing figures like $1000 per day (Business Insider US, 2020) or £150,000 annually (Myall, 2018). However, these figures are misleading, as demonstrated by Jones (2020), who surveyed 105 webcam performers and found a median monthly income of $1000, with only 12 out of 105 performers earning $10,000 or more in a month. Nevertheless, small-sample surveys and ethnographic research, which dominate the literature, cannot reliably estimate income across an industry with hundreds of thousands of content creators (Bellu, 2023; Jokubauskaitė et al., 2023). This challenge is further complicated by the presence of sporadic users who, despite comprising a significant portion of the workforce and participating in the same competition pool (Jokubauskaitė et al., 2023), are often less accessible to researchers. To more accurately assess the precarity of webcam work and hold platforms accountable, large-scale studies on income distributions are essential but remain largely uncharted.
Interviews or “thick” ethnographic descriptions offer crucial insights into performers’ lived experiences. However, these qualitative methods cannot be scaled to capture the broader mechanisms of platform economics and are insufficient as a comprehensive response to the “sexual datafication” (Saunders, 2020: 58) by platforms. The industry operates in a competitive, non-monopolistic environment (Jones, 2020), where disclosing information about revenue models, performer income distributions, ranking algorithms, and other technical elements could jeopardize business strategies. Consequently, collecting reliable data is challenging since platform representatives are reluctant to share information beyond PR-driven narratives (Harvey, 2010). Meanwhile, platform workers must navigate these opaque conditions while trying to make a living under uncertain and often vague platform regulations, limiting their ability to make informed decisions as “independent contractors” (Rosenblat and Stark, 2016).
Our article focuses on Chaturbate, one of the largest and most popular webcamming platforms (Xavier, 2024; Jokubauskaitė et al., 2023; Similarweb, n.d.). Chaturbate hosts livestreamed content, similar to its mainstream counterpart Twitch (Ruberg, 2022). Yet, most of Chaturbate’s streams are sexual or erotic. Its homepage greets the visitors with a seemingly endless catalog of sexually explicit stream thumbnails, along with the slogan “the act of masturbating while chatting online.” Unlike premium platforms such as LiveJasmin or Streamate, where viewers pay per minute, Chaturbate uses a freemium model: streams are free to watch, and performers earn income from tips often linked to specific requests, donation goals, or interactive sex toys. In contrast to previous research on income in the industry, we gathered a detailed data set of tips in these streams and analyzed their connection to performer visibility on the platform. We ask: how closely are visibility and income related?
Front-page visibility and viewership are often used as proxies for income in webcam research when direct data is unavailable (Jones, 2015; van Doorn and Velthuis, 2018; Velthuis and van Doorn, 2020; Jokubauskaitė et al., 2023). Algorithmic ranking systems are then frequently perceived as distributing income opportunities among performers. On Chaturbate, higher rankings are generally associated with larger audiences, in turn increasing the potential for tips (Hernández, 2020; Jokubauskaitė et al., 2023). Nonetheless, is financial success solely tied to front-page visibility? Industry sources, for example, suggest that due to freeloading, cultivating a smaller, dedicated customer base of “regulars” may be more lucrative than simply aiming for higher rankings (CB Explorer, 2021). Our analysis considers such claims, examining the complex relationships between visibility, viewership, and tips to provide a nuanced understanding of these dynamics.
We argue that uncovering the income dynamics of an industry previously often portrayed as high earning and low effort can benefit the existing aims of workers and activists to improve conditions not only within online sex work but also across other platformized industries. Not enough attention has also been paid to the financial impact of these large platforms—which we address by providing an estimate of the platform’s yearly revenue. The organization of this article is as follows: we first present literature on income generation strategies, visibility, and platform affordances in webcamming and other creative industries. Next, we introduce our empirical approach and discuss its ethical implications. The findings section is divided into two parts, covering income distributions, the relationships between visibility and tips, including what we consider “alternative visibility,” and niche-streaming and dependency on large tippers. We end the article by contextualizing our findings and discussing the implications for performers.
