Audience size,moderator activity,gender,and content diversity: Exploring user participation and financial commitment on Twitch.tv

Abstract

User participation has long been recognized as a cornerstone of thriving online communities. Social live-streaming service (SLSS) communities are built on a subscription-based model and rely on viewers’ participation and financial support. Using the collective effort model and heuristics of social influence, this study examines the influence of streamer and viewer behaviors on viewers’ participation and financial commitment on the SLSS, Twitch.tv. Findings from behavioral data collected over 7 weeks show larger audiences diminish individual participation and financial commitment while moderation may encourage more. Female streamers benefit from increased moderation, earning two to three times more in financial commitment compared to men, who streamed more frequently and for longer durations but attracted much smaller audiences. Viewers’ participation and financial commitment did not differ across streams with more content diversity. Our results demonstrate how group factors influence individual participation and financial commitment in newer subscription-based media.

Keywords

Audience size content diversity financial commitment gender live-streaming communities moderators online communities participation SLSS Twitch

Social live-streaming services (SLSSs) are an emergent form of online multimedia communities. The global market for SLSSs is rapidly growing, with a 28.1% annual growth rate and projections to reach almost $250,000 million by 2027 (Market Research Future, 2021). One of the most popular SLSSs, Twitch.tv, reported 6.9 million monthly streamers, 2.1 million average concurrent viewers, and 1116 billion minutes of content watched in 2020 alone (Twitch Tracker, n.d.). SLSSs add intimacy to traditional social media and video spectatorship; streamers can broadcast content to small or massive audiences who in turn can interact with streamers and viewers in real time through chat messages, special gifts, and financial donations (Zimmer et al., 2018). With greater interactivity and viewer agency, SLSSs have changed how users create content and interact with one another (Kaytoue and Silva, 2012).

The success of online communities (OCs) such as SLSSs relies on user participation: users share information, help one another, and provide social connection (Yang et al., 2017). However, compared to offline communities, membership online is more ephemeral and involves much less facetime; therefore, online members are less likely to feel attached toward the community and each other (Kraut and Resnick, 2012) and may easily leave and join another community with a click of a button (Kim et al., 2008). SLSS interactions are often serendipitous as viewers can easily tune in to different streamers or “channels” for live content (Harpstead et al., 2019). Thus, it is imperative SLSSs communities attract and retain members and provide enough incentives to ensure long-term sustainability (Kraut et al., 2020). While some existing studies have explored broader SLSS trends with regard to streamers and viewers (Harpstead et al., 2019), few have leveraged theories of OCs to systematically investigate group-level factors related to viewer participation and financial commitment within SLSSs.

Addressing this critical gap, we draw from the collective effort model (CEM) and heuristics of authority, reciprocity, scarcity, and similarity widely used in extant OC research (Kraut and Resnick, 2012) to systematically examine group-level factors that may influence individual viewers’ behavior in the SLSS, Twitch.tv. Examined at scale, we explore how audience size, moderator activity, streamer gender, and content diversity might be associated with individual viewer participation and financial commitment. Data from 326 Twitch streamers and 5620 streaming sessions collected over 7 weeks indicate individual viewers participated and financially contributed less in larger audiences than in smaller audiences. Moderator activities were associated with increased individual participation and financial commitment. Male streamers comprised the majority of our sample and streamed longer and more sessions; however, female streamers’ garnered audiences almost seven times larger, 46% more moderator activity, and earned more than double financial commitment per viewer than men.

Our findings contribute to OC and SLSSs research in four ways. First, this study extends CEM in combination with social influence heuristics of authority, reciprocity, scarcity, and similarity to examine behavioral dynamics in a newer and more intimate form of OC. Our results demonstrate the persistent relevance of CEM and the heuristics of group influence in SLSS context, highlighting that individuals are more willing to contribute to smaller, high status, socially oriented, and similar online groups (Kraut and Resnick, 2012). Second, this study unpacks the nuances of individual SLSS viewers’ behavior in group contexts. While total viewership quantifies performance success and enables streamers to generate revenue in SLSSs (Pellicone and Ahn, 2017), our per capita measures capture individuals’ participation and financial commitment to provide insight into the quality of communities and their collective ability to attract and retain members. Third, while most SLSS research has focused on gaming-related content (Harpstead et al., 2019; Spilker et al., 2018), our study examines the most popular content, including both gaming and nongaming categories. Finally, our results provide one of the first empirical examinations into financial commitment directly to streamers, informing future research into new subscription-based media.

SLSSs as unique OCs

OCs are groups of users with a shared purpose, interest, or need who socially interact with one another through computer-mediated communication (Rheingold, 2000). SLSSs are a type of OC where users gather and interact with others who share an interest in a streamer or their content. However, SLSSs have the following characteristics that make them distinct (Zimmer, Scheibe, and Stock, 2018): (1) They are synchronous. While past broadcasts are often saved and accessible to viewers (similar to YouTube), SLSSs are unique in offering thousands of real-time broadcasts, or “streams.” (2) They allow users to broadcast their own content in real time over their own “channel.” Anyone can create their own channel and stream. Users that broadcast their own content are called streamers. (3)They require mobile devices or PCs and webcams with Internet connectivity. While not all streamers include live webcam footage in their broadcast, webcams can increase co-presence, or the sense of being and acting with others (Durlach and Slater, 2000). Viewers can share and react to the emotions and facial expressions of streamers (Hamilton et al., 2014), thereby increasing their emotional connection and intimacy with the streamer. (4) Audiences can interact with the streamer and other viewers over text-based Chat. Chat transforms an otherwise passive viewing experience into an interactive one where viewers co-create content and shape the direction of the broadcast. (5) Some SLSSs support gamification mechanics that provide further interactivity. Many streamers include donation targets on their streams, with animations that feature and reward viewers who donate particular amounts. Some broadcasts or streams include “top donor” counters or lottery incentives that elicit competition among viewers. Viewers can also gamble and make predictions of what will occur during a broadcast (Johnson and Woodcock, 2019). (6) Audiences can directly reward and tip streamers with money, gifts, or points in real-time which often are accompanied by a custom message directed to the streamer. Whereas content creators on platforms such as YouTube generate revenue via advertisements and sponsorships, SLSSs enable the direct flow of revenue from viewers to streamers. This mass-personal broadcasting fosters microcelebrity subcommunities where users can have synchronous and intimate interactions with streamers and one another and can financially support streamers (Sjöblom et al., 2019).

