Community-based question answering platforms can be rich sources of information on a variety of specialized topics, from finance to cooking. The usefulness of such platforms depends heavily on user contributions (questions and answers), but also on respecting the community rules. As a crowd-sourced service, such platforms rely on their users for monitoring and flagging content that violates community rules. Common wisdom is to eliminate the users who receive many flags. Our analysis of a year of traces from a mature Q&A site shows that the number of flags does not tell the full story: on one hand, users with many flags may still contribute positively to the community. On the other hand, users who never get flagged are found to violate community rules and get their accounts suspended. This analysis, however, also shows that abusive users are betrayed by their network properties: we find strong evidence of homophilous behavior and use this finding to detect abusive users who go under the community radar. Based on our empirical observations, we build a classifier that is able to detect abusive users with an accuracy as high as 83 .
Qualitative and quantitative studies of bad behavior in online settings have been done before including newsgroups @cite_1 , online chat communities @cite_4 , and online multiplayer video games @cite_25 . A body of work also investigates the impact of the bad behavior. Researchers find that bad behavior has negative effects on the community and its members: it decreases community's cohesion @cite_18 , performance @cite_2 and participation @cite_30 . In the worst case, users who are the targets of bad behavior may leave or avoid online social spaces @cite_30 .
@cite_30 @cite_18 @cite_4 @cite_1 @cite_2 @cite_25
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A wide range of behavior may be seen as destructive to online communities. Yet behavior that is ‘bad’ in one community may be celebrated in another. The work of community maintenance is therefore strongly contextual, involving complex choices due to differing norms, community cross-membership, and the need to invoke fairness. The experienced, “lived in” work of moderators; how they enact norms and make choices about social maintenance, remains poorly understood. Our study addresses this gap, using a negotiated order lens. We employed netnographic techniques, analyzing online interviews with moderators of sub-communities in Reddit, and records of critical incidents. We find that moderators are intuitive prosecutors who draw on a variety of logics to accomplish their work. Controlling bad behavior, articulating and enforcing norms is, therefore, a collective accomplishment through which moderators make choices, create a jurisprudence record, and reconcile nested community norms in the maintenance of social order. 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. Online communities have the potential to be supportive, cruel, or anywhere in between. The development of positive norms for interaction can help users build bonds, grow, and learn. Using millions of messages sent in Twitch chatrooms, we explore the effectiveness of methods for encouraging and discouraging specific behaviors, including taking advantage of imitation effects through setting positive examples and using moderation tools to discourage antisocial behaviors. Consistent with aspects of imitation theory and deterrence theory, users imitated examples of behavior that they saw, and more so for behaviors from high status users. Proactive moderation tools, such as chat modes which restricted the ability to post certain content, proved effective at discouraging spam behaviors, while reactive bans were able to discourage a wider variety of behaviors. This work considers the intersection of tools, authority, and types of behaviors, offering a new frame through which to consider the development of moderation strategies. In this work we explore cyberbullying and other toxic behavior in team competition online games. Using a dataset of over 10 million player reports on 1.46 million toxic players along with corresponding crowdsourced decisions, we test several hypotheses drawn from theories explaining toxic behavior. Besides providing large-scale, empirical based understanding of toxic behavior, our work can be used as a basis for building systems to detect, prevent, and counter-act toxic behavior.