Detecting Multidimensional Political Incivility on Social Media
This work addresses the challenge of understanding and detecting uncivil political discourse on social media, which is an incremental advance in social media analysis.
The authors tackled the problem of detecting political incivility on social media by differentiating between impoliteness and political intolerance, achieving state-of-the-art results on a dataset of 13K political tweets and improving performance by incorporating social context information about tweet authors.
The rise of social media has been argued to intensify uncivil and hostile online political discourse. Yet, to date, there is a lack of clarity on what incivility means in the political sphere. In this work, we utilize a multidimensional perspective of political incivility, developed in the fields of political science and communication, that differentiates between impoliteness and political intolerance. We present state-of-the-art incivility detection results using a large dataset of 13K political tweets, collected and annotated per this distinction. Applying political incivility detection at large-scale, we observe that political incivility demonstrates a highly skewed distribution over users, and examine social factors that correlate with incivility at subpopulation and user-level. Finally, we propose an approach for modeling social context information about the tweet author alongside the tweet content, showing that this leads to improved performance on the task of political incivility detection. We believe that this latter result holds promise for socially-informed text processing in general.