Participation and financial commitment in SLSSs

In a meta-analysis of 83 OC studies, Malinen (2015) found that while there was no specific definition offered for participation, scholars most commonly conceptualized participation based on the visibility of activity, using an active-passive dichotomy. Active participation in OCs entails leaving a visible trace through commenting, sharing, and asking questions. In contrast, passive participation entails “lurking” behaviors such as searching, browsing, reading, and watching. While both types of participation are legitimate means of socializing oneself and feeling a sense of community, this study focuses on visible activities through active participation which may spur further visible participation (Yang et al., 2017). Without consistent visible participation, OCs may not be able to retain existing members or attract new members (Kraut and Resnick, 2012).

Similarly in SLSSs, active participation leaves visible traces via Chat comments or financial subscription and donation notifications, whereas passive participation entails watching or browsing content (Bründl et al., 2017). SLSS participation and financial commitment are entwined—as people watch, they learn the language, norms, and values of the community and may be motivated toward more active participation. Actively commenting in a chat may spark further social interactions with other viewers or the streamer, leading to further active participation (Yang et al., 2017). As viewers become more invested in their favorite streamer, they may feel compelled to “give back” to the community with financial subscriptions or donations (Diwanji et al., 2020), which may beget further participation and financial commitment in SLSSs.

The CEM and heuristics of social influence

The CEM provides a framework for examining individual participation in group settings that identifies likely threats to motivations and predicts how valued outcomes influence motivation and effort (Karau and Williams, 1997). According to CEM, individuals are more willing to contribute to a group when they deem their individual effort as unique, important, and identifiable, and when they like the group (Karau and Williams, 2001). In SLSSs, individual contributions can be seen through active participation over Chat and financial commitment via subscriptions and donations. Frequent commenters can gain the attention of the streamer or moderators in the community and may even be promoted as moderators, providing value to the communities they help govern (Wohn et al., 2018). Similarly, individuals’ financial contributions benefit and influence both the individual and the community. Individuals can broadcast a customized message attached to their financial contribution to the streamer, which is often acknowledged by the streamer. This recognition of individuals’ contributions as identifiable or unique may further encourage more participation and financial commitment while also contributing to the overall community’s growth.

CEM has been supported across a variety of OCs (Ling et al., 2005; Rashid et al., 2006).

A recent study found that small group size, and having an administrator role, more consistent communication, and more close friends within the OC were the strongest predictors of users’ commitment to OCs (Kraut et al., 2020). Building upon CEM, we also leverage Cialdini (2001) and Cialdini and Goldstein’s (2004) heuristics of social influence that highlight the role of social influence in gaining compliance and conformity. These heuristics serve as shortcuts for decision-making and include (1) authority: individuals are likely to comply to perceived authority figures who either wield expertise or have influence within a social structure’s hierarchy due to entrenched norms; (2) reciprocity: individuals are likely to oblige others and repay them in kind due to the fundamental and socialized need for affiliation; (3) scarcity: individuals will perceive items, information, or opportunities as highly desirable, valuable, and “exclusive” if they are limited and scarcely available; and (4) similarity: individuals will comply or conform to others’ behavior that they perceive as similar to themselves, with similarity a cue for a potential friend or acquittance (Cialdini, 2001; Cialdini and Goldstein, 2004). These heuristics in conjunction with CEM demonstrate how each group characteristic may be related to and influence individual viewers’ motivation to participate and financially contribute in SLSS group contexts. Previously, CEM and the heuristics were successfully tested to examine participation in OCs, with individuals more willing to contribute to smaller, high status, socially oriented, and similar online groups (Kraut and Resnick, 2012). Below, we examine how salient characteristics including audience size, moderator activity, streamer gender, and content diversity might be related to individuals’ active participation and financial commitment in SLSS communities.

Audience size

According to the CEM, individuals will contribute less, or “socially loaf” if they believe their efforts are unimportant, unidentifiable, or if they do not like the group (Karau and Williams, 2001). With larger audiences, live-streaming communication moves from small-scale interpersonal interactions to crowd-based interactions, with messages flooding the Chat window so quickly that they cannot be read by viewers (Carter and Egliston, 2018). The resulting overload in communication may lead to a decrease in participation and affinity for the group as users may deem their contributions as inconsequential (Kraut et al., 2020). One of the first studies examining the limits of individual information processing synchronous environments found that as chat room size increased, the number of messages posted per person decreased (Jones et al., 2008). Among large audiences and their deluge of comments, viewers may perceive their contributions as redundant, unimportant, or unidentifiable and may not be motivated toward active participation. They may also be unable to frequently or effectively communicate with one another which may hinder their ability to build connections (Yang et al., 2017).