Income and visibility in webcam sex work
Online platforms like Meta, Uber, YouTube, and also Chaturbate organize a vast array of “creative and technical workers in the social media economy such as influencers, cammers, beauty bloggers, and live-streamers” (Jarrett, 2022: n.p.). These independent contractors work on “lean platforms” (Srnicek, 2016), which do not own products or physical infrastructure, but instead “broker relationships between creators and clients” (Jarrett, 2022: n.p.). Online sex work, facilitated by platforms like OnlyFans, Chaturbate, LiveJasmin, or Pornhub, also falls under this category (Nayar, 2021; Rand and Stegeman, 2023; Ruberg and Brewer, 2022; Swords et al., 2023), despite being long excluded from the discourse due to the “continued marginalization of sex work” (Nayar, 2021: 160). Online adult creators share precarity, high levels of aspirational and relational labor (Rand, 2019; Stardust, 2019; van der Nagel, 2021; Vlase and Preoteasa, 2021; Ye et al., 2023), and used (in)visibility strategies (Stegeman et al., 2024) with other workers in the platform economy.
While webcamming has recently garnered academic attention, very little is known about revenue flows in this industry. As previously mentioned, Jones (2020) reported a median monthly income of 1000 USD among 105 surveyed webcam performers on various sites. On AdultWork, a UK-based sex work advertising platform, webcam performers and phone workers reported an average annual pay of less than 20,000 GBP (Sanders et al., 2018). Chaturbate, the focus of this article, claims an average performer makes 500 USD a week (Chaturbate, 2023a), though we will demonstrate this figure to be misleading. Industry review sites like Ready Set Cam (n.d.) suggest it is “possible to earn 30 to 60 USD an hour” on Chaturbate as a female, trans, or couples streamer, with male performers said to earn slightly less—20 to 50 USD. However, these estimates are often unreliable, based on rather limited sample sizes, anecdotal evidence, or influenced by platform promotion.
The private companies behind most online platforms are not obligated to share data on their revenue or income distributions, making large-scale data on financial transactions notoriously difficult to collect and often only accessible via computational methods or data leaks. Consequently, systematic reviews of income flows in the creator economy are exceedingly rare. Notably, studies on the membership platform Patreon, based on leaked historical data from 2013 to 2015, revealed that only the top 1% of creators earned substantial income, while the majority raised negligible amounts (Regner, 2021; Sanyoura and Anderson, 2022). But Patreon’s (n.d.) restrictive adult content guidelines push these professionals toward sex-friendlier alternatives such as the well-known fansite OnlyFans. In addition, Patreon’s model of long-term patronage differs significantly from many other gig-based types of creator labor, such as webcamming.
Other research on the creator economy often uses popularity metrics, such as viewership numbers, as a proxy for income. For example, Rieder et al. (2023) estimated income distributions among YouTube creators based on video views. Yet, the business models of mainstream and adult platforms differ significantly. On platforms like YouTube, income is primarily ad-driven, depending on the number of viewers exposed to ads during a creator’s video. Conversely, most advertising networks exclude adult content sites (Zhyliaev, 2023) to avoid association with such industries; therefore, most creator income on these platforms comes from direct viewer payments. Since on freemium webcam sites every viewer is a potential tipper, the parallel between income and audience size has been drawn before (Jokubauskaitė et al., 2023), albeit carefully due to the known phenomenon of freeloading. Chaturbate, praised for its “good traffic” (Jones, 2020), reportedly had only 1 in 200 viewers who tip (Velthuis and van Doorn, 2020). Nonetheless, higher viewership has been shown to correlate with higher donation-based income on Twitch—a non-adult game streaming site (Houssard et al., 2023). Thus, while viewership and income are likely connected on Chaturbate, the exact relationship remains unclear without detailed investigation.
In webcamming, as with many other online platforms, the opportunities to attract an audience are organized by various affordances, with algorithmic front-page ranking systems often seen as central to performer success. Since “[w]hether to be chosen for purchase or to be available for “a match” one must be seen” (Stark and Pais, 2021: 57), the topmost, highly visible positions on the first page are desirable for those seeking a large audience. Indeed, a higher-ranking position on the front page has been shown to correlate with a more substantial audience (Jokubauskaitė et al., 2023). Platform workers, therefore, strive to be “algorithmically recognizable” (Gillespie, 2017) by the automated system that evaluates their productivity for the platform based on consumer behavior, including clicks, spending, ratings, and other generated data. Such systems often result in unequal distributions of attention (Hindman, 2009), particularly for scalable goods and services, as in the case of freemium webcam shows.
