All the feels

Robinson, Raquel; Rubin, Zachary; Segura, Elena Márquez; Isbister, Katherine

doi:10.1145/3102071.3102103

Cited by 35 publications

(6 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These investigations have identified that most streamers utilize similar methods of communicating with their audience, including microphone and webcam (Sjöblom et al, 2019), and that this interaction with viewers is seen as highly important (Lessel et al, 2018). In conjunction with the audiovisual elements that appear in Twitch streams, work has been done in order to facilitate automatization and the detection of key events within the streams (Chu and Chou, 2017), as well as to develop additional tools to assist streamers (Browne and Batra, 2018;Lessel, Vielhauer, et al, 2017;Robinson et al, 2017). The development of such tools has not only been limited to streamers, but also more widely to help both developers and viewers understand the dynamics between the spectator and the streamer (Pan et al, 2016), and to improve how content is recommended to users (Lin and Chen, 2018).…”

Section: Live Video Game Streaming and Twitchmentioning

confidence: 99%

Social motivations of live-streaming viewer engagement on Twitch

Hilvert-Bruce

Neill

Sjöblom

et al. 2018

Computers in Human Behavior

558

420

View full text Add to dashboard Cite

Section: Live Video Game Streaming and Twitchmentioning

confidence: 99%

Social motivations of live-streaming viewer engagement on Twitch

Hilvert-Bruce

Neill

Sjöblom

et al. 2018

Computers in Human Behavior

558

420

View full text Add to dashboard Cite

“…Signals such as heart rate, electrical activity of the brain, and galvanic skin response have all been explored in game contexts to infer player experience [17,18]. Although physiological data may not be easily accessible at the resolution gathered by medical-grade devices, novel methods of gathering physiological data in situ continue to emerge; sensors are embedded into game controllers for use in biofeedback games [19], to engage spectators [20], or simply as an innovative game input [21]. Further, physiological signals such as heart rate [22], blink rate, and facial expressions [23] can be derived from webcam-based signals along with facial expressions.…”

Section: Primer On Games and Game Datamentioning

confidence: 99%

The Potential of Game-Based Digital Biomarkers for Modeling Mental Health

Mandryk

Birk

2019

JMIR Ment Health

View full text Add to dashboard Cite

Background Assessment for mental health is performed by experts using interview techniques, questionnaires, and test batteries and following standardized manuals; however, there would be myriad benefits if behavioral correlates could predict mental health and be used for population screening or prevalence estimations. A variety of digital sources of data (eg, online search data and social media posts) have been previously proposed as candidates for digital biomarkers in the context of mental health. Playing games on computers, gaming consoles, or mobile devices (ie, digital gaming) has become a leading leisure activity of choice and yields rich data from a variety of sources. Objective In this paper, we argue that game-based data from commercial off-the-shelf games have the potential to be used as a digital biomarker to assess and model mental health and health decline. Although there is great potential in games developed specifically for mental health assessment (eg, Sea Hero Quest), we focus on data gathered “in-the-wild” from playing commercial off-the-shelf games designed primarily for entertainment. Methods We argue that the activity traces left behind by natural interactions with digital games can be modeled using computational approaches for big data. To support our argument, we present an investigation of existing data sources, a categorization of observable traits from game data, and examples of potentially useful game-based digital biomarkers derived from activity traces. Results Our investigation reveals different types of data that are generated from play and the sources from which these data can be accessed. Based on these insights, we describe five categories of digital biomarkers that can be derived from game-based data, including behavior, cognitive performance, motor performance, social behavior, and affect. For each type of biomarker, we describe the data type, the game-based sources from which it can be derived, its importance for mental health modeling, and any existing statistical associations with mental health that have been demonstrated in prior work. We end with a discussion on the limitations and potential of data from commercial off-the-shelf games for use as a digital biomarker of mental health. Conclusions When people play commercial digital games, they produce significant volumes of high-resolution data that are not only related to play frequency, but also include performance data reflecting low-level cognitive and motor processing; text-based data that are indicative of the affective state; social data that reveal networks of relationships; content choice data that imply preferred genres; and contextual data that divulge where, when, and with whom the players are playing. These data provide a source for digital biomarkers that may indicate mental health. Produced by engaged human behavior, game data have the potential to be leveraged for population screening or prevalence estimations, leading to at-scale, nonintrusive assessment of mental health.

show abstract

“…These studies mostly come from two areas of research: computer science and engineering. Their focus is more on providing solutions to problems, as such, they particularly rely on experiments [ 73 , 74 , 75 ], the development of applications, and applied engineering [ 76 , 77 , 78 , 79 ]. Therefore, they do not need a strong grounding in well-established theories, although some do occasionally appear; for example, the Lyapunov optimization theory [ 16 ], the psychophysiological approach [ 80 ], Blommaert’s framework of critical discourse analysis, and Computer-mediated discourse analysis (CMDA) [ 81 ].…”

Section: Resultsmentioning

confidence: 99%

“…There is a wide variety of topics addressed and a wealth of related applications being developed. Among the topics that have been covered, we found the development of functionalities to enhance communication [ 134 , 137 ], immersive user experiences through the implementation of multi-view systems [ 93 ], resource allocation to facilitate adaptive live streaming [ 141 ], and the application of the streamer’s biometric data to enhance the viewer’s experience [ 79 ]. Another significant group of papers in this cluster dealt with how to reduce bandwidth overheads and operating costs [ 16 , 131 , 133 , 138 ], broadcast times and latency [ 19 ], or metrics to optimize video quality [ 20 ].…”

Section: Resultsmentioning

confidence: 99%

Exploring the Emerging Domain of Research on Video Game Live Streaming in Web of Science: State of the Art, Changes and Trends

Cabeza-Ramírez

García

Fernández

2021

IJERPH

View full text Add to dashboard Cite

In recent years, interest in video game live streaming services has increased as a new communication instrument, social network, source of leisure, and entertainment platform for millions of users. The rise in this type of service has been accompanied by an increase in research on these platforms. As an emerging domain of research focused on this novel phenomenon takes shape, it is necessary to delve into its nature and antecedents. The main objective of this research is to provide a comprehensive reference that allows future analyses to be addressed with greater rigor and theoretical depth. In this work, we developed a meta-review of the literature supported by a bibliometric performance and network analysis (BPNA). We used the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) protocol to obtain a representative sample of 111 published documents since 2012 and indexed in the Web of Science. Additionally, we exposed the main research topics developed to date, which allowed us to detect future research challenges and trends. The findings revealed four specializations or subdomains: studies focused on the transmitter or streamer; the receiver or the audience; the channel or platform; and the transmission process. These four specializations add to the accumulated knowledge through the development of six core themes that emerge: motivations, behaviors, monetization of activities, quality of experience, use of social networks and media, and gender issues.

show abstract

All the feels

Cited by 35 publications

References 11 publications

Social motivations of live-streaming viewer engagement on Twitch

Social motivations of live-streaming viewer engagement on Twitch

The Potential of Game-Based Digital Biomarkers for Modeling Mental Health

Exploring the Emerging Domain of Research on Video Game Live Streaming in Web of Science: State of the Art, Changes and Trends

Contact Info

Product

Resources

About