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：公民如何在在线讨论中调节仇恨、极端与有毒言论？我们分析了德国推特上超过13万条讨论的大型语料库，时间跨度涵盖移民危机与政治动荡的四年动荡期。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变化。研究发现：表达简单观点（虽未必有事实依据但无侮辱性言论）与后续讨论中最低程度的仇恨、有毒言论及极端性呈正相关；讽刺性表达亦有助于实现上述效果，尤其在存在组织化极端团体时更为显著。更具建设性的评论（如提供事实或揭示矛盾）可能适得其反，反而吸引更多极端言论。长期来看，提及外群体或内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）同样会引发更差的结果。本研究突破了对小样本话语的单次分析局限，为通过集体公民调节实现网络公共空间的有效管理提供了启示。