2021/02/16 by Jiajun Bao, Junjie Wu, Yiming Zhang +2
Computer Science · Physics and Astronomy · Social Sciences · #Conversation #Hate Speech and Cyberbullying Detection #Opinion Dynamics and Social Influence #Outcome (game theory) #Prosocial behavior #Ranking (information retrieval) #Social Media and Politics #Social media #The Internet #cs.CL #cs.CY #cs.SI
paper · pdf · doi:10.1145/3442381.3450122
Accepted for Publication at the Web Conference 2021; 12 pages
arxiv created 2021/02/16 · arxiv updated 2021/02/17 · openalex created_date 2021/03/01 · openalex publication_date 2021/04/19 · openalex updated_date 2026/08/05
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.