Some researchers have commented on the inequalities in ranking based on race and ethnicity (Jones, 2015) as well as other “axes of difference,” such as gender and age (Caminhas, 2022). Others found skewed visibility distributions on Chaturbate, resulting in a “winner-take-all effect” (van Doorn and Velthuis, 2018; Velthuis and van Doorn, 2020; Jokubauskaitė et al., 2023). In terms of visibility, the well-known “rich get richer” phenomenon leads to a small group of performers consistently being ranked high and attracting significantly more viewers, while others never reach the front page and have small audiences. On Chaturbate specifically, this effect has been observed to be very prominent—Jokubauskaitė et al. (2023:10) describe its algorithmic ranking environment as having a “high-risk, high-reward atmosphere”. Among its almost 200,000 observed performers, less than 3% ranked on the front page at least once during an 11-week period. However, the actual monetary rewards of such visibility in the camming realm have yet to be analyzed.
In the stigmatized and marginalized adult work domain, high visibility is not always sought after due to potential security and other risks. For instance, creators of sexual content are concerned about being “outed,” facing employment issues, experiencing physical aggression, or unwanted attention online and offline if they become (hyper)visible (Stegeman et al., 2024). Some webcam performers thus avoid front-page visibility and assert that “resisting platform visibility logics can help them monetize their labor” by creating scarcity and being visible only to the right audience (Stegeman et al., 2024: 8). This tendency is also observed in the wider creator industries, where marginalized creators often choose to “communicate with a smaller and more invested subsection of their audience, while avoiding negative and unproductive audience interactions on public platforms” (Glatt, 2023: 11). In webcamming, Angela Jones (2019:9) explored the strategy of BBW (“big beautiful women”) performers to stream on niche sites to “reach an audience that loves fat women”, but they also reflect on the limitations described by streamers—namely, the lack of traffic and a wider well-tipping audience.
Qualitative sex work research also highlights the importance of regulars or “whales” to a performer’s income (Hernández, 2020; Jones, 2020; van Doorn and Velthuis, 2018). The term “whale” originates from the video game industry, referring to players who spend significant amounts of money on “freemium” content (Nieborg, 2015). Focusing on finding and maintaining relationships with well-tipping regulars may be one approach to “strategic invisibility” (Stegeman et al., 2024). Since sex workers “can and do also make use of affordances in unexpected and subversive ways to promote their interests” (Rand and Stegeman, 2023: 2112), it is essential to investigate if and how webcam performers may overcome the income-making challenges posed by skewed visibility distributions on platforms like Chaturbate. Therefore, systematic insight into visibilities beyond the front page of webcam platforms is needed to complicate our understanding of the significance of algorithmic ranking with regard to income in the (adult) platform economy.
Taking Chaturbate as a case study of a large and popular adult creator labor platform, this analysis examines the complex relationships between visibility, viewership, and income from tips. Leveraging a large-scale quantitative dataset and methods informed by fieldwork, we go beyond the straightforward relationships between these variables to investigate alternative forms of visibility that webcam performers may access. If only a very small number of performers achieve front-page visibility and significant viewership, what about the rest? Can we observe platform matchmaking through alternative visibilities related to specific content, such as specialized tags on the platform? In addition, can alternative visibilities beyond the front page compensate for the lack of mainstream visibility on the platform?
Dataset and methods
To answer these questions, we rely on viewership and homepage position as metrics of mainstream visibility on the platform, whereas filtering by tags and gender categories allows us to investigate alternative ways performers may be visible. To assess performers’ tip earnings, we collected data on all user donations over a 2-week period. Our quantitative approach allows us to investigate income distributions at scale and map platform-wide interactions between the mentioned variables. Nevertheless, we deemed it important to embed the quantitative analysis in the relevant industry context. Therefore, this article is also informed by fieldwork conducted at industry events and interviews with platform representatives between 2020 and 2024.
The necessary data for our approach could not be obtained from Chaturbate through data release requests or a comprehensive randomized-sample survey of its large performer base with many sporadic streamers (Jokubauskaitė et al., 2023; Jones, 2020). Deriving from “the methods of the medium” (Rogers, 2013), we, therefore, appropriated Chaturbate’s affordances—particularly the live chat’s centrality for income making—as the basis of our analytical approach. We used a custom Python-based tool to collect data over 2 weeks, between 11 and 25 July 2022. During this period, we captured data on the entire performer population and their streaming sessions by fetching a list of all live streams every 2 minutes. This list, or “catalog”, is provided as a JSON file by the platform as part of an API for “white label” sites, that is, affiliates that host (some of) Chaturbate’s content under their own brand. It contains various metadata about the stream, including titles, self-reported gender categories, tags describing show content, front-page ranking positions, and viewer counts at the time of retrieval.
