TWITCH AND MEDIA ENJOYMENT 1 Title: Watching

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Title: Watching players: An exploration of media enjoyment on Twitch

Please cite as: Wulf, T, Schneider, F. M., & Beckert, S. (2018). Watching players: An exploration of media enjoyment on Twitch. Games and Culture. Advance online publication. https://doi.org/10.1177/1555412018788161. Authors: Tim Wulf1, Frank M. Schneider2, and Stefan Beckert3 1

University of Cologne, Department Psychology, Social Psychology II: Communication and Media Psychology, Richard-Strauss-Straße 2, 50931 Cologne, Germany, [email protected] 2

University of Mannheim, Institute of Media and Communication Studies, Haus Oberrhein, Rheinvorlandstraße 5, 68159 Mannheim, Germany, [email protected] 3

University of Hohenheim, Lehrstuhl für Medienpsychologie (540 F), 70593 Stuttgart, Germany, [email protected]

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© 2018, Sage Publishing. This paper is not the copy of record and may not exactly replicate

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the final, authoritative version of the article. Please do not copy or cite without authors

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permission. The final article will be available, upon publication, via its

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DOI: 10.1177/1555412018788161

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TWITCH AND MEDIA ENJOYMENT

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Abstract

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Video game streaming platforms have reached high popularity within the last years. As one of

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these popular platforms, Twitch provides users with the opportunity to participate in several

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gaming situations: They can simultaneously watch in-game actions, the streamer playing the

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game, and additionally, they can interact with the streamer and other viewers by using the

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chat. In an online survey, the current study explored how individuals (N = 548) experience

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media enjoyment when using Twitch. Findings indicate that social aspects of using Twitch

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predominantly contribute to enjoyment. Approaches towards the phenomenon of video game

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streaming as well as implications for research on the usage of second screens and Social TV

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are discussed.

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Keywords: Twitch, video game streaming, media enjoyment, parasocial relationships,

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suspense

TWITCH AND MEDIA ENJOYMENT 1

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Watching players: An exploration of media enjoyment on Twitch

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Since the 1980s, video games have become an essential part of media consumption. In the

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beginning, especially young users were motivated to play (Lenhart et al., 2008). However,

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nowadays video games address a broader audience and enchant people to watch others play

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using Let’s Play Videos (Venus, 2012) and joining eSports competitions as spectators (Seo,

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2016; Taylor, 2012). Specifically, people can watch players live and participate in eSports

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streams via the video game streaming site Twitch (Lin & Wan, 2016). That is, Twitch does not

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only stream the particular game but also monitors the player and his live-commentaries,

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allowing the spectator to interact with him and other watchers via chat. Twitch succeeds in

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being one of the internet’s most used video transmission platforms. Hence, as such a multi-

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faceted medium, research needs to investigate what facets drive users’ enjoyment while

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watching.

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Many theories have examined the processes under which media induce enjoyment.

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Entertainment experiences may be employed in order to maintain positive mood (Knobloch-

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Westerwick, 2006; Zillmann, 1988) and generate feelings of pleasure (Bosshart & Macconi,

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1998; Vorderer, Klimmt, & Ritterfeld, 2004). This may occur, for example, by helping people

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to escape into virtual worlds and narrations (Green, Brock, & Kaufman, 2004; Katz &

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Foulkes, 1962), building up relations to media personae (Rubin, Perse, & Powell, 1985), or

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stimulating arousal, for example, via excitation transfer (Zillmann, 1996a). Moreover, recent

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research also found that using social media on a second screen while watching TV may

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enhance enjoyment under certain conditions (Krämer, Winter, Benninghoff, & Gallus, 2015).

