The diffusion of a task recommendation system to facilitate contributions to an online community

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The diffusion of a task recommendation system to facilitate contributions to an online community
Authors: Y. Connie Yuan, Daniel Regis Cosley, Howard T. Welser, Ling Xia, Geri Gay [edit item]
Citation: Journal of Computer-Mediated Communication 15 (1): 32-59. 2009 October.
Publication type: Journal article
Peer-reviewed: Yes
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Google Scholar cites: Citations
Link(s): Paper link
Added by Wikilit team: Added on initial load
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The diffusion of a task recommendation system to facilitate contributions to an online community is a publication by Y. Connie Yuan, Daniel Regis Cosley, Howard T. Welser, Ling Xia, Geri Gay.


[edit] Abstract

This paper studies the diffusion of {SuggestBot}, an intelligent task recommendation system that helps people find articles to edit in Wikipedia. We investigate factors that predict who adopts {SuggestBot} and its impact on adopters' future contributions to this online community. Analyzing records of participants' activities in Wikipedia, we found that both individual characteristics and social ties influence adoption. Specially, we found that highly involved contributors were more likely to adopt {SuggestBot;} interpersonal exposure to innovation, cohesion, and tie homophily all substantially increased the likelihood of adoption. However, connections to prominent, high-status contributors did not influence adoption. Finally, although the {SuggestBot} innovation saw limited distribution, adopters made significantly more contributions to Wikipedia after adoption than nonadopter counterparts in the comparison group.

[edit] Research questions

"This paper studies the diffusion of SuggestBot, an intelligent task recommendation system that helps people find articles to edit in Wikipedia. We investigate factors that predict who adopts SuggestBot and its impact on adopters' future contributions to this online community."

Research details

Topics: Other antecedents of participation, Collaboration software [edit item]
Domains: Computer science, Information systems [edit item]
Theory type: Analysis, Explanation [edit item]
Wikipedia coverage: Case [edit item]
Theories: "Building on the theory of collective action (Olson, 1965, Marwell & Oliver, 1993), SuggestBot uses a strategy called “intelligent task routing” to reduce a person's cost of finding articles to work on by recommending articles that both need attention and that are similar to articles that person has edited in the past3. Such articles are likely to be close to a person's interests, making it easier for them to contribute.

following Burke and Reitzes's (1991) identity theory of commitment, when commitment to a group is reinforced with a clearly assigned role, commitment to a role identity can further motivate engagement in activities that are associated with the role of a highly committed member (p.242) Monge and Contractor (2003) summarize two main lines of reasoning that support the theory of homophily, including Byrne's (1971) similarity-attraction hypothesis and Turner's (1987) theory of self-categorization. The similarity-attraction hypothesis predicts that people are more likely to interact with those who share similar traits. The theory of self-categorization proposes that people tend to categorize themselves and others in terms of race, gender, age, education, interests, and so on." [edit item]

Research design: Statistical analysis [edit item]
Data source: Wikipedia pages [edit item]
Collected data time dimension: Cross-sectional [edit item]
Unit of analysis: User [edit item]
Wikipedia data extraction: Live Wikipedia [edit item]
Wikipedia page type: User:talk [edit item]
Wikipedia language: Not specified [edit item]

[edit] Conclusion

"Analyzing records of participants' activities in Wikipedia, we found that both individual characteristics and social ties influence adoption. Specially, we found that highly involved contributors were more likely to adopt SuggestBot; interpersonal exposure to innovation, cohesion, and tie homophily all substantially increased the likelihood of adoption. However, connections to prominent, high-status contributors did not influence adoption. Finally, although the SuggestBot innovation saw limited distribution, adopters made significantly more contributions to Wikipedia after adoption than nonadopter counterparts in the comparison group."

