Measuring and Forecasting Conversation Incivility: the Role of Antisocial and Prosocial Behaviors
Xinchen Yu, Hayden Arnold, Benjamin Su, Eduardo Blanco

TL;DR
This paper develops new metrics to measure conversation incivility after hate speech replies, analyzes language patterns, and forecasts incivility levels, aiming to promote civil discourse and prevent escalation.
Contribution
It introduces novel metrics for incivility measurement, improves alignment with human perception, and analyzes language and interaction patterns related to antisocial and prosocial behaviors.
Findings
New metrics better align with human perceptions
Language patterns differ between antisocial and prosocial posts
Forecasting incivility remains a challenging task
Abstract
This paper focuses on the task of measuring and forecasting incivility in conversations following replies to hate speech. Identifying replies that steer conversations away from hatred and elicit civil follow-up conversations sheds light into effective strategies to engage with hate speech and proactively avoid further escalation. We propose new metrics that take into account various dimensions of antisocial and prosocial behaviors to measure the conversation incivility following replies to hate speech. Our best metric aligns with human perceptions better than prior work. Additionally, we present analyses on a) the language of antisocial and prosocial posts, b) the relationship between antisocial or prosocial posts and user interactions, and c) the language of replies to hate speech that elicit follow-up conversations with different incivility levels. We show that forecasting the…
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Taxonomy
TopicsBullying, Victimization, and Aggression · Hate Speech and Cyberbullying Detection · Media Influence and Health
