User contributions in the form of posts, comments, and votes are essential to the success of online communities. However, allowing user participation also invites undesirable behavior such as trolling. In this paper, we characterize antisocial behavior in three large online discussion communities by analyzing users who were banned from these communities. We find that such users tend to concentrate their efforts in a small number of threads, are more likely to post irrelevantly, and are more successful at garnering responses from other users. Studying the evolution of these users from the moment they join a community up to when they get banned, we find that not only do they write worse than other users over time, but they also become increasingly less tolerated by the community. Further, we discover that antisocial behavior is exacerbated when community feedback is overly harsh. Our analysis also reveals distinct groups of users with different levels of antisocial behavior that can change over time. We use these insights to identify antisocial users early on, a task of high practical importance to community maintainers.
The damage personal attacks cause to online discourse motivates many platforms to try to curb the phenomenon. However, understanding the prevalence and impact of personal attacks in online platforms at scale remains surprisingly difficult. The contribution of this paper is to develop and illustrate a method that combines crowdsourcing and machine learning to analyze personal attacks at scale. We show an evaluation method for a classifier in terms of the aggregated number of crowd-workers it can approximate. We apply our methodology to English Wikipedia, generating a corpus of over 100k high quality human-labeled comments and 63M machine-labeled ones from a classifier that is as good as the aggregate of 3 crowd-workers, as measured by the area under the ROC curve and Spearman correlation. Using this corpus of machine-labeled scores, our methodology allows us to explore some of the open questions about the nature of online personal attacks. This reveals that the majority of personal attacks on Wikipedia are not the result of a few malicious users, nor primarily the consequence of allowing anonymous contributions from unregistered users.
Despite the egalitarian rhetoric surrounding online cultural production, profound gender inequalities remain in who contributes to one of the most visited Web sites worldwide, Wikipedia. In analyzing this persistent disparity, previous research has focused on aspects of current contributors and the existing Wikipedia community. We draw on unique panel survey data of young adults with information about both Wikipedia contributors and non-contributors. We examine the role of people's background attributes and Internet skills in participation on the site. We find that the gender gap in editing is exacerbated by a similarly significant Internet skills gap. Our results show that the most likely contributors are high-skilled males and that among low-skilled Internet users no gender gap in Wikipedia contributions exists. Our findings suggest that efforts to understand the gender gap must also take Internet skills into account.
Wikipedia may be the best‐developed attempt thus far to gather all human knowledge in one place. Its accomplishments in this regard have made it a point of inquiry for researchers from different fields of knowledge. A decade of research has thrown light on many aspects of the Wikipedia community, its processes, and its content. However, due to the variety of fields inquiring about Wikipedia and the limited synthesis of the extensive research, there is little consensus on many aspects of Wikipedia's content as an encyclopedic collection of human knowledge. This study addresses the issue by systematically reviewing 110 peer‐reviewed publications on Wikipedia content, summarizing the current findings, and highlighting the major research trends. Two major streams of research are identified: the quality of Wikipedia content (including comprehensiveness, currency, readability, and reliability) and the size of Wikipedia. Moreover, we present the key research trends in terms of the domains of inquiry, research design, data source, and data gathering methods. This review synthesizes scholarly understanding of Wikipedia content and paves the way for future studies.
Internet users’ participation and contributions are critical to the growth of Wikipedia. Based on self-determination theory, this study investigates the impacts of several motivational factors on two different types of user behaviors: content contribution and community participation. The research findings show that content contribution is more often driven by extrinsically oriented motivations, including reciprocity and the need for self-development, while community participation is more often driven by intrinsically oriented motivations, including altruism and a sense of belonging to the community. This paper contributes empirically to the research on Wikipedia, and it has practical implications for open content system development and management.