Joshua Garland; Center for Strategic Communication and Center on Narrative, Disinformation and Strategic Influence; Arizona State University

Impact and dynamics of hate and counter speech online

Hateful rhetoric is plaguing online discourse, fostering extreme societal movements and possibly giving rise to real-world violence. A potential solution to this growing global problem is citizen-generated counter speech where citizens actively engage with hate speech to restore civil non-polarized discourse. The effectiveness of counter speech in curbing the spread of hatred is unknown and hard to quantify. This is, in part, due to no large-scale studies of the dynamics between hate and counter speakers. To this end, we leveraged a unique situation in Germany where self-labeling groups engaged in organized online hate and counter speech to develop an ensemble-learning based classification algorithm trained on a corpus of millions of utterances of hate and counter speech from these two groups. Applying this system to 180,000 complete political conversations that took place on German Twitter over four years we are able to study changes in the dynamics of political discourse as well as investigate the effectiveness of counter speech using several different macro- and micro-level measures. We report on the dynamic interactions of hate and counter speech over time and provide insights into whether, as in ‘classic’ bullying situations, organized efforts are more effective than independent individuals in steering online discourse. Taken together, our results build a multifaceted picture of the dynamics of hate and counter speech online.