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Regarding Twitch, many of these approaches overlap due to Twitch’s integrative multimedia

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character. Combining various media features on a single platform (namely, suspense elicited

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by the video game, relationships to the streaming person, and social interaction with others

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using the chat), it is unclear which media are related to the enjoyment of using Twitch and,

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thus, its success. Unveiling these fascinating mechanisms would offer various theoretical and


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practical implications: Whereas research had a chance to get insight into the interplay of

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media facets which have yet been observed mostly regarding their individual contribution to

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enjoyment, practitioners had a chance to realize how they need to create media to attain a

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maximum of popularity. Therefore, we first review and identify important aspects of media

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enjoyment that may be related to the features of Twitch. To investigate whether the enjoyment

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of Twitch users’ relates to the experiences that are connected to these features (i.e., parasocial

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relationships, suspense of the streamed video game, and interaction via the chat), we

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conducted an online survey among Twitch users who were recruited via international gaming

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community websites. Using structural equation modeling, we analyzed how experiences that

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are related to specific Twitch features contribute to enjoyment.

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Twitch as a Multi-Faceted Medium

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Twitch is a live video platform specialized for streaming video games on which

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streamers record themselves via webcam while playing.. Simply put, it is a platform where

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people watch gamers play games. Although Twitch offers the option to save videos after

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streaming, the main purpose is to stream and watch live content. In contrast to regular live

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TV, the audience and gamers (so-called streamers) can interact with each other.

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Since its foundation in 2011, Twitch has burgeoned: Today, there are 15 million daily

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active viewers (Twitch, 2017). On average, Twitch has 550,000 concurrent viewers, with

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peaks up to 2.1 million viewers during big events (Twitch, 2016a). There are currently over 2

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million unique streamers per month (Twitch, 2016a) sharing their content and experiences.

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Altogether, Twitch is currently the 33rd most popular website worldwide (average daily

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visitors to this site and pageviews on this site over the past 3 months; Alexa.com, 2018).

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Comparable to YouTube, streams are recommended to users via algorithms.

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Algorithms in this sense can best be understood as a “finite set of rules that gives a sequence

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of operations for solving a specific type of problem” (Knuth, 1997, p.4). This set is generated

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by the users’ browser history, metadata, and searching behavior. Such algorithms are used to


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provide internet users with enjoyable future choices based on previous media use, for

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example, by suggesting movies to watch and books to read (Nguyen, Hui, Harper, Terveen, &

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Konstan, 2014). An analysis of the impact of recommendation algorithms on YouTube

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revealed that recommendations generate the most amount of views besides intentional

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keyword search (Zhou, Khemmarat, & Gao, 2010). Although these algorithms appear

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unfathomable to users (Rieder, Matamoros-Fernández, & Coromina, 2018), these studies

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highlight how algorithms shape selection processes. With regard to Twitch, algorithms take

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the users’ metadata into account and track viewing behavior (e.g., streams are recommended

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if they are in the users’ language, popular, and shared among favorite streamers’

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communities). As a result, Twitch offers navigation through several streams via a sidebar with

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an overview of (a) followed streamers who are online, (b) streams that friends are currently

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watching, (c) recommended streams (selected by algorithms), and (d) a search bar for further

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stream search. Altogether, Twitch aims at offering a personalized entertainment experience

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that is as enjoyable as possible.

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In general, watching other people playing games is no revolutionary phenomenon

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introduced by Twitch: In South Korea, broadcasting video games via television is an

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established form of everyday media use (Cheung & Huang, 2011). Similarly, in Europe,

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interested viewers were able to watch video games on TV using broadcasters such as GIGA

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Television until 2009 (Sky, 2008). With the rise of the internet, platforms such as YouTube

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provide the opportunity even for laypersons—and in contrast to TV broadcasting—to create

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and share their own content (Cha, Kwak, Rodriguez, Ahn, & Moon, 2007). Consequently,

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there is a broader range of video game content, for example let’s play videos (Venus, 2012).