At the same time, while CEM predicts more social loafing with larger audiences, it is possible that more viewers facilitate more participation. In their survey of over 650 users across two different OCs, Ma and Agarwal (2007) found that virtual co-presence, or the awareness of being with others in a virtual environment, had a positive effect on driving user contributions. The attention received from larger audiences may positively influence the motivation to create user-generated content, thereby increasing the amount of participation from viewers (Huberman et al., 2008). The increased presence of others can affect interpersonal awareness, which in turn may affect social interactions and contributions in OCs (Yang et al., 2017).

RQ1: How is a live-streaming session’s audience size associated with individual viewers’ (a) participation during the live session and (b) financial commitment to the streamer?

Moderator activity

Concerns over trolling, harassment, and misbehavior are pervasive across OCs. In anonymous and pseudonymous virtual spaces, individuals may behave in disinhibited ways, contrary to their normal face-to-face behavior, with minimal consequences (Suler, 2004). As high-status authority figures that are influential and committed to the communities they govern, moderators handle misbehavior and socially engage its members (Seering et al., 2017). The heuristic of authority demonstrates how moderators are able to influence others with their position in the hierarchy and expertise (Cialdini and Goldstein, 2004). In SLSSs, a moderator’s position is especially salient given the visibility of their actions and their ability to ban certain members and promote norms of reciprocity (Seering et al., 2017). Rather than simply removing users with the technical tools available, a study across Twitch, Reddit, and Facebook found that moderators socially engage with the community during occurrences of misbehavior (Seering et al., 2019). This consideration of members’ social needs is likely to generate favorable compliance and contributions from members (Cialdini and Goldstein, 2004), allowing viewers opportunities to observe, internalize norms, and emulate positive behaviors.

Responsiveness may also influence members by establishing norms around reciprocity. When viewers see moderators respond to questions or comments, viewers may feel an obligation to further respond in kind due to the reciprocity heuristic and individuals’ need for affiliation. In their study of Twitch moderators, Seering et al. (2017) found that text Chat behaviors were contagious; viewers imitated moderators’ behavior when posting spam, questions, and smiles significantly more so than nonmoderators. These positive emojis, such as smiles, can increase the intimacy level between social interactants (Janssen et al., 2014) and further the liking and similarity of group members that may motivate more participation. In addition, many streamers include Chatbot moderators that answer viewers’ informational questions related to streaming equipment used or the length of stream. This responsiveness and recognition of viewers’ participation may increase viewers’ perception that their contributions are identifiable, unique, and important which may further motivate more participation. One study based on CEM found that peer and expert editorial oversight (or more simply, moderation) increased both the quantity and quality of contributions while reducing misbehavior in a movie recommendation community (Cosley et al., 2005). Therefore,

H1: Moderator activity will be positively associated with individual viewers’ (a) participation during a live session and (b) financial commitment to a streamer.

Gender

SLSSs are considered to be a male-dominated domain (Sjöblom et al., 2019) with some viewers preferring to watch and comment in only male or female streamers’ channels (Gerber, 2017). According to the scarcity heuristic, opportunities become more desirable the more they are perceived as scarce and unavailable (Cialdini, 2001). The scarcity of female streamers as well as the lack of female viewers’ visible participation (Long and Tefertiller, 2020) may increase interest from audiences. In addition, female streamers are perceived as more socially oriented compared to male streamers (Zimmer and Scheibe, 2019), and viewers may feel compelled to reciprocate such social behaviors with more participation and financial commitment. Given SLSSs users’ social motivations (Hilvert-Bruce et al., 2018), the scarcity and perceived sociality of female streamers may increase viewer interest and participation.

At the same time, due to their perceived scarcity and marginalized status in live-streaming contexts, female streamers are especially vulnerable to harassing behavior (Pellicone and Ahn, 2017), which may affect their performance and viewers’ experience. A recent study examined discriminatory rhetoric used to reference female streamers across several forum threads on r/Twitch, a Reddit community focused on Twitch (Ruberg, et al., 2019). The term “cam girl” or “boobie streamer” was a common-place label that denigrated the work of female streamers as illegitimate and undeserved (Ruberg et al., 2019). While the practices of all live-streamers are essentially body work—streamers are paid through a system of subscriptions and tips and perform with their personalities and bodies to hold viewers’ attention online—the association to sex work is reserved for only female streamers (Ruberg et al., 2019). One study examined gamers’ reactions to prerecorded audio found that the female voice elicited three times more negative comments than the male voice and no voice conditions, with a clear pattern of gendered derogatory language and questions regarding competency (Kuznekoff and Rose, 2013). The perceived scarcity of female streamers as well as the antipathy they receive may increase viewership and negative comments. Therefore,

RQ2: Are there differences between male and female streamers’ (a) number of streaming sessions, (b) audience size, (c) moderator activity, (d) individual viewers’ participation, and (e) individual viewers’ financial commitment?

In response to potentially more negative comments and behaviors, female streams may have more moderator activity. While subsequent moderator responses to harassment or negative behaviors may actively combat harassment, establish clearer norms, and ensure a safe space for streamers and viewers to interact, the removal of comments to ensure a more positive space may also lead to a decline in participation per capita. One study examined Reddit comments across two communities with differing moderation policies; one favored “safe space,” while the other favored “free speech.” While moderators removed more comments in the safe space community, language used in the safe space was more positive than the free speech community which featured more negative and angry messages (Gibson, 2019). In this way, moderators may be more present and active for female streamers’ broadcasts which may garner larger audiences and more negative comments while also influencing the quality and quantity of subsequent comments and behavior.

RQ3: How does streamer gender moderate the relationship between moderator activity and individual viewers’ (a) participation and (b) financial commitment to a streamer?

