Modeling Narrative Archetypes in Conspiratorial Narratives: Insights from Singapore-Based Telegram Groups

• URL: http://arxiv.org/abs/2512.10105v1
• Date: Wed, 10 Dec 2025 21:51:16 GMT
• Title: Modeling Narrative Archetypes in Conspiratorial Narratives: Insights from Singapore-Based Telegram Groups
• Authors: Soorya Ram Shimgekar, Abhay Goyal, Lam Yin Cheung, Roy Ka-Wei Lee, Koustuv Saha, Pi Zonooz, Navin Kumar,
• Abstract summary: This work analyzes conspiratorial narratives in Singapore-based Telegram groups.<n>It shows that such content is woven into everyday discussions rather than confined to isolated echo chambers.<n>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.
• Score: 14.545582048682911
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Fine-grained Narrative Classification in Biased News Articles [10.412867371293629]
  We propose a novel fine-grained narrative classification in biased news articles.<n>We also explore article-bias classification as the precursor task to narrative classification.<n>We develop INDI-PROP, the first ideologically grounded fine-grained narrative dataset.
  arXiv  Detail & Related papers  (2025-12-03T09:07:52Z)
• Latent Topic Synthesis: Leveraging LLMs for Electoral Ad Analysis [51.95395936342771]
  We introduce an end-to-end framework for automatically generating an interpretable topic taxonomy from an unlabeled corpus.<n>We apply this framework to a large corpus of Meta political ads from the month ahead of the 2024 U.S. Presidential election.<n>Our approach uncovers latent discourse structures, synthesizes semantically rich topic labels, and annotates topics with moral framing dimensions.
  arXiv  Detail & Related papers  (2025-10-16T20:30:20Z)
• ConspirED: A Dataset for Cognitive Traits of Conspiracy Theories and Large Language Model Safety [87.90209836101353]
  ConspirED is the first dataset of conspiratorial content annotated for general cognitive traits.<n>We develop computational models that identify conspiratorial traits and determine dominant traits in text excerpts.<n>We evaluate large language/reasoning model (LLM/LRM) robustness to conspiratorial inputs.
  arXiv  Detail & Related papers  (2025-08-28T06:39:25Z)
• Talking Point based Ideological Discourse Analysis in News Events [62.18747509565779]
  We propose a framework motivated by the theory of ideological discourse analysis to analyze news articles related to real-world events.<n>Our framework represents the news articles using a relational structure - talking points, which captures the interaction between entities, their roles, and media frames along with a topic of discussion.<n>We evaluate our framework's ability to generate these perspectives through automated tasks - ideology and partisan classification tasks, supplemented by human validation.
  arXiv  Detail & Related papers  (2025-04-10T02:52:34Z)
• What distinguishes conspiracy from critical narratives? A computational analysis of oppositional discourse [42.0918839418817]
  We propose a novel topic-agnostic annotation scheme that distinguishes between conspiracies and critical texts. We also contribute with the multilingual XAI-DisInfodemics corpus (English and Spanish), which contains a high-quality annotation of Telegram messages.
  arXiv  Detail & Related papers  (2024-07-15T14:18:47Z)
• Unveiling Social Media Comments with a Novel Named Entity Recognition System for Identity Groups [2.5849042763002426]
  We develop a Named Entity Recognition (NER) System for Identity Groups. Our tool not only detects whether a sentence contains an attack but also tags the sentence tokens corresponding to the mentioned group. We tested the utility of our tool in a case study on social media, annotating and comparing comments from Facebook related to news mentioning identity groups.
  arXiv  Detail & Related papers  (2024-05-13T19:33:18Z)
• Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social-Text Classification [27.884015521888458]
  Hyphen is a discourse-aware hyperbolic spectral co-attention network. We show that Hyphen generalises well and achieves state-of-the-art results on ten benchmark datasets.
  arXiv  Detail & Related papers  (2022-09-15T16:04:32Z)
• Author Clustering and Topic Estimation for Short Texts [69.54017251622211]
  We propose a novel model that expands on the Latent Dirichlet Allocation by modeling strong dependence among the words in the same document. We also simultaneously cluster users, removing the need for post-hoc cluster estimation. Our method performs as well as -- or better -- than traditional approaches to problems arising in short text.
  arXiv  Detail & Related papers  (2021-06-15T20:55:55Z)
• Discourse Parsing of Contentious, Non-Convergent Online Discussions [0.16311150636417257]
  Inspired by the Bakhtinian theory of Dialogism, we propose a novel theoretical and computational framework. We develop a novel discourse annotation schema which reflects a hierarchy of discursive strategies. We share the first labeled dataset of contentious non-convergent online discussions.
  arXiv  Detail & Related papers  (2020-12-08T17:36:39Z)
• Paragraph-level Commonsense Transformers with Recurrent Memory [77.4133779538797]
  We train a discourse-aware model that incorporates paragraph-level information to generate coherent commonsense inferences from narratives. Our results show that PARA-COMET outperforms the sentence-level baselines, particularly in generating inferences that are both coherent and novel.
  arXiv  Detail & Related papers  (2020-10-04T05:24:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.