Belief Is All You Need: Modeling Narrative Archetypes in Conspiratorial Discourse

TL;DR

This paper introduces a novel computational framework combining message classification and belief graph neural networks to analyze and identify narrative archetypes in conspiratorial discourse within digital communication, revealing their integration into everyday discussions.

Contribution

It presents a new two-stage method for classifying conspiratorial messages and modeling belief alignment, uncovering diverse narrative archetypes in large-scale social media data.

Findings

Conspiratorial messages are embedded in routine discussions, not just isolated echo chambers.

The SiBeGNN model outperforms baseline clustering methods in identifying belief-based narrative groups.

The framework supports applications in stance detection, political communication, and content moderation.

Abstract

Conspiratorial discourse is increasingly embedded within digital communication ecosystems, yet its structure and spread remain difficult to study. This work analyzes conspiratorial narratives in Singapore-based Telegram groups, showing that such content is woven into everyday discussions rather than confined to isolated echo chambers. We propose a two-stage computational framework. First, we fine-tune RoBERTa-large to classify messages as conspiratorial or not, achieving an F1-score of 0.866 on 2,000 expert-labeled messages. Second, we build a signed belief graph in which nodes represent messages and edge signs reflect alignment in belief labels, weighted by textual similarity. We introduce a Signed Belief Graph Neural Network (SiBeGNN) that uses a Sign Disentanglement Loss to learn embeddings that separate ideological alignment from stylistic features. Using hierarchical clustering on these embeddings, we identify seven narrative archetypes across 553,648 messages: legal topics, medical concerns, media discussions, finance, contradictions in authority, group moderation, and general chat. SiBeGNN yields stronger clustering quality (cDBI = 8.38) than baseline methods (13.60 to 67.27), supported by 88 percent inter-rater agreement in expert evaluations. Our analysis shows that conspiratorial messages appear not only in clusters focused on skepticism or distrust, but also within routine discussions of finance, law, and everyday matters. These findings challenge common assumptions about online radicalization by demonstrating that conspiratorial discourse operates within ordinary social interaction. The proposed framework advances computational methods for belief-driven discourse analysis and offers applications for stance detection, political communication studies, and content moderation policy.