[edit] Comments


Further notes[edit]

Facts about "The diffusion of a task recommendation system to facilitate contributions to an online community"RDF feed
AbstractThis paper studies the diffusion of {SuggeThis paper studies the diffusion of {SuggestBot}, an intelligent task recommendation system that helps people find articles to edit in Wikipedia. We investigate factors that predict who adopts {SuggestBot} and its impact on adopters' future contributions to this online community. Analyzing records of participants' activities in Wikipedia, we found that both individual characteristics and social ties influence adoption. Specially, we found that highly involved contributors were more likely to adopt {SuggestBot;} interpersonal exposure to innovation, cohesion, and tie homophily all substantially increased the likelihood of adoption. However, connections to prominent, high-status contributors did not influence adoption. Finally, although the {SuggestBot} innovation saw limited distribution, adopters made significantly more contributions to Wikipedia after adoption than nonadopter counterparts in the comparison group.pter counterparts in the comparison group.
Added by wikilit teamAdded on initial load +
Collected data time dimensionCross-sectional +
ConclusionAnalyzing records of participants' activitAnalyzing records of participants' activities in Wikipedia, we found that both individual characteristics and social ties influence adoption. Specially, we found that highly involved contributors were more likely to adopt SuggestBot; interpersonal exposure to innovation, cohesion, and tie homophily all substantially increased the likelihood of adoption. However, connections to prominent, high-status contributors did not influence adoption. Finally, although the SuggestBot innovation saw limited distribution, adopters made significantly more contributions to Wikipedia after adoption than nonadopter counterparts in the comparison group.pter counterparts in the comparison group.
Data sourceWikipedia pages +
Google scholar urlhttp://scholar.google.com/scholar?ie=UTF-8&q=%22The%2Bdiffusion%2Bof%2Ba%2Btask%2Brecommendation%2Bsystem%2Bto%2Bfacilitate%2Bcontributions%2Bto%2Ban%2Bonline%2Bcommunity%22 +
Has authorY. Connie Yuan +, Daniel Regis Cosley +, Howard T. Welser +, Ling Xia + and Geri Gay +
Has domainComputer science + and Information systems +
Has topicOther antecedents of participation + and Collaboration software +
Issue1 +
MonthOctober +
Pages32-59 +
Peer reviewedYes +
Publication typeJournal article +
Published inJournal of Computer-Mediated Communication +
Research designStatistical analysis +
Research questionsThis paper studies the diffusion of SuggesThis paper studies the diffusion of SuggestBot, an intelligent task recommendation system that helps people find articles to edit in Wikipedia. We investigate factors that predict who adopts SuggestBot and its impact on adopters' future contributions to this online community.re contributions to this online community.
Revid10,972 +
TheoriesBuilding on the theory of collective actioBuilding on the theory of collective action (Olson, 1965, Marwell & Oliver, 1993), SuggestBot uses a strategy called “intelligent task routing” to reduce a person's cost of finding articles to work on by recommending articles that both need attention and that are similar to articles that person has edited in the past3. Such articles are likely to be close to a person's interests, making it easier for them to contribute.

following Burke and Reitzes's (1991) identity theory of commitment, when commitment to a group is reinforced with a clearly assigned role, commitment to a role identity can further motivate engagement in activities that are associated with the role of a highly committed member (p.242)

Monge and Contractor (2003) summarize two main lines of reasoning that support the theory of homophily, including Byrne's (1971) similarity-attraction hypothesis and Turner's (1987) theory of self-categorization. The similarity-attraction hypothesis predicts that people are more likely to interact with those who share similar traits. The theory of self-categorization proposes that people tend to categorize themselves and others in terms of race, gender, age, education, interests, and so on.
der, age, education, interests, and so on.
Theory typeAnalysis + and Explanation +
TitleThe diffusion of a task recommendation system to facilitate contributions to an online community
Unit of analysisUser +
Urlhttp://onlinelibrary.wiley.com/doi/10.1111/j.1083-6101.2009.01491.x/full +
Volume15 +
Wikipedia coverageCase +
Wikipedia data extractionLive Wikipedia +
Wikipedia languageNot specified +
Wikipedia page typeUser:talk +
Year2009 +