Content diversity

With competition from alternative groups a click away, it is imperative that streamers continuously create content that provides value to their viewers (Kim et al., 2008; Kraut and Resnick, 2012). Content diversity may have a role in the sustainability of an online group through the heuristic of similarity which may serve as a cue of being among like-minded or similar people (Cialdini, 2001). While niche content communities cater to a smaller pool of potential members, they may have clearer content expectations that attract and retain homogeneous viewers more easily than communities that address diverse topics. In their examination of the diversity of Twitter messages, Wang and Kraut (2012) found more focused topics increased a group’s ability to attract new members and establish social connections. Similarly, a Reddit study found higher retention rates for niche subcommunities oriented around a distinct topic (Zhang et al., 2017). These homogeneous groups with more similar members who like the group may in turn motivate more participation and financial commitment.

While less content diversity may seem beneficial for attracting new members, it may not be optimal for long-term retention. Communities with greater content diversity appeal to a wider audience which may increase its ability to adapt should trend or viewer preferences change over time. Diverse content may also enliven discussions and provide more opportunities for members to find common ground and establish deeper social connections. One survey found a greater affinity for diverse content Facebook groups, with stronger effects for topical groups compared to family, identity, and task-oriented communities (Kraut et al., 2020). Despite having more content diversity and less homogeneity, this greater affinity for the group may lead to more individual participation and financial commitment. The relationship between content diversity and individual contributions may also differ by viewers’ tenure. One study on Reddit subcommunities found that niche groups exhibited larger linguistic and acculturation gaps between new and established members, with newcomers more engaged in diverse content communities (Zhang et al., 2017). In this way, niche content communities may be difficult for newcomers to participate in, while diverse content communities offer more pathways for participation. Analyzing Twitch forums where experienced streamers provide advice to “noob” or newer streamers, Pellicone and Ahn (2017) found that experimenting across a variety of genres may negatively affect viewership. Established streamers encouraged newer streamers to focus their content to first grow a consistent and committed viewer base, who in turn, will be patient and continue watching as the streamer explores other content. Therefore,

RQ4: How is content diversity associated with a stream sessions’ (a) audience size, and individual viewers’ (b) participation and (c) financial commitment to a streamer?

Methods

Twitch context

Twitch launched in 2011 originally as a live-streaming gaming and eSports platform, garnering more than 3 million viewers each month during its first year. Today, Twitch has expanded its content categories and user base to over 140 million monthly visitors and remains one of the most popular SLSSs. Sixty-five percent of all Twitch users are men and 73% of Twitch users are below the age of 35, with the United States comprising the largest portion of viewers at 25% of viewership, followed by Germany at 7%, Russia at 5%, Canada at 4%, and Brazil at 4% of all Twitch viewership (StreamScheme, 2021). A streamer’s profile, or “channel,” primarily displays the number of followers they have, their recent broadcasts or live sessions, and categories they recently streamed. As the Twitch homepage and search results display live channels according to category and concurrent viewership, viewers can browse and select streams based on content and audience size. When watching a live stream, viewers often use Emotes, which are platform- and streamer-specific emojis, to communicate in short-hand over Chat. Emotes signal an instantaneous emotional reaction that can be contagious. It differentiates fans from casual viewers and fosters emotional engagement and community belonging (Carter and Egliston, 2018; Seering et al., 2017). Viewers can also “follow” the streamer’s channel to receive notifications, subscribe to a streamer at $4.99, $9.99, or $24.99 per month, “gift” subscriptions to other viewers, and donate Twitch currency “bits” (1 bit = 1 cent) to cheer on and encourage a streamer during exciting moments of the live-stream. Donating bits through “cheering” is often accompanied by an animated or custom Emote and message to the streamer. These financial contributions are often displayed as a notification on screen with the username of the contributor, financial tier and subscription length, as well as an optional custom message to the streamer who often acknowledges or thanks contributors in real time.

Data collection

We identified Twitch’s top five streaming categories at the time of data collection: Just Chatting, League of Legends, Fortnite, Call of Duty: Modern Warfare, and Ghost of Tsushima. Then from each category, we randomly selected 100 streamers from a list of all streamers who were currently live at the time of data collection, for a total of 500 streamers. We then filtered the sample based on the following criteria: streamers must have a webcam on for gender coding, be a Twitch affiliate or partner with at least 100 followers at the start of data collection, have had an account for at least 100 days, stream in English, be a private individual (rather than a group or eSports channel), be a mature streamer (18+), have a publicly available Chat for viewers, and have at least a maximum of four concurrent viewers during a stream. This resulted in 326 eligible streamers in the dataset. Data from live sessions and their respective Chats were collected every time the 326 streamers went live between 17 July and 5 September 2020, resulting in a total of 5620 sessions. Data included streamers’ ID, account creation date, follower count, language, date and time of each stream, tags, stream title, viewer counts, and all Chat comments and notifications.

As detailed in Table 1, streamers live-streamed an average of 18.42 sessions (median = 11, standard deviation (SD) = 19.06). The average session lasted 309.8 minutes (median = 262, SD = 287.64), garnered 432 viewers (median = 26, SD = 2611), and 3305 comments (median = 793, SD = 27, 958.5). Streamers earned an average of $142.58 in subscriptions and 1543 bits ($15.43) per session. Twenty percent of streamers in the sample were women and 80% were men.

Table 1.

Descriptive statistics for Twitch streamers and streaming sessions.

