Analyzing Uncivil Speech Provocation and Implicit Topics in Online Political News

TL;DR

This paper develops methods to analyze incivility and implicit discussion topics in online political news comments, using classifiers and topic inference techniques to understand reader reactions and provocations.

Contribution

It introduces a dataset of political news comments, classifiers for uncivil speech, and a novel method to identify implicit discussion topics triggered by news articles.

Findings

Classifiers effectively predict incivility in comments.

Articles can be linked to reader incivility without explicit mentions.

The approach reveals hidden discussion topics in reader comments.

Abstract

Online news has made dissemination of information a faster and more efficient process. Additionally, the shift from a print medium to an online interface has enabled user interactions, creating a space to mutually understand the reader responses generated by the consumption of news articles. Intermittently, the positive environment is transformed into a hate-spewing contest, with the amount and target of incivility varying depending on the specific news website in question. In this paper, we develop methods to study the emergence of incivility within the reader communities in news sites. First, we create a dataset of political news articles and their reader comments from partisan news sites. Then, we train classifiers to predict different aspects of uncivil speech in comments. We apply these classifiers to predict whether a news article is likely to provoke a substantial portion of reader comments containing uncivil language by analyzing only the article's content. Finally, we devise a technique to "read between the lines" --- finding the topics of discussions that an article triggers among its readers without frequent, explicit mentions of these topics in its content.