Each live performer additionally hosts a publicly visible text chat alongside their video stream, where viewers can interact with the performer through messages, emoticons, and tips. At the time of data collection, the chat used SockJS, an open-source and well-documented web technology. 1 We connected to each live performer’s chat listed in the catalog and recorded incoming tips, saving them into a database. Our analysis is based on this dataset of all tips during the observation period, combined with catalog metadata.
The stream metadata was also used to determine the start and end of streaming “sessions,” that is, periods within which performers were online continuously. We defined a session as continuous streaming for at least 6 minutes (three data collection intervals). Once the performer is offline for at least 16 minutes (eight intervals) the session is considered ended. We identified 123,674 distinct performers and 956,662 streaming sessions, averaging about eight sessions per performer. We employed descriptive statistics, correlation analysis, and exploratory data visualizations to evaluate data distributions, relationships between variables, performer clustering by co-donation, and the most popular and profitable tags.
Chaturbate performers can block access from particular geographic locations. Our data collection respected these blocks, representing the performer population as seen from The Netherlands, where we are based. Tips made within the first 2 minutes of a performer starting to stream may not be captured due to our data collection interval. However, such early tips are rare and unlikely to affect our findings.
Ethical considerations
Our case study does not come without ethical concerns, especially due to the continuous stigmatization and marginalization of the sex worker community. Particularly, we are aware of the data safety and privacy concerns as well as questions of exploitation related to automated online data collection. Nevertheless, we argue that the “birds-eye view” approach we offer is essential to begin to understand the opaque socio-technical structures faced by performers. We also believe that large-scale analysis of powerful platforms can “use data against injustice” (Keilty, 2023: n.p.) and benefit worker communities when conducted with an “ethic of care” (Tronto, 1998).
By offering insights into the outcomes of algorithmic management, this study may provide more leverage in advocating for better working conditions both within the platform and at various governmental levels. Our work also offers insight into the scale and distribution of performer earnings along the lines of different tip-generating approaches. In addition, our estimation of platform earnings in a highly secretive industry is unprecedented. This can give actionable information to performers without imposing additional “intellectual, practical, or emotional labor” (Phipps, 2015) on marginalized groups. The principal findings of this article will therefore be published in a report aimed at sex workers, activists, and their organizations.
Given the scale of data collection from over a hundred thousand performers—each with an audience that might number in the thousands in itself, obtaining informed consent is “manifestly impracticable” (Franzke et al., 2020: 10). To mitigate risks, we adhered to a strict ethics protocol, ensuring dignified and secure data collection, storage, and dissemination. Taking users’ expectations of privacy into consideration (Franzke et al., 2020: 10), we focused solely on the information available through the platform’s interface, excluding any visual or private-show data, and only present aggregated findings without any identifiable information. Decisions regarding data collection, encryption, storage, anonymization, 2 sharing, and presentation were continually evaluated to minimize risks to the streamer population. The research plan was approved by the Ethics Committee of the Faculty of Social and Behavioral Sciences at the University of Amsterdam. 3
Findings
We examine the intersection of income and visibility on the live-streaming platform Chaturbate, focusing on three key areas: the distribution of income among performers, including variations by gender categories and streaming duration; the relationship between income, front-page visibility, and show viewership; and, finally, the profitability and reliance on “whales” within “alternative” visibilities. These aspects are addressed in two sections.
Income distributions
Our findings indicate that Chaturbate facilitates extensive financial activity. Over only 2 weeks, users tipped between 24 and 33.5 million USD, based on the total 305,212,623 4 tokens tipped, each one costing them 0.08 to 0.11 USD (Chaturbate Token Calculator, 2023). We can thus extrapolate the platform’s income to be roughly between 624 and 871 million USD in a year only from tips. Out of that, performers receive 0.05 USD per token (Chaturbate, 2024), equating to a payout rate between 45% and 62%. In the observed 2 weeks, this amounted to about 15 million USD distributed to 123,674 5 performers across 956,662 stream sessions.