	Total (N_streamer = 326, N_session = 5620)			Male (N_streamer = 259, N_session = 4725)			Female (N_streamer = 67, N_session = 895)			t
	Mean	Median	SD	Mean	Median	SD	Mean	Median	SD
Streamer
Content Div.	2.18	2	1.15	2.18	2	1.09	2.19	2	1.37	−0.09
Age (in days)	1714	1680	948.2	1755	1784	974.9	1557	1424	824.96	1.68
Number of followers	37,194	2832	144,771	34,208	2537	141,042.4	48,739	5071	158,971	−0.68
Number of sessions	18.42	11	19.06	19.7	12	19.79	13.66	7	15.17	2.7**
Session
Audience size	432	26	2611	231.6	24	1111.56	1488.6	50	5917	−6.33***
Mod. activity	590.1	184	2966.75	550.1	169	2735	801.7	270	3967.32	−1.82
Content div.	1.87	2	0.87	1.88	2	0.84	1.83	2	1	1.37
Duration (min)	309.8	262	287.64	312.7	255	303.6	294.2	282	180.82	2.47*
Number of comments	3305	793	27,958.5	3208	723	29,419.6	3820.1	1220	18,674.8	−0.81
Part. per cap.	41.52	23.43	131	41.16	24.1	130.71	43.41	20.92	132.5	−0.47
Total subs ($)	142.58	9.98	1467.6	134.65	9.98	1569.46	184.5	29.94	721.18	−1.5
Subs per cap.	1.06	0.26	4.06	0.95	0.23	3.71	1.62	0.49	5.52	−3.49***
Total bits	1543	5	12,696.6	1230	3	8944.2	3192	100	24,233.04	−2.39*
Bits per cap.	20.14	0.13	141.36	17.75	0.08	112	32.78	0.78	243.2	−1.81

“Div” is abbreviated for diversity, “Part. per cap.” for individual viewers’ participation per capita, “Total subs” for total subscription amount, “Subs per cap.” for individual viewers’ subscriptions per capita, and “Bits per cap.” for individual viewers’ bits per capita.

p < .05. **p < .01. ***p < .001.

Measures

Dependent variables

Participation per capita

Participation per viewer was measured by dividing the total number of comments in a chat by the maximum concurrent audience size in each live session.

Financial Commitment

Financial commitment was measured by the amount of financial support viewers voluntarily donated to a streamer through premium subscriptions and bits. Subscriptions per capita measured the average amount (in dollars) subscribed per viewer in a given live session. The subscribed tiered dollar amount for 1 month was summed then divided by the maximum concurrent audience for each session. Bits per capita measured the amount of in-game currency donated to a streamer through “cheering” and was calculated by dividing the total number of bits donated during a session by the maximum audience size.

Independent variables

Audience size

An API call collected total viewership counts every 10 minutes during a live stream. The highest viewership count was then used to measure audience size of the session.

Moderator activity

Using chat logs that accompanied each session, moderator activity was measured by summing all moderator messages, notifications of banned users, and deleted messages (an action exclusive to moderators).

Gender

Streamer gender was manually coded as male (0) or female (1) by examining the webcam during a live-stream or recently streamed videos and the self-identified pronouns used by the streamer in their profile. Nonbinary individuals were not identified in our sample.

Content diversity

Streamers used tags to classify their live-streaming session within Twitch, allowing potential viewers to search for specific streaming content. These tags were qualitatively coded under 10 different categories as game-related, art, music, sports, food, beauty, chatting, comedy/entertainment, health, and nature. As a streaming session can include multiple tags, the number of categories within each session and across all sessions were summed to create a session diversity score and a streamer diversity score, respectively.

Control variables

We controlled streamer-level variables including account age (in days), number of followers, and number of streaming sessions recorded during data collection. Additional session-level control variables include the duration of a streaming session (in minutes) and viewership incentives (binary) such as “drops,” which offer viewers in-game loot for linking a specific game account to Twitch and watching streamers play that game title.

Analysis

To estimate both streamer- and session-level effects, we used linear mixed-effects models in R (version 1.2.5033) using the lme-4 package. Three linear mixed-effects models were estimated to explore the effect of viewership size (RQ1),¹ moderator activity (H1), gender (RQ2d/e), streamer and session content diversity ((RQ4b/c), onto participation per capita, subscriptions per capita, and bits per capita, respectively (see Tables 2 –4) with individual streamers as the random effect. Another linear mixed-effects model was run to assess how streamer and session content diversity relate to audience size (RQ4a). Streamer follower count, account age, session count, session duration, and incentive were controlled in all models. Significance was determined using Satterthwaite’s method to estimate degrees of freedom and generate p-values for mixed-effects models (Kuznetsova et al., 2017). Decreases in Akaike information criterion (AIC; Akaike, 1973) assessed model fit improvement.

Table 2.

Mixed-effects models for participation per capita (N_streamer = 326, N_session = 5620).

Predictors	Model 1			Model 2
	Estimates	S.E.	t	Estimates	S.E.	t
(Intercept)	0.36	0.11	3.3**	0.36	.11	3.33**
Audience size	−3.12	1.4	−2.24*	−3.06	1.4	−2.19*
Mod. activity	0.21	0.53	38.75***	0.2	.63	31.76***
Gender	3.49	8.73	0.4	1.26	8.81	0.14
Sess. diversity	6.48	3.86	1.68	56.52	3.86	1.69
Str. diversity	5.17	3.59	1.44	5.14	3.58	1.43
Mod × gender				2.04	1.17	1.75
Covariates
Incentive	0.17	0.12	1.49	0.17	0.12	1.48
Duration	0.01	0.01	1.61	0.01	0.01	1.64
Session count	−0.59	0.17	−3.43***	−0.59	0.17	−3.43***
Acct. age	0.01	0.01	−1.64	0.01	0.01	−1.63
Number of followers	0	0	−3.22**	0	0	−3.19**
σ ²	12,193			12,191
τ₀₀	2149			2138
AIC	69,212			69,211

ICC = 0.145. “Audience size” and “Moderator activity” are scaled down by 1000. “Sess. diversity” is abbreviated for session diversity, “Str. diversity” for streamer diversity, “Mod × gender” for the interaction of moderator activity and gender, and “Acct. age” for streamers’ account age.

p < .05. **p < .01. ***p < .001.