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Moreover, professional eSports has become popular and attracts many players (Weiss &

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Schiele, 2013) and watchers (Hamari & Sjöblom, 2017) who join big events and watch video

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game tournaments live. These contests build the “intersection of computer games and

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professionalized sports” (Seo & Jung, 2016, p. 637), because watchers enjoy experiencing the


skills of professional players. Indeed, Hamari and Sjöblom (2017) found that typical

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entertainment gratifications such as escapism and learning processes are connected to the

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frequency of people watching eSports. Such processes might also be the reason why Twitch

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offers various opportunities to (a) connect to streamers and (b) to other game spectators

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allowing to ask questions and comment the current gameplay in real-time beyond the video

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game streaming. These interactions may contribute to a unique entertainment experience.

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Twitch and Media Enjoyment Sportscast, social network, video game, and teaching video—Twitch is a complex medium and by far more than just a broadcasting platform. Indeed, research found multiple

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motivations to use Twitch. In particular, following a uses and gratification perspective,

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Sjöblom and Hamari (2016) found cognitive, affective, personal integrative, social

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integrative, and tension release motivations. Additionally, information seeking and learning to

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play seem to be important motivations as well (Sjöblom, Törhönen, Hamari, & Macey, 2017).

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Extending this line of research, Twitch may also serve many factors that are supposed to

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affect media enjoyment (Vorderer et al., 2004). In the following paragraphs, we want to

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elaborate on these factors.

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Video Game Real-Time Streaming

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Besides the opportunity to choose between different streams, Twitch’s live character

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shapes the entertainment experience. Comparable to sportscasts, being part of a real-time live

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event is highly relevant for the audience’s suspense (Knobloch-Westerwick, David, Eastin,

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Tamborini, & Greenwood, 2009; Morse, 1983). Different from a movie, series or let’s play

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video that can be watched repeatedly, live performances are unpredictable for the reason that

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there is no chance to know what will happen next. For example, Schramm and Klimmt (2003)

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found rooting for the favorite team to be one of the highest predictors of watching a match at

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the 2002’s FIFA World Cup. Regarding sportscasts, it has been shown that suspense of an

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unpredictable outcome is a driver of enjoyment (Peterson & Raney, 2008; Raney, 2012). In


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the context of Twitch, combined with sympathies for their favorite streamer, watchers may be

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affected by arousal and suspense hoping for him or her to win the match or accomplish a

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challenge (Zillmann, 1996b). As suspense is an established prerequisite of media enjoyment

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(Klimmt, Rizzo, Vorderer, Koch, & Fischer, 2009; Zillmann, 1996b), we propose that feelings

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of suspense when using Twitch are positively associated with media enjoyment (H1).

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Relationship to Streaming Personae

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Choosing among a high variety of streamers, users may select the streamer who is

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most entertaining, who has the best skills to learn from, or who responds most cooperatively

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to their requests. Indeed, Twitch users are also able to follow their favorite streamers. As a

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result, they will be notified whenever their favorites are hosting a stream. Contributing to the

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study of user–streamer relationships, past research investigated relationships that media users

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develop to media personae. Horton and Wohl (1956) theorized media users to seemingly

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interact with media personae: As hosts of TV shows or the news anchorman directly address

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the viewers—though she or he does not know every single person watching—people get a

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notion of an interaction between this persona and themselves, which is termed a parasocial

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interaction. Building upon this concept, parasocial relationships (PSRs) were discussed as

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long-term involvement with media characters comparable to some kind of friendship (Dibble,

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Hartmann, & Rosaen, 2015; Hartmann & Goldhoorn, 2011; Rubin et al., 1985).

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We argue that relations to Twitch streamers may function as specific kind of PSRs:

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Streamers do interact with users, they answer questions asked in the chat, address their

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audience, and explain why they follow certain strategies. In this regard, Payne, Keith,

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Schuetzler, and Giboney (2017) emphasize Twitch’s value as a unique digital learning

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paradigm as teachers (streamers), learners (viewers), and co-learners (other viewers) may be

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responsive with each other to optimize the understanding of strategy. Twitch streamers may

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directly interact with individual users—especially when answering individual questions or

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giving their thanks for a specific user’s donation. Although this is contrary to Horton and


Wohl (1956) in terms of a parasocial interaction (indeed, it is a social interaction), it might as

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well contribute to a parasocial relationship (we will return to this issue in the discussion).

