  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.