Table 3.

Mixed-effects models for subscriptions per capita (N_streamer = 326, N_session = 5620).

Predictors	Model 1			Model 2
	Estimates	S.E.	t	Estimates	S.E.	t
(Intercept)	1.57	0.35	4.47***	1.59	0.35	4.54***
Audience size	−0.1	0.05	−2.22*	−0.09	0.04	−2.08*
Mod. activity	0.47	0.02	27.57***	0.41	0.02	20.30***
Gender	1.01	0.28	3.6***	0.78	0.28	2.76**
Sess. diversity	0.01	0.13	0.09	0.01	0.12	0.12
Str. diversity	−0.01	0.12	−0.1	−0.01	0.12	−0.12
Mod × gender				0.21	0.04	5.62***
Covariates
Incentive	0.03	0.38	0.09	0.02	0.38	0.06
Duration	0	0	2.25*	0	0	2.34*
Session count	−0.02	0.01	−3.15**	−0.02	0.01	−3.15**
Acct. age	0	0	−1. 83	0	0	−1.8
Number of followers	0	0	−2.17*	0	0	−2.08*
σ ²	12.91			12.84
τ₀₀	2.21			2.17
AIC	30,705			30,676***

ICC = 0.137. “Audience size” and “Moderator activity” are scaled down by 1000. “Sess. diversity” is abbreviated for session diversity, “Str. diversity” for streamer diversity, “Mod × gender” for the interaction of moderator activity and gender, and “Acct. age” for streamers’ account age.

p < .05. **p < .01. ***p < .001.

Table 4.

Mixed-effects models for bits per capita (N_streamer = 326, N_session = 5620).

Predictors	Model 1			Model 2
	Estimates	S.E.	t	Estimates	S.E.	t
(Intercept)	0.22	0.11	2.01*	0.24	0.11	2.29*
Audience size	−2.08	1.45	−1.43	−1.36	1.42	−0.96
Mod. activity	0.13	0.62	21.55***	6.47	0.72	9.00***
Gender	0.19	8.62	2.18*	−5.78	8.52	−0.68
Sess. diversity	2.40	4.21	0.57	2.67	4.10	0.65
Str. diversity	−1.55	3.60	−0.43	−1.7	3.51	−0.49
Mod × gender				0.24	1.33	8.11***
Covariates
Incentive	−7.50	0.13	−0.58	−8.72	0.13	−0.69
Duration	.01	0	1.42	.01	0	1.73
Session count	−0.15	0.17	−0.89	−0.14	0.16	−0.87
Acct. age	−0.01	0	−1.48	0	0	0.18
Number of followers	0	0	−1.8	0	0	−1.54
σ2	17,313			16,353
τ₀₀	1658			1586
AIC	71,074			70,757***

ICC = .084. “Audience size” and “Moderator activity” are scaled down by 1000. “Sess. diversity” is abbreviated for session diversity, “Str. diversity” for streamer diversity, “Mod × gender” for the interaction of moderator activity and gender, and “Acct. age” for streamers’ account age.

p < .05. **p < .01. ***p < .001.

To compare male and female streamers’ number of streaming sessions, audience size, and moderator activity (RQ2a/b/c), Welch’s t-tests were used to account for sample size differences (see Table 1). Three additional models were estimated to explore the interaction between gender and moderator activity (RQ3) onto participation and the two measures of financial commitment (see Tables 2 –4).

Results

RQ1 asked how audience size is related to individual viewers’ (a) participation and (b) financial commitment. Audience size had a significant and negative association with participation per capita (coefficient = −3.06, p < .05) as well as subscriptions per capita (coefficient = −0.09, p < .05), but was not significantly related to bits per capita (coefficient = −1.36, p = .34). In other words, an increase in audience size by 1000 viewers relates to three fewer comments and $0.09 less in subscriptions per viewer.²

H1 predicted that moderator activity would be positively associated with (a) individual viewers’ participation and (b) individual viewers’ financial commitment. Confirming H1a, moderator activity had a significant and positive association with participation per capita (coefficient = 0.2, p < .001). Moderator activity was also positively associated with subscriptions per capita (coefficient = 0.41, p < .001), and bits per capita (coefficient = 6.47, p < .001), confirming H1b. Overall, every 1000 moderator activities translate to 0.21 more comments, $0.47 more in subscriptions, and $0.13 more in bits donated per viewer.

RQ2a–c examined how male and female streamers differed in the number of streaming sessions, audience size, and moderator activity, respectively. Male streamers streamed significantly more sessions than female streamers (M_male = 19.7, M_female = 13.66, t = 2.7, p = .007). Female streamers’ sessions garnered almost seven-fold the audience size of male streamers’ sessions (M_male = 232, M_female = 1489, t = −6.33, p < .001), and while they had 46% more moderator activity than male streamers, this relationship was only near significant (M_male = 550.06, M_female = 801.73, t = −1.99, p = .063). RQ2d and RQ2e examined how male and female streamer sessions differed in individual viewers’ participation and financial commitment. There were no gender differences in participation per capita (coefficient = 1.26, p = .89) or bits per capita (coefficient = −5.78, p = .50), however, gender was significantly related to subscriptions per capita, meaning female streamers earned significantly more subscriptions per viewer than male streamers (coefficient = 0.78, p = .006).

