Community norm violations can impair constructive communication and collaboration online. As a defense mechanism, community moderators often address such transgressions by temporarily blocking the perpetrator. Such actions, however, come with the cost of potentially alienating community members. Given this tradeoff, it is essential to understand to what extent, and in which situations, this common moderation practice is effective in reinforcing community rules. In this work, we introduce a computational framework for studying the future behavior of blocked users on Wikipedia. After their block expires, they can take several distinct paths: they can reform and adhere to the rules, but they can also recidivate, or straight-out abandon the community. We reveal that these trajectories are tied to factors rooted both in the characteristics of the blocked individual and in whether they perceived the block to be fair and justified. Based on these insights, we formulate a series of prediction tasks aiming to determine which of these paths a user is likely to take after being blocked for their first offense, and demonstrate the feasibility of these new tasks. Overall, this work builds towards a more nuanced approach to moderation by highlighting the tradeoffs that are in play.
Norms and engagement A major factor governing engagement in online communities is a sense of belonging @cite_54 , which in many communities engenders the emergence of community-specific norms, such as specific patterns of language @cite_51 @cite_28 @cite_48 @cite_14 . Wikipedia is no exception: it relies on group dynamics to promote editing productivity @cite_18 and quality @cite_56 , as well as participation in governance @cite_52 @cite_40 .
@cite_18 @cite_14 @cite_28 @cite_48 @cite_54 @cite_52 @cite_56 @cite_40 @cite_51
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Online communities serve various important functions, but many fail to thrive. Research on community success has traditionally focused on internal factors. In contrast, we take an ecological view to understand how the success of a community is influenced by other communities. We measured a community's relationship with other communities - its "niche" - through four dimensions: topic overlap, shared members, content linking, and shared offline organizational affiliation. We used a mixed-method approach, combining the quantitative analysis of 9495 online enterprise communities and interviews with community members. Our results show that too little or too much overlap in topic with other communities causes a community's activity to suffer. We also show that this main result is moderated in predictable ways by whether the community shares members with, links to content in, or shares an organizational affiliation with other communities. These findings provide new insight on community success, guiding online community designers on how to effectively position their community in relation to others. The rapidly increasing popularity of Web 2.0 online communities originates in the ease of collaborative content creation and its sharing. As a result, more community members actively participate in the community and its data growth rates are continuously increasing. This poses the challenge for community platform operators on efficient administration and moderation to ensure the quality of content and to prevent violations of laws (e.g. copyright, privacy, illegal content) and community rules. Involvement of employed administrators who read and approve every piece of user-generated content is clearly the safest way of quality assurance. Since this is a time consuming task it does not scale up with Web 2.0 dimensions. So administrative functions are delegated to members of the community, the moderators. The strategy for choosing trustworthy moderators in big anonymous communities is specified in policies based on user reputation that is measured in bonus points. The proper balancing between community self-management and administration is crucial for the quality, attractiveness, and scalability of the entire community. Therefore understanding the mutual influences between community actors, reputation systems and platform policies that employ user reputation is crucial to ensure the overall quality, user acceptance, and success of the entire online community. Our objective is to predict the behavior in an online community for different policies, which may result in different overall quality of the community content. For this purpose we present our community analysis framework CoSiMo (an acronym for Community Simulation and Modeling), which employs the model-based approach for predicting the impact of policies on community dynamics and health. Through systematic variation of simulated quality assurance mechanisms we show that our model plausibly captures the influence of policies to content quality and can be therefore exploited for optimization of real online communities. Citizen participation in online communities of scientific investigations has recently become more popular. Enhancing the engagement of citizens within these communities is a focus of attention for researchers and practitioners who want to amplify the impact on learning, science and society. This study investigates the relationship between engagement factors and behaviour patterns in an online community that requires high levels of citizen participation. While other studies explore engagement in communities where citizens contribute data, the current research investigates a community to support citizens in facilitating their own scientific investigations. Data were collected from log files and questionnaires, and multiple measures of engagement were examined: engagement metrics, roles, motivation, attitude, satisfaction and belonging to the community. The results allowed comparison of the engagement levels among different types of citizen participation communities and categorised members in engagement profiles, according to their behaviour patterns. Findings indicate a need for differing design approaches based on the type of citizen participation community and individual engagement profiles. A community's identity defines and shapes its internal dynamics. Our current understanding of this interplay is mostly limited to glimpses gathered from isolated studies of individual communities. In this work we provide a systematic exploration of the nature of this relation across a wide variety of online communities. To this end we introduce a quantitative, language-based typology reflecting two key aspects of a community's identity: how distinctive, and how temporally dynamic it is. By mapping almost 300 Reddit communities into the landscape induced by this typology, we reveal regularities in how patterns of user engagement vary with the characteristics of a community. Our results suggest that the way new and existing users engage with a community depends strongly and systematically on the nature of the collective identity it fosters, in ways that are highly consequential to community maintainers. For example, communities with distinctive and highly dynamic identities are more likely to retain their users. However, such niche communities also exhibit much larger acculturation gaps between existing users and newcomers, which potentially hinder the integration of the latter. More generally, our methodology reveals differences in how various social phenomena manifest across communities, and shows that structuring the multi-community landscape can lead to a better understanding of the systematic nature of this diversity.