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

TL;DR

This paper introduces a multimodal topic modeling approach using BERTopic and CLIP to analyze conspiracy theories on Telegram, capturing both textual and visual content and their intersections.

Contribution

It develops a novel framework for analyzing multimodal conspiracy content, addressing gaps in visual content analysis and cross-modal topic comparison.

Findings

Identified diverse textual and visual conspiracy themes

Analyzed symmetry and intersections of topics across modalities

Provided insights into discursive strategies in conspiracy communication

Abstract

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.