Don't just watch, join in: Exploring information behavior and copresence on Twitch. (April 2020)
- Record Type:
- Journal Article
- Title:
- Don't just watch, join in: Exploring information behavior and copresence on Twitch. (April 2020)
- Main Title:
- Don't just watch, join in: Exploring information behavior and copresence on Twitch
- Authors:
- Diwanji, Vaibhav
Reed, Abigail
Ferchaud, Arienne
Seibert, Jonmichael
Weinbrecht, Victoria
Sellers, Nicholas - Abstract:
- Abstract: This mixed-methods study examined users' information behavior and their perceptions of copresence on Twitch.tv, where millions of people come together live every day to stream, interact, and make their own entertainment. Human information theory model and social identity theory constituted the theoretical framework for this research. Studying topic specific live streaming services is an emerging and exciting field in communication. Topic specific live streaming sites such as Twitch.tv are evolving constantly into important sources of information that complement the traditional information systems such as libraries and online search engine sites like Google. Chat logs of three live streams on Twitch.tv were analyzed using Linguistic Query and Word Count (LIWC) and SPSS statistical tools. Qualitative thematic analysis was carried out using Nvivo 12. Information reaction and production were the most frequent information behaviors across three streams. Qualitative analysis indicated that users within the three live streams showed a great deal of copresence. This study is an important first step to provide theoretical insights into understanding human information behavior on Twitch, topic specific live streaming sites, and social live streaming sites in general. Highlights: Human information behavior is not limited to information seeking. Information reaction and production were the most frequent information behaviors. Users treat livestreams as community space,Abstract: This mixed-methods study examined users' information behavior and their perceptions of copresence on Twitch.tv, where millions of people come together live every day to stream, interact, and make their own entertainment. Human information theory model and social identity theory constituted the theoretical framework for this research. Studying topic specific live streaming services is an emerging and exciting field in communication. Topic specific live streaming sites such as Twitch.tv are evolving constantly into important sources of information that complement the traditional information systems such as libraries and online search engine sites like Google. Chat logs of three live streams on Twitch.tv were analyzed using Linguistic Query and Word Count (LIWC) and SPSS statistical tools. Qualitative thematic analysis was carried out using Nvivo 12. Information reaction and production were the most frequent information behaviors across three streams. Qualitative analysis indicated that users within the three live streams showed a great deal of copresence. This study is an important first step to provide theoretical insights into understanding human information behavior on Twitch, topic specific live streaming sites, and social live streaming sites in general. Highlights: Human information behavior is not limited to information seeking. Information reaction and production were the most frequent information behaviors. Users treat livestreams as community space, creating copresence. Users act as both information producers and information receivers. Contributes to scholarly understanding of livestreaming as emerging media behavior. … (more)
- Is Part Of:
- Computers in human behavior. Volume 105(2020)
- Journal:
- Computers in human behavior
- Issue:
- Volume 105(2020)
- Issue Display:
- Volume 105, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 105
- Issue:
- 2020
- Issue Sort Value:
- 2020-0105-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-04
- Subjects:
- Human information behavior -- Copresence -- Twitch -- Topic specific live streaming services
Interactive computer systems -- Periodicals
Man-machine systems -- Periodicals
004.019 - Journal URLs:
- http://www.sciencedirect.com/science/journal/07475632 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.chb.2019.106221 ↗
- Languages:
- English
- ISSNs:
- 0747-5632
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3394.921600
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 12918.xml