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 a help of human annotators, language models, machine learning classifiers, and longitudinal statistical analyses, we discern the dynamics of different dimensions 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. More 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 in the long run. 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.

翻译：公民如何对在线讨论中的仇恨、毒性及极端言论进行治理？我们分析了德国推特上涵盖移民危机与政治动荡的四年动荡期内超过13万条讨论语料。借助人工标注者、语言模型、机器学习分类器及纵向统计分析，我们揭示了不同话语维度的动态特征。研究发现：表达简单观点（虽未必基于事实但无侮辱性言辞）与后续讨论中最低程度的仇恨、毒性及发言者极端化倾向相关；讽刺手法也有助于实现上述目标，尤其在存在组织化极端群体时。更具建设性的评论（例如提供事实或揭露矛盾）可能适得其反，反而吸引更多极端言论。长期来看，提及内群体或外群体通常会导致话语质量恶化。强烈的情感基调——无论是愤怒、恐惧等负面情绪，还是热情、自豪等正面情绪——同样会加剧不良后果。本研究超越了对小规模语料的单次分析，为通过集体公民治理有效管理在线公共空间提供了启示。