@inproceedings{shimgekar-etal-2026-belief,
title = "Belief Is All You Need: Signed Belief Graph Neural Networks for Topic Modeling in Conspiratorial Discourse",
author = "Shimgekar, Soorya Ram and
Goyal, Abhay and
Lee, Roy Ka-Wei and
Saha, Koustuv and
Zonooz, Pi and
Tandoc Jr, Edson C and
Kumar, Navin",
editor = "Card, Dallas and
Field, Anjalie and
Keith, Katherine and
Mendelsohn, Julia",
booktitle = "Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science",
month = jul,
year = "2026",
address = "San Diego",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.nlpcss-1.20/",
pages = "341--355",
ISBN = "979-8-89176-426-2",
abstract = "Conspiratorial discourse is increasingly present in online communication, yet how it is organized across discussion topics remains unclear. We analyze Singapore-based Telegram groups to examine how conspiratorial content appears within everyday conversations rather than isolated echo chambers. To better capture the structure of such discussions, we propose a two-stage framework for topic modeling tailored to conspiratorial posts. First, a RoBERTa-large classifier identifies conspiratorial messages (F1 = 0.866) using 2,000 expert-annotated examples. We then construct a graph where connections reflect textual similarity and conspiratorial stance. This graph is modeled using a Signed Belief Graph Neural Network (SiBeGNN), which learns message embeddings that distinguish conspiratorial from non-conspiratorial content. We apply hierarchical clustering on these embeddings to perform topic modeling over 553,648 Telegram messages, producing seven topic clusters: General Legal Topics, Medical Concerns, Media Discussions, Banking and Finance, Contradictions in Authority, Group Moderation, and General Discussions. Our method substantially outperforms standard embedding-based clustering approaches (cDBI = 8.38 vs. 13.60{--}67.27), with manual evaluation showing 88{\%} inter-rater agreement in cluster interpretation. The results show that conspiratorial content appears across multiple everyday topics, including finance, law, and daily life, rather than forming isolated thematic communities."
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<abstract>Conspiratorial discourse is increasingly present in online communication, yet how it is organized across discussion topics remains unclear. We analyze Singapore-based Telegram groups to examine how conspiratorial content appears within everyday conversations rather than isolated echo chambers. To better capture the structure of such discussions, we propose a two-stage framework for topic modeling tailored to conspiratorial posts. First, a RoBERTa-large classifier identifies conspiratorial messages (F1 = 0.866) using 2,000 expert-annotated examples. We then construct a graph where connections reflect textual similarity and conspiratorial stance. This graph is modeled using a Signed Belief Graph Neural Network (SiBeGNN), which learns message embeddings that distinguish conspiratorial from non-conspiratorial content. We apply hierarchical clustering on these embeddings to perform topic modeling over 553,648 Telegram messages, producing seven topic clusters: General Legal Topics, Medical Concerns, Media Discussions, Banking and Finance, Contradictions in Authority, Group Moderation, and General Discussions. Our method substantially outperforms standard embedding-based clustering approaches (cDBI = 8.38 vs. 13.60–67.27), with manual evaluation showing 88% inter-rater agreement in cluster interpretation. The results show that conspiratorial content appears across multiple everyday topics, including finance, law, and daily life, rather than forming isolated thematic communities.</abstract>
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%0 Conference Proceedings
%T Belief Is All You Need: Signed Belief Graph Neural Networks for Topic Modeling in Conspiratorial Discourse
%A Shimgekar, Soorya Ram
%A Goyal, Abhay
%A Lee, Roy Ka-Wei
%A Saha, Koustuv
%A Zonooz, Pi
%A Tandoc Jr, Edson C.
%A Kumar, Navin
%Y Card, Dallas
%Y Field, Anjalie
%Y Keith, Katherine
%Y Mendelsohn, Julia
%S Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego
%@ 979-8-89176-426-2
%F shimgekar-etal-2026-belief
%X Conspiratorial discourse is increasingly present in online communication, yet how it is organized across discussion topics remains unclear. We analyze Singapore-based Telegram groups to examine how conspiratorial content appears within everyday conversations rather than isolated echo chambers. To better capture the structure of such discussions, we propose a two-stage framework for topic modeling tailored to conspiratorial posts. First, a RoBERTa-large classifier identifies conspiratorial messages (F1 = 0.866) using 2,000 expert-annotated examples. We then construct a graph where connections reflect textual similarity and conspiratorial stance. This graph is modeled using a Signed Belief Graph Neural Network (SiBeGNN), which learns message embeddings that distinguish conspiratorial from non-conspiratorial content. We apply hierarchical clustering on these embeddings to perform topic modeling over 553,648 Telegram messages, producing seven topic clusters: General Legal Topics, Medical Concerns, Media Discussions, Banking and Finance, Contradictions in Authority, Group Moderation, and General Discussions. Our method substantially outperforms standard embedding-based clustering approaches (cDBI = 8.38 vs. 13.60–67.27), with manual evaluation showing 88% inter-rater agreement in cluster interpretation. The results show that conspiratorial content appears across multiple everyday topics, including finance, law, and daily life, rather than forming isolated thematic communities.
%U https://aclanthology.org/2026.nlpcss-1.20/
%P 341-355
Markdown (Informal)
[Belief Is All You Need: Signed Belief Graph Neural Networks for Topic Modeling in Conspiratorial Discourse](https://aclanthology.org/2026.nlpcss-1.20/) (Shimgekar et al., NLP+CSS 2026)
ACL