Online conversations can go in many directions: some turn out poorly due to antisocial behavior, while others turn out positively to the benefit of all. Research on improving online spaces has focused primarily on detecting and reducing antisocial behavior. Yet we know little about positive outcomes in online conversations and how to increase them—is a prosocial outcome simply the lack of antisocial behavior or something more? Here, we examine how conversational features lead to prosocial outcomes within online discussions. We introduce a series of new theory-inspired metrics to define prosocial outcomes such as mentoring and esteem enhancement. Using a corpus of 26M Reddit conversations, we show that these outcomes can be forecasted from the initial comment of an online conversation, with the best model providing a relative 24% improvement over human forecasting performance at ranking conversations for predicted outcome. Our results indicate that platforms can use these early cues in their algorithmic ranking of early conversations to prioritize better outcomes.
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WikiConv: A Corpus of the Complete Conversational History of a Large Online Collaborative Community
We present a corpus that encompasses the complete history of conversations between
contributors to Wikipedia, one of the largest online collaborative communities. By recording the intermediate states of conversations—including not only comments and replies, but also their modifications, deletions and restorations—this data offers an unprecedented view of online conversation. This level of detail supports new research questions pertaining to the process (and challenges) of large-scale online collaboration. We illustrate the corpus’ potential with two case studies that highlight new perspectives on earlier work. First, we explore how a person’s conversational behavior depends on how they relate to the discussion’s venue. Second, we show that community moderation of toxic behavior
happens at a higher rate than previously estimated. Finally the reconstruction framework
is designed to be language agnostic, and we show that it can extract high quality conversational data in both Chinese and English.
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- NSF-PAR ID:
- 10098199
- Date Published:
- Journal Name:
- Proceedings of the Conference on Empirical Methods in Natural Language Processing
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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