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Moreover, professional streamers have a personal schedule of streaming times so that users

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can rely on seeing their friends again—similar to characters of a periodic TV show.

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Therefore, viewers are able to maintain their relationships to streamers. The stronger bonds

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between viewers and streamers grow, the more users may root for their favorite streamer’s

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success. For all these reasons, we argue that relationships to Twitch streamers are a specific

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kind of PSRs.

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In general, PSRs are discussed to be a prerequisite of media enjoyment (Green et al.,

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2004; Vorderer et al., 2004) and have been shown to positively relate to it (Dibble & Rosaen,

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2011). Regarding Twitch, research has demonstrated that viewing intention is positively

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connected to viewer–streamer relationships (Hu, Zhang, & Wang, 2017). We therefore

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hypothesize that in a video game streaming context, PSRs to Twitch streamers are positively

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related to enjoyment (H2).

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In a sports broadcasting context, PSRs also contribute to suspense as they lead to more

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rooting for one’s favorite. More precisely, Hartmann, Stuke, and Daschmann (2008) found

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PSRs to contribute to suspense viewing sportscasts via two different facets of PSRs: The first

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was termed Virtual Friendship (VF) and indicates an intimate relationship, in which the

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persona “appears to be like an old friend and an important companion” (p. 28). The second

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factor was termed Respectful Interest (RI) and represents a relationship based on impressions

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“that the viewers have gained during exposure to media” (p. 29). Acknowledging the

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conceptual differences between unidirectional TV-broadcasting and the potentially interactive

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usage of Twitch, it seems difficult to exactly apply these findings to Twitch. However, as

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eSports streaming shows various conceptual overlaps to sports broadcasting (Seo, 2016; Seo

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& Jung, 2016; Taylor, 2012), and in general, strong PSRs might lead to more rooting for


one’s favorite, we predict that suspense elicited by watching streamers playing is a mediator

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of the effect of PSRs to streamers on media enjoyment (H3).

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Interacting With the Community of Users via Chat

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Besides the relationship Twitch users maintain with the streaming person, they also get

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the chance to communicate with other users, share their impressions, and get a sense of the

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common opinion within the chat. Hamilton, Garretson, and Kerne (2014) highlight Twitch as

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a social medium that integrates content and community. Building upon community research

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by McMillan and Chavis (1986), these authors demonstrate that Twitch fulfills all aspects of

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social communities, namely: membership (by subscription to certain streamers), influence

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(via interaction with other viewers and the streamer), need fulfillment (they refer to rewards

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such as sociability, status, success, and gaining of both knowledge and skills), and emotional

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connection to others (through a common history and identification with others that is shaped

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via continuous participation). As the community has one central theme—the game and its

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streamer—all users join the same topic. Moreover, research on media-formed collectives like

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the community of gamers (Kneer, Munko, Glock, & Bente, 2012; Nauroth, Gollwitzer,

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Bender, & Rothmund, 2015) showed that such social collectives are similar to sports fandoms

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created by people cheering for the same team (Hughson & Free, 2006; Parry, Jones, & Wann,

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2015). Similar to a favorite soccer team, shared media preferences are ‘artifacts’ symbolizing

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the solidarity of the collective: Events which are relevant to the collective (e.g., sport contests,

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Sapolsky & Zillmann, 1978) will provide higher enjoyment when they are watched

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collectively than when they are watched individually. Viewing group-relevant content

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together with peers (e.g., in a fan park) provides supplementary gratifications. We assume that

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the chat works in a comparable way: Similar to viewers who watch soccer matches with fans

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of their favorite team, chat users cheer with others for their favorite streamer. Moreover, with

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regard to Hamilton et al. (2014), the chat denotes social community and the possibility to


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participate. Therefore, the chat is a core feature for social interaction and the overall socially

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enjoyable experience using Twitch.

