  How can citizens address hate in online discourse? We analyze a large corpus
of more than 130,000 discussions on Twitter over four years. With the help of
human annotators, language models and machine learning classifiers, we identify
different dimensions of discourse that might be related to the probability of
hate speech in subsequent tweets. 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 without insults, relates to
the least hate in subsequent discussions. Sarcasm can be helpful as well, in
particular in the presence of organized extreme groups. 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 discourse quality. We obtain
similar results for other measures of quality of discourse beyond hate speech,
including toxicity, extremity of speech, and the presence of extreme speakers.
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.
