Agonism plays a vital role in democratic dialogue by fostering diverse perspectives and robust discussions. Within the realm of online conflict there is another type: hateful antagonism, which undermines constructive dialogue. Detecting conflict online is central to platform moderation and monetization. It is also vital for democratic dialogue, but only when it takes the form of agonism. To model these two types of conflict, we collected Twitter conversations related to trending controversial topics. We introduce a comprehensive annotation schema for labelling different dimensions of conflict in the conversations, such as the source of conflict, the target, and the rhetorical strategies deployed. Using this schema, we annotated approximately 4,000 conversations with multiple labels. We then trained both logistic regression and transformer-based models on the dataset, incorporating context from the conversation, including the number of participants and the structure of the interactions. Results show that contextual labels are helpful in identifying conflict and make the models robust to variations in topic. Our research contributes a conceptualization of different dimensions of conflict, a richly annotated dataset, and promising results that can contribute to content moderation.

翻译：对抗性在民主对话中扮演着重要角色，通过培养多元视角和激烈的讨论来促进对话。在线冲突领域存在另一种类型：仇恨敌对性，它破坏了建设性对话。检测在线冲突对于平台审核和盈利至关重要。它对于民主对话也至关重要，但前提是它采取对抗性形式。为了对这两种冲突进行建模，我们收集了与热门争议话题相关的Twitter对话。我们引入了一个全面的标注方案，用于标记对话中冲突的不同维度，例如冲突源、目标以及所采用的修辞策略。使用该方案，我们对约4000段对话进行了多标签标注。然后，我们在该数据集上训练了逻辑回归和基于Transformer的模型，并结合了对话中的上下文信息，包括参与者数量和互动结构。结果表明，上下文标签有助于识别冲突，并使模型对话题变化具有鲁棒性。我们的研究贡献了冲突不同维度的概念化、一个丰富标注的数据集，以及可为内容审核提供帮助的前沿成果。