RQ3 examined whether gender moderated the relationship between moderator activity and individual viewers’ (a) participation and (b) financial commitment. The interaction term of moderator activity and gender was not significant on participation per capita (coefficient = 2.04, p = .08), but was significant relationship on subscriptions per capita (coefficient = 0.21, p < .001), and bits per capita (coefficient = 0.24, p < .001). In other words, for every additional 1000 moderator actions in Chat, female streamer sessions earned $0.21 more in subscriptions per viewer, and $0.24 more in bits per viewer than male streamer sessions.

RQ4 asked how content diversity related to a stream session’s (a) audience size, and individual viewers’ (b) participation and (c) financial commitment. Streamer content diversity was not significantly related to audience size (coefficient = −.05, p = 0.55 and session content diversity was similarly not significant in relation to audience size (coefficient = .02, p = 0.48). Streamers’ content diversity was not significantly related to participation per capita (coefficient = 5.14, p = 0.15), subscriptions per capita (coefficient = −.01, p = 0.91), and bits per capita (coefficient = −1.7, p = 0.63). Sessions’ content diversity was also not significantly related to participation per capita (coefficient = 6.52, p = 0.09), subscriptions per capita (coefficient = .01, p = 0.91), and bits per capita (coefficient = 2.67, p = 0.52).

Discussion

Guided by CEM and the heuristics of social influence, this study examines how group context characteristics including audience size, moderator activities, streamer’s gender, and content diversity influence viewers’ active participation and financial commitment. Our results demonstrate that, despite differences in how users interact and connect with one another across older OCs and SLSSs (Zimmer et al., 2018), individuals’ participation and financial commitment can be similarly predicted by CEM and group influence. Larger audiences were related to diminished individual participation and financial commitment while moderator activity related to more. Female streamers attracted almost sevenfold the number of viewers and earned two to three times more financial contributions. Content diversity was not related to individual participation or financial commitment. In examining the top five streaming categories, inclusive of both gaming and nongaming streams, our study provides a more comprehensive understanding of individual behaviors across SLSS communities on Twitch.tv.

Larger audiences, smaller contributions

In line with prior research, this study demonstrated more social loafing in larger audiences (Jones et al., 2008; Karau and Williams, 2001). While total viewership and revenue earned may serve as heuristic measures of success, focusing on participation and financial commitment per viewer reveals the nuanced individual behaviors that can be used to compare across different communities. Large audiences will naturally result in greater participation, but as our results demonstrate, they come at the cost of diminishing individual participation and financial commitment within SLSSs. As meaningful interpersonal interactions become more difficult, individuals may have perceived their contribution to the group as inconsequential (Carter and Egliston, 2018). In addition, streams with large audiences may signal the entertainment value, thus attracting viewers who just wish to watch without interactions.

It is also worth noting that, while CEM is often used to examine social loafing, social loafing within SLSSs is not necessarily indicative of a passive audience as a whole. Streamers have the freedom to limit participation by selecting who can contribute, providing viewers with subscriber-only or follower-only Chats. Viewers and streamers can also communicate using third-party platforms such as Discord, suggesting that a passive audience on SLSSs could well be active elsewhere. Despite these possibilities, examining participation and financial commitment (or lack thereof) within an SLSS platform remains relevant considering the influence individuals’ contributions have on other SLSS viewers’ experiences. If all viewers congregated in a streamer’s Discord channel rather than the SLSS platform, it would be detrimental to the streamer’s community growth and sustainability. Active participation over Chat and financial contributions are what make SLSS communities social and appealing to viewers (Hilvert-Bruce et al., 2018); they provide a streamer’s community access to potential new members, develop relationships between existing members, and enable its growth both within and outside SLSSs.

Modeling moderators

More moderator activity was related to more participation and financial commitment per viewer, implying moderators may have encouraged viewer contributions through their authority to model group norms and reinforce positive behaviors, as well as their reciprocity in addressing viewers’ questions and comments. However, it is also possible that increased moderator activity was a direct response to greater participation, either from larger audiences or strong contributors. Given the mutual influence between moderator activity and viewer participation, we cannot assert causality. Nevertheless, our findings demonstrate the instrumental role of moderators in engaging and maintaining participatory communities. Future studies should further examine the differences proactive moderation strategies have in driving viewer behavior compared to reactive moderation strategies that are driven by viewer behavior.

Female streamers: larger audiences, more financial commitment

Our results highlight the gender differences in live-streaming contexts that are primarily dominated by male streamers (Sjöblom et al., 2019). The majority of streamers in our sample were men, who also streamed significantly more and longer sessions than female streamers but experienced diminishing returns with regard to audience size and financial capital. While female streamers were outnumbered, they were more popular and successful than male streamers, attracting audiences almost seven times larger and earning two to three times in subscriptions per capita and the total number of bits. The scarcity of female streamers and their streaming sessions may have increased interest, especially for viewers who watch streamers because of gender-stereotypes regarding their sociability and reciprocity (Gerber, 2017; Zimmer and Scheibe, 2019). It is important to note that we sampled streamers across the top five categories, four of which are video game titles, so it is possible streamer gender dynamics differ in less popular categories.

At the same time, female streamers’ increased popularity and success may come with more misbehavior and gender-based harassment. Female streamers had 46% more moderator activity than male streamers and earned an additional $0.21 in subscriptions per viewer and $0.24 in bits per viewer for every 1000 actions done by moderators. Female streamers averaged 1257 more viewers in a session compared to male streamers, which can translate to an additional $264 total in subscriptions and $301.68 total in bits donated. While the influence of moderator activity may have motivated more participation and financial commitment, especially for female streamers, it is possible that increased moderator activity emerged as a consequence of greater misbehavior, sexism, and gender-based harassment. In their study examining over one billion Twitch Chat messages, Nakandala et al. (2017) found that streamers’ gender was significantly related to the types of messages received, with female streamers receiving more objectifying messages compared to male streamers who received more game-related messages. These findings were even more pronounced for popular female channels. In response to such misbehavior and subsequent moderation, female streamers may have received increased financial returns as a form of social support. Considering how the act of donating bits is called “cheering,” viewers may have donated more bits in an attempt to counteract negative messages and support the streamer. Regardless, our findings may highlight moderators’ role in mitigating misbehavior and promoting a safer space for the female streamers’ communities, all of which may signal the desirability and quality of the community (Ruberg et al., 2019).