In line with other observations about creator labor (Houssard et al., 2023; Rieder et al., 2023), these tipped tokens were highly unequally divided among performers. The majority of the performers received little to no total tips, while a few high-earners took large sums (Figure 1). Specifically, over 69% of all tips went to the top 10% of performers, while the lower half made less than 1% (Figure 2). Even within the top 1% of earners, distribution was strongly skewed, with earnings ranging from 1600 to over 20,000 USD.
Distribution of total performer income on Chaturbate (USD).
Gender, income, and work hour metrics among five income groups based on quartiles, median, 10th, and first percentiles of total tips received.
The mean weekly earnings among all performers were 61.70 USD, but they varied widely (SD = 8053.2), and the median earnings were much lower—only 11.45 USD. These averages are far below the 500 USD weekly income the platform advertises in its introductory video for “independent broadcasters” (Chaturbate, 2023a). In fact, only 2.2% of performers crossed the 500 USD per week threshold. In addition, more than 66% of all performers did not reach the minimum 50 USD 6 required to withdraw earnings at the end of 2 weeks (Chaturbate, 2023b). Hourly income also varied widely: between 0 and 2,612.06 USD per hour, with a mean of 5.34 USD (SD = 20.55 USD per hour) and a median of only 0.99 USD per hour. These findings are far below the minimum hourly wage in the United States (7.25 USD), where the company is based, but close to those in Colombia (2.03 USD) and Romania (4.58 USD), two countries with large webcammer populations (Bellu, 2023; YNOT CAM, 2022).
Self-determined gender categories may offer one explanation for the very low average earnings—we found substantial differences in income among gender groups, especially between those streaming under the “male” label and the rest (see Figure 2). “Trans” performers made the largest median hourly income of 2.78 USD, followed by “female” performers with 2.27 USD per hour, “couples” with 1.9 USD per hour, and “male” performers with a median hourly income of zero USD. Notably, 53% of male-labeled streamers received no tips during the 2 weeks, compared to only 3.4% in the similarly sized 7 female-labeled group.
This disparity may be due to a smaller audience interested in male streams, but it may also reflect that “distinct performer subcommunities have varied platform uses and audiences” (Jokubauskaitė et al., 2023: 10). “Exhibitionist” streamers, who perform for reasons other than monetary gain, are a known group within the online sex work industry (Martins, 2019). Fieldwork conversations indicate that some clients, mostly male, stream themselves hoping to be noticed by female performers. Historically, Chaturbate stood out from its competitors by hosting an “exhibitionist” category for unverified streamers, but it was removed due to stricter age-verification regulations, imposed after 2021 (Hony, 2021; Franco, 2024). However, it is likely that such streamers still use the site without seeking (substantial) monetary gains.
This proposition is further substantiated by the performers’ activity on the platform. As indicated in Figure 2, the lower half of the performers (the very low income group) spent a median of only about 2 hours live during the 2 weeks, and a mean of nearly six. In addition, nearly a third of all performers (38,776) streamed only once or twice in that time. Although the observation period was brief, this indicates sporadic platform use or high turnover, which has been reported by the performers themselves (Jones, 2020) as well as observed previously (Jokubauskaitė et al., 2023). Interestingly, those we labeled low earners spent considerable time on the platform but still earned very little in total and hourly. This quarter of the performers generally could be considered part-timers, or those dipping their toes into webcamming, but (so far) not making notable earnings.
For the remainder of the article, we focus mostly on those we consider professionalized performers. These are the top 25% of all earners on the platform (30,909 streamers), with a total tip income of at least 95 USD. Collectively, they earned over 90% of all income on the platform and streamed a median of 39.9 hours over 2 weeks, or roughly 4 hours a day, excluding preparations and other creator labor. This group identifies as “female” more frequently than the average performer, with 71.1% using this label. This indicates a feminized workforce similar to that described on other platforms (Jokubauskaitė et al., 2023; Rand and Stegeman, 2023) and in the industry more broadly.
Among these professionalized streamers, the link between front-page ranking position, viewership, and income is apparent (Figure 3, heatmaps B and D). Financially successful performers at the top of each heatmap reach high positions and attract many viewers, while the mentioned low and very low earners are left with little visibility and audiences of fewer than 20 (at the bottom of heatmaps A and C). Correlation analysis also aligns with such general tendencies: performers’ total earnings correlate with their mean front-page position (r = −.34 8 ) and mean audience size (r = .44). Nonetheless, the relationships are not perfectly linear and present complexities.
