Browsing by Subject "peer production"

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    Newcomer Retention and Productivity in Online Peer-Production Communities
    (2018-07) Karumur, Raghav Pavan Srivatsav
    Online communities are online interaction spaces for people that break the barriers of time, space, and scale and provide opportunities for companionship and social support, information exchange, retail, and entertainment. Among them are online peer production communities that have a fantastic business model where volunteers come together to produce content and drive traffic to these sites. Although as a class these communities are successful, the success of individual communities greatly varies. To become and remain successful, these communities must meet a number of challenges related to starting communities, retention of members, encouraging commitment, and contribution from their members, regulating the behavior of members and so on. This dissertation focuses on the specific challenge of newcomer retention and productivity in the context of online peer-production communities. Exploring three different communities with entirely different structures and compositions – MovieLens, GitHub, and Wikipedia and building upon prior work in this space, this dissertation offers a number of important predictors of retention and productivity of newcomers. First, this dissertation explores the value of early activity diversity in the presence of the amount of early activity as a predictor of newcomer retention. Second, this dissertation digs into more fundamental psychological traits of newcomers such as personality and presents findings on relationships between personality and newcomer retention, preferences, and productivity. Third, this dissertation explores and presents results on the relationship between community interactions (apart from norms, policies and rigid structures) and newcomer retention. Fourth, this dissertation studies and presents the effects of various kinds of prior experience of newcomers on retention and productivity in a new group they join. This dissertation concludes by offering a number of directions for future research.
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    Understanding Geographic Bias in Crowd Systems
    (2017-12) Thebault-Spieker, Jacob
    Crowd platforms are increasingly geographic, from the sharing economy to peer production systems like OpenStreetMap. Unfortunately, this means that existing geographic advantages or disadvantages (e.g. by income, urbanness, or race) may also impact these crowd systems. This thesis focuses on two primary themes: (1) how these geographic advantages and disadvantages interact with crowd platform services, and (2) how people’s geographic behavior within these platforms may lead to these biases being reflected. The first chapter in my thesis finds that sharing economy services fare less well in low-income, non-white, and more suburban areas. This chapter introduces the spatial Durbin model to the field of HCI, and shows that geographic factors like distance, socioeconomic status and demographics inform where sharing economy workers provide service. The second chapter in my thesis provides focuses on people in peer production communities contribute geographic content. By considering peer production as a spatial interaction process, this study finds that some kinds of content tend to be produced much more locally than others. Finally, my third contribution focuses on individual contributor behavior, and shows geographic “born, not made” trends. People tend to be consistent in the places, and kinds of places (urban, and non-high poverty counties) they contribute. The findings of this third study help identify mechanisms for how geographic biases may come about. Looking forward, my work helps inform an exciting agenda of future work, including building systems that provide individual crowd members sufficient geographic context to counteract these worrying geographic biases.