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Besides the social component of texting with others, Twitch’s chat also comprises

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additional functions. For instance, via text commands, the chat can be used to gain specific

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information (e.g., showing moderators of the chat), adjust the personal chat (e.g., hide the

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comments of particular users) or adjust settings (e.g., change text color). An example that

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illustrates the use of commands in a more interactive way was the project ‘Twitch plays

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Pokémon’ in 2014. In this project, users played the game using chat commands (every

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command implied pressing one particular button). The game was finished after more than two

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weeks and attracted about 80,000 viewers (Lin & Wan, 2016). As Twitch’s chat entails a high

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amount of possibilities to engage in, predominantly of social but also of technical nature, we

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propose that this unique feature is positively related to enjoyment (H4). To investigate our

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assumptions, we conducted an online survey and asked Twitch users about their experiences.

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Methods Participants In total, 548 individuals participated in the study. Age ranged from 14 to 43 (M =

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21.77, SD = 5.16), Sample distribution of age seems consistent with age patterns published by

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Twitch (2016b). Most of the participants were male (95.8%). Though Twitch is predominantly

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used by male users (< 20% of users are female, OPGroup.tv, 2015), the gender distribution of

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the sample is biased due to the overrepresentation of males. Almost half of the participants

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(44.3%) had achieved at least a bachelor’s degree. Although the recruiting took place on

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international forums, there was a prevalence of European (51.6%) and North-American

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participants (37.8%), which is in line with Twitch’s viewership (Twitch 2016b). Counter-

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Strike: Global Offensive was the most often watched game by 42.15% of the participants.

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Other popular games were Dota 2 (21.9%), Hearthstone: Heroes of Warcraft (21.0%), and

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League of Legends (16.8%).

TWITCH AND MEDIA ENJOYMENT 1 2

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Procedure Posts with a short study description were submitted to gaming community sites (e.g.,

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reddit.com) and forums dedicated to specific games (e.g., mobafire.com). After reading the

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survey instructions, they were asked about their general usage of Twitch (e.g., with regard to

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favorite games, frequency of using, and the use of functions). Afterwards, they answered

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questions about PSRs to their favorite streamer, perceived suspense, and enjoyment when

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using Twitch. Last, participants indicated their demographics. After finishing the

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questionnaire, participants had the option to take part in a lottery to win Amazon vouchers.

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Materials and Measures

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Regarding all scales, item wordings were in English. If no English version was

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available, items were translated and checked by a native English speaker. Furthermore, the

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order of items was randomized within each questionnaire, and every scale was assessed using

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a 5-point Likert Scale (1 = do not agree at all to 5 = fully agree). As we evaluated our data via

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structural equation modeling (SEM), all scales were modeled as latent constructs and tested

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for factorial validity by applying confirmatory factor analyses (CFA) using the R package

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lavaan (Rosseel et al., 2012) in advance. All constructs had to undergo several adjustments to

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attain a satisfying (Brown, 2015) model fit (see Table 1 for the items used within the final

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model). As data was non-normally distributed, we calculated the Satorra-Bentler rescaled chi-

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square statistic (SB χ2; Satorra & Bentler, 1994) to account for violations of multivariate

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normality. This procedure also provides robust parameter estimates and model fit indices. We

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used robust mean square error of approximation (rRMSEA), cutoff: ≤ .06; standardized root

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mean square of residuals (SRMR), cutoff: ≤ .08; robust comparative fit index (rCFI), cutoff: ≥

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.95; and robust Tucker-Lewis Index (rTLI), cutoff: ≥ .95; see Brown (2015) for selected

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cutoff criteria. With these cutoff criteria, PSR, suspense, and enjoyment scales attained good

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model fits (see Table 2).


