What distinguishes conspiracy from critical narratives? A computational analysis of oppositional discourse

TL;DR

This paper introduces a novel annotation scheme and multilingual dataset to differentiate conspiracy theories from critical narratives, emphasizing the role of inter-group conflict and emotional tone in distinguishing them.

Contribution

It presents a new annotation framework, a multilingual corpus, and baseline NLP experiments to better distinguish conspiracy from critical discourse on social media.

Findings

Inter-group conflict and emotional tone are key to differentiating narratives.

The multilingual corpus enables cross-lingual analysis of COVID-19 related messages.

Baseline models show promising results in automatic classification.

Abstract

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.