The current prevalence of conspiracy theories on the internet is a significant issue, tackled by many computational approaches. However, these approaches fail to recognize the relevance of distinguishing between texts which contain a conspiracy theory and texts which are simply critical and oppose mainstream narratives. Furthermore, little attention is usually paid to the role of inter-group conflict in oppositional narratives. We contribute by proposing a novel topic-agnostic annotation scheme that differentiates between conspiracies and critical texts, and that defines span-level categories of inter-group conflict. We also contribute with the multilingual XAI-DisInfodemics corpus (English and Spanish), which contains a high-quality annotation of Telegram messages related to COVID-19 (5,000 messages per language). We also demonstrate the feasibility of an NLP-based automatization by performing a range of experiments that yield strong baseline solutions. Finally, we perform an analysis which demonstrates that the promotion of intergroup conflict and the presence of violence and anger are key aspects to distinguish between the two types of oppositional narratives, i.e., conspiracy vs. critical.

翻译：当前互联网上阴谋论的盛行是一个重要问题，许多计算方法试图解决这一问题。然而，这些方法未能认识到区分包含阴谋论的文本与仅持批判态度、反对主流叙事的文本之间的相关性。此外，对立性叙事中群体间冲突的作用通常很少受到关注。我们的贡献在于提出了一种新颖的与主题无关的标注方案，该方案能够区分阴谋论文本与批判性文本，并定义了群体间冲突的片段级类别。我们还贡献了多语言XAI-DisInfodemics语料库（英语和西班牙语），其中包含与COVID-19相关的Telegram消息的高质量标注（每种语言5,000条消息）。通过进行一系列实验并获得强大的基线解决方案，我们证明了基于自然语言处理的自动化方法的可行性。最后，我们通过分析表明，促进群体间冲突以及暴力和愤怒情绪的存在是区分两种对立性叙事（即阴谋论与批判性叙事）的关键方面。