Collective moderation of hate, toxicity, and extremity in online discussions

How can citizens moderate hate, toxicity, and extremism in online discourse? We analyze a large corpus of more than 130,000 discussions on German Twitter over the turbulent four years marked by the migrant crisis and political upheavals. With the help of human annotators, language models and machine learning classifiers, we identify different dimensions of discourse. We use a matching approach and longitudinal statistical analyses to discern the effectiveness of different counter speech strategies on the micro-level (individual tweet pairs), meso-level (discussion trees) and macro-level (days) of discourse. We find that expressing simple opinions, not necessarily supported by facts, but also without insults, relates to the least hate, toxicity, and extremity of speech and speakers in subsequent discussions. Sarcasm also helps in achieving those outcomes, in particular in the presence of organized extreme groups on the meso-level. Constructive comments such as providing facts or exposing contradictions can backfire and attract more extremity. Mentioning either outgroups or ingroups is typically related to a deterioration of discourse. A pronounced emotional tone, either negative such as anger or fear, or positive such as enthusiasm and pride, also leads to worse outcomes. Going beyond one-shot analyses on smaller samples of discourse, our findings have implications for the successful management of online commons through collective civic moderation.