# More than Memes: A Multimodal Topic Modeling Approach to Conspiracy Theories on Telegram

arXiv: https://arxiv.org/abs/2410.08642 · HTML: https://mlsanity.com/paper/2410.08642/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 49 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## Builds on (most cited first, 18 of 18)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [On the Origins of Memes by Means of Fringe Web Communities](https://mlsanity.com/paper/1805.12512/) (2018)
- [A Quantitative Approach to Understanding Online Antisemitism](https://mlsanity.com/paper/1809.01644/) (2018)
- [On the Evolution of (Hateful) Memes by Means of Multimodal Contrastive Learning](https://mlsanity.com/paper/2212.06573/) (2022)
- [On the Globalization of the QAnon Conspiracy Theory Through Telegram](https://mlsanity.com/paper/2105.13020/) (2021)
- [The Pushshift Telegram Dataset](https://mlsanity.com/paper/2001.08438/) (2020)
- [Users Polarization on Facebook and Youtube](https://mlsanity.com/paper/1604.02705/) (2016)
- [Experiments on Generalizability of BERTopic on Multi-Domain Short Text](https://mlsanity.com/paper/2212.08459/) (2022)
- [Introducing an Abusive Language Classification Framework for Telegram to Investigate the German Hater Community](https://mlsanity.com/paper/2109.07346/) (2021)
- [An Exploratory Analysis of COVID Bot vs Human Disinformation Dissemination stemming from the Disinformation Dozen on Telegram](https://mlsanity.com/paper/2402.14203/) (2024)
- [Automatic Discovery of Political Meme Genres with Diverse Appearances](https://mlsanity.com/paper/2001.06122/) (2020)
- [C-CLIP: Contrastive Image-Text Encoders to Close the Descriptive-Commentative Gap](https://mlsanity.com/paper/2309.03921/) (2023)
- [The Conspiracy Money Machine: Uncovering Telegram's Conspiracy Channels and their Profit Model](https://mlsanity.com/paper/2310.15977/) (2023)
- [Unraveling the Italian and English Telegram Conspiracy Spheres through Message Forwarding](https://mlsanity.com/paper/2404.18602/) (2024)
- [Multimodal Pipeline for Collection of Misinformation Data from Telegram](https://mlsanity.com/paper/2204.12690/) (2022)
- [N-Modal Contrastive Losses with Applications to Social Media Data in Trimodal Space](https://mlsanity.com/paper/2403.12747/) (2024)

## Built on by (most cited first, 7 of 7)

- [Multimodal Analysis of State-Funded News Coverage of the Israel-Hamas War on YouTube Shorts](https://mlsanity.com/paper/2604.00994/) (2026)
- [LLM-based Detection of Manipulative Political Narratives](https://mlsanity.com/paper/2605.14354/) (2026)
- [Beyond Semantics: Modeling Factual and Affective Perceptual Experiences from Vision-Language Data](https://mlsanity.com/paper/2606.03345/) (2026)
- [Who's Behind It? Annotating and Extracting Conspiratorial Actors from German Telegram Posts](https://mlsanity.com/paper/2607.04962/) (2026)
- [Beyond a Joke: Multi-Angle Reasoning for Detecting and Explaining Harmful Humor in Memes](https://mlsanity.com/paper/2607.15442/) (2026)
- [Agentic Detection of Online Conspiracies](https://mlsanity.com/paper/2609.30250/) (2026)
- [MemeTrace: A Multimodal LLM-Based Agent for Safety-Aware Meme Provenance Analysis](https://mlsanity.com/paper/2610.19267/) (2026)

## Abstract (arXiv)

To address the increasing prevalence of (audio-)visual data on social media, and to capture the evolving and dynamic nature of this communication, researchers have begun to explore the potential of unsupervised approaches for analyzing multimodal online content. However, existing research often neglects visual content beyond memes, and in addition lacks methods to compare topic models across modalities. Our study addresses these gaps by applying multimodal topic modeling for analyzing conspiracy theories in German-language Telegram channels. We use BERTopic with CLIP for the analysis of textual and visual data in a corpus of ~40, 000 Telegram messages posted in October 2023 in 571 German-language Telegram channels known for disseminating conspiracy theories. Through this dataset, we provide insights into unimodal and multimodal topic models by analyzing symmetry and intersections of topics across modalities. We demonstrate the variety of textual and visual content shared in the channels discovered through the topic modeling, and propose a conceptual framework for the analysis of textual and visual discursive strategies in the communication of conspiracy theories. We apply the framework in a case study of the topic group Israel Gaza.