Relationships between the highest-ranking position achieved and total tips received (A&B), and between average viewer numbers and total tips received (C&D). 9
For instance, the highest position achieved by a show had a weak correlation with total tips (r = −.22), but the time it spent on the front page 10 correlated strongly (r = .46), suggesting that while top-ranking positions are beneficial, their impact on earnings is less significant than sustained visibility. Audience size also strongly correlated with the number of distinct tippers (r = .59), regardless of their donation size. It appears that the number of tippers does not “cap” at a certain number in each stream, but a larger audience still means more people who tip and thus a larger income for the performer.
In the next section, we zoom in on the group of performers who make a significant income but fall outside of the high-visibility group, specifically those in the mid-density areas of heatmaps B and D (Figure 3). These performers achieve substantial income despite average rankings below the top 1000, meaning they do not appear near the platform’s front page. Nevertheless, they manage to attract a sizable or tipping-prone audience through alternative means. We term this phenomenon “alternative visibility”—a form of visibility not derived from algorithmic homepage ranking but from other strategies and platform features.
Alternative visibility and whale tippers
The previous section showed strong winner-takes-all tendencies among the income of Chaturbate’s performers and offered a few possible interpretations, including the relationships to front-page visibility and audience size. Nevertheless, this article seeks to complicate the visibility–income link and explore whether appearing on the front page is the only way to make significant income while webcamming. To do so, we propose the notion of alternative visibility.
Some forms of alternative visibility may stem from the broader platform ecosystem that adult and mainstream creators utilize to promote their content (Poell et al., 2021: 111) and “build personas and brands to engage consumers on a level beyond sexual content” (Swords et al., 2023: 288). For example, many webcam performers use social media to announce upcoming shows or stream less explicit content on platforms like Twitch (Lifehacker International, 2017). While a comprehensive survey of all alternative visibility sources is outside this article’s scope, we focus on platform-native elements that facilitate visibility beyond homepage ranking. Specifically, this section explores gender labels and customizable tags that help performers reach viewers through filters or affiliate sites, featuring such content. We also investigate performers’ dependency on whale tippers and how it differs based on their visibility.
For an overview of alternative versus front-page visibility, including niche-streaming, we mapped the top 25% of earners on a co-donation network (Figure 4). The network was clustered based on tipping relationships: streamers (nodes) appear closer if more users tipped both of them. The node clusters therefore represent content similarities based on user tastes. The network’s center shows very well-earning performers with tips from a large, coherent group of tippers. Most of them also had algorithmic front-page visibility. Conversely, performers positioned outside this well-connected center mostly did not appear on the front page of Chaturbate. Importantly, hourly income varies across the graph, with some large earners outside the well-connected center. All the while, even the smaller nodes represent significant earnings. This further shows that front-page visibility is not always necessary for attracting well-tipping viewers.
Co-donation network graph (top 25% of earners).
To further investigate alternative visibility, we explored performers’ gender labels and show tags—visibility devices that afford filtering shows for specific content or performer descriptors. Importantly, they also coincide with co-donation clusters, especially in relation to gender labels, and are most distinct for trans (orange) and male (green) performers, while couples (purple) and female (uncolored) performers were more intertwined. This likely reflects their primary audiences and their expectations: “couple” and “female” categories mostly target heterosexual male users, the largest group of porn consumers (Miller et al., 2020). In these terms, a “couple” is a straight-presenting pair or a group engaging in heteronormative sex, while, for example, streams featuring multiple female performers appear under the “female” category. Male couples typically stream under the “male” label, targeting a gay audience. “Trans”-labeled content, while sharing tippers with other categories, appears to have a rather distinct audience.
Unlike platform-determined gender categories, tags are generated by performers, who use them in multiples simultaneously, and thus offer more refined content specifications. Users can filter performers by the provided tags 11 used to hypercategorize show content (#fuckmachine, #feet, #anal, or #mommy), performer bodies (#bigboobs, #bigass, #bigcock, #smalltits, #skinny, #curvy, #blonde, #redhead), age (#mature, #milf, #18, #young), and racial or ethnic groups (#asian, #ebony, #latina), often using derogatory terms (Stegeman et al., 2023). Co-tipping clusters on the map correspond to groups of tags, some of which are indicated in Figure 4. Examples include clusters around young male performers, shows related to feet, mature performers engaging in anal sex, or Latina performers with large breasts.
