  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.
