Understanding Conflicts in Online Conversations

Abstract

With the rise of social media, users from across the world are able to connect and converse with each other online. While these connections have facilitated a growth in knowledge, online discussions can also end in acrimonious conflict. Previous computational studies have focused on creating online conflict detection models from inferred labels, primarily examine disagreement but not acrimony, and do not examine the conflict’s emergence. Social science studies have investigated offline conflict, which can differ from its online form, and rarely examines its emergence. The current research aims to understand how online conflicts arise in online personal conversations. Our ground truth is a Facebook tool that allows group members to report conflict to administrators. We contrast discussions ending with a conflict report with paired non-conflict discussions from the same post. We study both user characteristics (e.g., historical user-to-user interactions) and conversation dynamics (e.g., changes in emotional intensity over the course of the conversation). We use logistic regression to identify the features that predict conflict. User characteristics such as the commenter’s gender and previous involvement in negative online activity are strong indicators of conflict. Conversational dynamics, such as an increase in person-oriented discussion, are also important signals of conflict. These results help us understand how conflicts emerge and suggest better detection models and ways to alert group administrators and members early on to mediate the conversation.