Limitations and future directions

This study has a few limitations. Like all theories, CEM does not account for each and every viewer’s motivation to participate in SLSS streams. For example, some viewers may contribute more, instead of less, in larger audiences where they can garner more attention. Viewers may also be more motivated around crowd-based contributions rather than social interactions, copy/pasting emotes or phrases based on other viewers’ responses. For these individuals, social facilitation, rather than social loafing, occurs when audience size increases.

Empirically, the current study is limited by the lack of causality, given the cross-sectional nature of the analysis. As our analysis examined a variety of streamers across the top five Twitch categories at the time of data collection, our findings may not hold for less popular categories. Besides Chat and financial contributions, there are other ways of social interaction which we did not examine. For example, viewers can “follow” a streamer, signaling interest and investment in future broadcasts that may not appear as involved or salient as Chat participation or subscription. Also, this study did not account for the various ways in which streamers can control the quantity and quality of comments on Chat. For example, streamers can opt for subscriber-only, follower-only, or emote-only Chats which may influence how and how much individuals participate and subscribe. We also did not account for strong contributors and their influence on other viewers’ participation and financial commitment. Future studies on viewer behavior should therefore account for multiple forms of participation and streamer controls.

Our data were collected during the COVID-19 pandemic when many countries issued stay-at-home orders. During this time, viewership over Twitch and the live-streaming industry increased substantially (Kastrenakes, 2020), with viewers likely seeking entertainment, distraction, and a sense of community as a coping mechanism (de Wit et al., 2020), which may have influenced Chat participation in addition to their relationship development with a streamer and their community. Meanwhile, it is also possible that viewers financially contributed less compared to prepandemic levels due to unemployment and a shrinking global economy. Longitudinal research covering extended periods and across a large number of streamers and across live-streaming platforms is warranted to generalize our findings and explore the potential influence of COVID-19 on both streamers’ and viewers’ behavior.

Finally, our analysis consisted of English-speaking channels within Twitch, which is heavily skewed more toward American and Western audiences. While live-streaming practices have more recently begun to enter into mainstream culture in North America, usage has been more ubiquitous in countries such as China with more viewers and professional/full-time streamers, and more diverse content streamed compared to North American or European streamers (Lu et al., 2018). Considering these differences, our findings may reflect dynamics of Twitch specifically rather than SLSSs as a whole, warranting further research across a variety of SLSSs both within and outside the Western-English speaking context.

Conclusion

Individual viewers’ participation and financial commitment are essential for building and growing SLSSs communities. Using CEM and social influence heuristics which include authority, reciprocity, scarcity, and similarity, this study examined the potential effect group characteristics including audience size, moderator activity, streamer gender, and content diversity have on individuals’ participation and financial commitment in the case of Twith.tv. We found that the collective effort model and heuristics that are well-supported in traditional OCs also hold true in SLSSs. Viewers are not atomic agents acting within a community vacuum; rather, are mutually influenced by the behavior of the streamer and other viewers. We find distinct overhead costs and diminishing returns for streamers with larger audiences. In contrast, moderator activity is related to more individual participation and financial commitment for all streamers, with female streamers financially benefiting more than males. While total viewership and revenue have been a primary consideration for SLSS streamers and designers alike, these results provide important nuances to understanding community sustainability. As more users and streamers participate in SLSSs and subscription-based communities, our results provide one of the first empirical examination into financial commitment directly to streamers, informing future research into new subscription-based media.

Footnotes

Appendix 1

We ran additional analyses to explore whether audience size had a curvilinear relationship with our outcome variables. Below we report the results of these models, which were significant and fit the data better than the reported results for participation per capita and subscriptions per capita.

Acknowledgements

The authors thank Laramie Taylor and Wang Liao for their valuable feedback on early drafts as well as David Wolff for his assistance with data collection.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Grace H Wolff

Cuihua Shen

Notes

Author biographies

Grace H Wolff earned her PhD from the Department of Communication at UC Davis in 2021. She is a user experience (UX) researcher interested in leveraging both quantitative and qualitative methods to better understand users’ communication patterns, attitudes, and behaviors across digital ecosystems.

Cuihua Shen is a professor at the Department of Communication and co-founder of the Computational Communication Research Lab, UC Davis. Her research interests include online and mobile social networks, misinformation, and computational social science.

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Audience size,moderator activity,gender,and content diversity: Exploring user participation and financial commitment on Twitch.tv

Abstract

Keywords

SLSSs as unique OCs

Participation and financial commitment in SLSSs

The CEM and heuristics of social influence

Audience size

Moderator activity

Gender

Content diversity

Methods

Twitch context

Data collection

Measures

Dependent variables

Participation per capita

Financial Commitment

Independent variables

Audience size

Moderator activity

Gender

Content diversity

Control variables

Analysis

Results

Discussion

Larger audiences, smaller contributions

Modeling moderators

Female streamers: larger audiences, more financial commitment

More or less content diversity?

Limitations and future directions

Conclusion

Footnotes

Appendix 1

Acknowledgements

Funding

ORCID iDs

Notes

Author biographies

References