A Linguistic Incentive Analysis of Heated Online Discussions

Abstract

Heated discussion or conversation in online communities interferes in smooth communications and civil settlements. To prevent such unhealthy upsurge, it is important to understand what is the feature common in the posts which are prone to trigger it. We examined whether there is a connection between heat-provoking posts and linguistic features. First, we constructed a comment dataset consisting of approximately 45,000 comments posted on Japanese Wikipedia community pages. Next, we defined "overheat" phenomenon and five features and calculated feature scores of all comments. Each comment was classified into four or two classes based on the definition of "overheat." In the analysis of comments, we compared these classes using the calculated features. The results of the analysis show that there are certain linguistic differences between these classes.