对话顺利进行：量化和预测在线对话中的亲社会结果
Conversations Gone Alright: Quantifying and Predicting Prosocial Outcomes in Online Conversations
摘要：
在线对话可以有很多方向：有些是因为反社会行为而变得不好，而另一些则是对所有人都有利的。改善在线空间的研究主要集中在检测和减少反社会行为。然而，我们对在线对话的积极结果以及如何增加积极结果知之甚少亲社会结果仅仅是缺乏反社会行为还是更多？在这里，我们研究如何会话功能导致亲社会的结果在网上讨论。我们引入了一系列新的理论启发的指标来定义亲社会的结果，如指导和自尊增强。使用2600万Reddit对话的语料库，我们表明这些结果可以从在线对话的初始评论中预测，最佳模型在对预测结果进行排名时，比人类预测性能提高了24%。我们的研究结果表明，平台可以在早期对话的算法排名中使用这些早期线索，以优先考虑更好的结果。
英文摘要：
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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