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Parasocial relationships. We measured PSRs with the participants’ favorite streamer

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via Hartmann et al.’s (2008) positive PSR scale. This scale measures two aspects of PSR:

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Intimacy of the relation, (virtual friendship, VF; e.g. “I think my favorite racing driver is like

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an old friend,”, 6 items), and a more typical media-bound relationship (respectful interest, RI;

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e.g. “The TV coverage shows me what my favorite racing driver is like,” 7 items). Due to its

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two-factorial structure, and validation in the context of sports, the PSR scale was well suited

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to embrace the different nuances of Twitch. For this study, the scale was adapted to the

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context of Twitch and eSports (see Table 1). To reach a satisfying fit within the CFA, the

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following changes were made: In each of the VF and RI scales, one item was excluded due to

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low factor loadings. In addition, three items of the RI scale and one item of the VR scale were

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excluded due to high correlations with multiple items of the other subscale respectively.

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Suspense. To measure the level of suspense, participants answered an eight-item self-

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assessment scale by Hartmann et al. (2008). Like the PSR scale before, items were adapted to

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fit the topic. As its original focus was on sports contests, only minor changes were necessary,

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like replacing the word race with match. Two items with poor SMC values were excluded.

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Enjoyment. Enjoyment was measured using four items by Reinecke, Vorderer, and

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Knop (2014). This scale was previously used to measure enjoyment of social media

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(Facebook), playing video games, and watching recorded games (Reinecke, Klatt, & Krämer,

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2011). As Twitch combines many of these facets, the scale had a good contextual fit. We

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decided to keep all items, though the rRMSEA was slightly below a good model fit, but still

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satisfying.

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Demographics and Twitch feature usage. Finally, we asked participants which

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technical features they use while viewing Twitch streams (chat function, video, following

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streamers, subscriptions to streams) and assessed demographic data.

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Results

TWITCH AND MEDIA ENJOYMENT 1 2

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To test our hypotheses, we applied structural equation modelling (SEM) using the lavaan package for R (Rosseel, 2012).

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Model fit. We estimated one SEM that addressed all of our hypotheses and research

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questions. The model showed a good fit, SB χ2(130) = 264.750, p < .001, rRMSEA = .046,

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90% CI [.038; .054], SRMR =.063, rCFI = .963, rTLI = .957. The model accounted for

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11.80% of the variance in suspense and 25.30% of the variance in enjoyment.

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Latent regression analysis. To test our hypotheses, we analyzed the regression

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parameters in our model (see Figure 1). Regarding H1, feelings of suspense showed a small

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positive but non-significant connection with media enjoyment (β = .09, p = .069). In line with

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H2, PSRs showed a positive association with media enjoyment (β =.42, p < .001), and a

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positive association with suspense (β = .34, p < .001). However, as the connection between

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suspense and enjoyment was not significant, we rejected the assumption of a possible

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mediation effect (H3). Finally, using the chat (H4) was associated with higher levels of media

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enjoyment (β = .20, p < .001).

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Post-hoc analysis. In H1, we predicted that suspense would be positively connected to

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media enjoyment. The estimated SEM revealed that suspense was no unique predictor for

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enjoyment when controlling for PSRs and using the chat. To investigate whether suspense had

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indeed no association with enjoyment or whether other predictors (i.e. PSRs and the chat)

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shared common variance with suspense and therefore suppressed the predictive power of

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suspense, we followed up our analyses with another explorative SEM that only accounted for

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the effect of suspense on enjoyment. We used the same item structure for enjoyment and

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suspense as before. The model showed a good fit, SB χ2(34) = 54.55, p = .014, rRMSEA =

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.033, 90% CI [.017; .048], SRMR =.029, rCFI = .991, rTLI = .988. Without the other factors,

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suspense showed a significant connection with media enjoyment (β = .26, p