Such hyperspecificity of adult content online is not surprising and is summed up by the well-known “Rule 34 of the Internet” meme: “If it exists, there is porn of it.” The recent turn toward more niche porn has been addressed by scholars, who argue that the Internet has played a role in such content diversification (Mazières et al., 2014), popularized alternative porn and mainstreamed it (Paasonen, 2007). For webcammers, similarly to gender labels, content tags play a strategic role in audience selection (Jones, 2019). Performers often share tips on using tags to access specific niche viewers, use tools (e.g., Chaturbate Tag Picker, n.d.) to analyze tag popularity, and sometimes hijack tags unrelated to their show for extra exposure.
Beyond these strategic uses of tags and gender labels, another significant factor affecting performer income is the presence of whale tippers. Since the importance of whales has been indicated in the industry (Lil Sugar Plum, n.d.; Nayar, 2021: 171), our next analytical step was to investigate performer dependency on large tippers. To do so, we examined a user’s proportional contribution to performers’ income among those who made a significant income (top 25% of earners). We defined viewers who tip at least 20% of someone’s income as whales 12 from that performer’s perspective. This means that a performer may have up to five whales, with the withdrawal of each one significantly impacting that streamer’s income. Consequently, performers reliant on whales for at least 50% of their income were considered whale-dependent. Since the network graph (Figure 4) does not represent performers who may have fewer than 10 co-tippers with other streamers—and those would likely be the most whale-dependent—we also examined all professionalized performers with regard to the number of distinct tippers they had and the proportion of their income from these donors (Figure 5).
Proportions of all professionalized performers, divided into groups of mainstream and alternative visibility; among those: whale-dependent and independent; and, finally, by the number of whale tippers they had.
We found that, indeed, among professionalized performers with alternative visibility, over a third (35.7%) were whale-dependent, compared to only 10.5% of performers with front-page visibility. In addition, 38.42% of all income made by alternative visibility performers 13 came from whale tippers, while the same was true for only 15.69% of the total tips among mainstream visibility performers. 14 There was a negative correlation (−0.38) between the number of distinct tippers and a performer’s highest-ranking position among the professionalized streamers, suggesting that the higher-positioned performers had more distinct donors. Conversely, lower-ranked performers had more whale sponsors (r = .29). This indicates a relationship between visibility and dependency on tippers: the less visible a performer is, the more dependent they are on whales. Therefore, those visible on the front page cater to a large audience of potential tippers, whereas alternative visibility performers likely have a smaller audience of loyal and generous tippers.
Discussion and conclusion
Taking Chaturbate as a case study within the larger creator economy, our article set out to explore performer income and its ties to visibility, viewership, and whale-tippers. We gathered and analyzed the first large and detailed dataset of these platform elements to examine income distributions and their connection to visibility beyond the front page of the platform. We find that while ranking algorithms contribute to manufacturing winner-take-all conditions, front-page visibility is not the only path to significant earnings. Even though practices such as avoiding hypervisibility (Stegeman et al., 2024), niche-streaming (Jones, 2019), and attracting regulars (Hernández, 2020; Jones, 2020; van Doorn and Velthuis, 2018) have been highlighted in earlier work, we were able to link what we term “alternative visibility” with real financial outcomes.
Still, we confirm Chaturbate to be a “high-risk, high-reward” competitive environment (Jokubauskaitė et al., 2023), with only 25% of performers making non-negligible income from tips, and yet fewer earning enough to sustain themselves solely through this platform. Our findings are likely not all of the revenue performers bring in from adult content since they are known to supplement their income through splitcamming, selling their content on fansites, engaging with clients outside of livestreaming, and promoting platforms as affiliates (Jones, 2020). Besides earning more (Sanders et al., 2018), these strategies help them build their brand (Ryan, 2019) and perform authenticity (Jones, 2020). However, streamers earning more than 95 USD on Chaturbate also spent considerable time on the platform, speaking to the precarity of being an online adult creator. This contrasts with the frequently advertised large earnings and minimal time investment.
Platform specificities are crucial when discussing our findings on income disparities. The large group of performers who earn little or nothing do not all aspire to make a living out of webcamming. Similar to the broader gig economy, where some creators do it just for “having fun or having something to do in their spare time” (Anderson et al., 2021), some Chaturbate users, mostly men, likely stream for self-pleasure. This may explain the large number of male performers on this platform in an otherwise largely feminized field. More historical research into Chaturbate and other webcam platforms would be required to contextualize such findings.
When streamers who do not aim to make a living compete for the same attention pool as those seeking tips, the platform’s algorithmic management plays a key role in controlling (Möhlmann et al., 2021) which performers should be considered valuable platform contributors. The platform must balance the needs of revenue-generating complementors of the “multisided market” (Rochet and Tirole, 2003). That is, users willing to pay for specific content, and performers who will create content that fits their, often niche, tastes. This “financially productive” performer segment constitutes around a quarter of the total performer base and aligns Chaturbate more closely with its smaller competitors (Jokubauskaitė et al., 2023). Competition for the limited front-page “real estate” nevertheless remains fierce, as income distributions among professionalized streamers are still highly skewed, and time spent at the top correlates with the highest tips. Thus, “alternative visibility” practices benefit not only the performers but also the platform, which taps into the significant cut of earnings it takes from the streamers who might otherwise lack viewers.
Performers who stream outside the front page, either deliberately (Stegeman et al., 2024) or not, are akin to those specializing in premium, rather than freemium shows. Instead of attracting a large audience of “deer” 15 who jump between front-page shows, they stream to a moderate group of viewers, including a few generous tippers with whom they have established creator–fan relationships. This scenario underscores a trade-off between “hypervisibility and strategic invisibility” (Stegeman et al., 2024). Performers either rely on the opaque algorithmic ranking system for visibility and a large audience or depend on a small number of regulars. In both cases, the creator’s position remains insecure, as sudden algorithm changes can disrupt visibility or the loss of even one whale can significantly harm earnings.
Dependency on a few regular high-tippers may also affect the “relational boundaries” (Baym, 2018) performers set with clients, as balancing intimacy and distance, or work and life, becomes difficult when a significant portion of one’s income comes from a single donor. Webcam performers have previously acknowledged shifting boundaries in relation to monetary rewards and client familiarity (DeLacey, 2024), which may also apply here. The intersectionality of marginalization at play here contributes to webcammers’ “nested precarities” (Duffy et al., 2021). While webcam streamers rank higher in the whorearchy (gswer23, 2020; McNeill, 2012) than full-service and many other sex workers, they still face stigma and non-recognition of their work. Some are further marginalized due to gender expression, age, show content, or body shape, which, as seen in the influencer industry (Glatt, 2023), can heighten challenges in relational labor. For example, our findings suggest trans performers rely predominantly on alternative visibility and may be more susceptible to dependency on whales. Further research is needed to examine how marginalization, algorithmic recognition, visibility risks, social media audience size, and the likelihood of (shadow)banning (Are and Briggs, 2023) intersect to shape webcam performers’ precarities.
Our article contributes a methodological approach to studying income generation on platforms centered around live public chats with a tipping function. While developed for Chaturbate, it can serve as a blueprint for analyzing comparable freemium webcam platforms such as BongaCams or Stripchat, both of which mostly rely on public tip-based interactions. More distantly related (non-adult) livestreaming platforms like Twitch could also be analyzed following our approach, though platform-specific affordances and user practices, such as the use of third-party tipping apps, would need to be considered. Similar winner-take-all visibility distributions have been observed on other freemium webcam sites (Jokubauskaitė et al., 2023), also suggesting a potentially broader application of our findings. However, differences in performer base constitution or content categorization and algorithmic ranking systems across platforms may shape income distributions and their relationship to visibility in subtly distinct ways.
For a more comprehensive understanding of the competitive environment that webcam performers operate in, future research should analyze income flows from outside audiences versus platform-provided traffic. Mapping and investigating different types of alternative visibilities through both qualitative and quantitative analyses would provide an even more detailed understanding of income flows beyond the front page. Similarly, further analysis of tagging practices and their relationship to earnings would provide insight into the dynamics of specific niche content and its financial viability. Since intra-platform competition is situated within the non-monopolistic industry’s inter-platform competition (Velthuis and van Doorn, 2020), expanding similar income analyses to other webcam platforms would help better understand the broader competitive environment.
Footnotes
Appendix 1
Acknowledgements
The authors would like to thank Bernhard Rieder, Thomas Poell, Olav Velthuis, and two anonymous reviewers for their valuable comments on the manuscript, and Sarah Burkhardt for insights into data collection.
Funding
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek (406.DI.19.035).
