Detection of Conspiracy Theories on Telegram

Abstract

In today's digitally connected world, the spread of conspiracy theories on social media poses a significant challenge to societal trust and public discourse. This thesis aims to develop a model for identifying conspiracy theories on Telegram, a platform known for its private nature and the use of channels to disseminate information. The main research question guiding this study is: *How can conspiracy theories be identified on Telegram?* To address this, the research leverages graph theory, machine learning, and topic modeling.

The first step involved modeling the network structure of Telegram channels using graph theory. Channels were represented as nodes, and forwarded messages as directed, weighted edges, allowing for the analysis of network structures and the identification of influential channels. Various centrality measures, including weighted degree centrality, betweenness centrality, and viral message centrality, were computed to assess the influence of channels within the network.

A fine-tuned multilingual BERT (m-BERT) model was used to classify Telegram messages as either conspiracy-related or not. This model was trained on a manually labeled dataset and demonstrated robust performance.

To identify specific conspiracy theories, BERTopic, a topic modeling technique, was applied to the messages classified as conspiracy-related. The resulting topics were then analyzed using the OpenAI API, which linked them to known conspiracy theories. The study found that all identified topics could be connected to existing conspiracy theories, suggesting that the model is effective in detecting these narratives on Telegram.

The research also included the validation of the model using a fictive conspiracy theory, "The Verdant Shadow Conspiracy," created specifically for this purpose. This validation demonstrated the model's ability to detect even simulated conspiracies, though it highlighted the importance of continuous refinement and expansion of the dataset.

The contributions of this thesis are twofold: scientifically, it advances the understanding of how to model and analyze Telegram networks to detect conspiracy theories, and societally, it offers a framework that can be used to monitor and potentially mitigate the spread of harmful misinformation. Future research should focus on expanding the dataset, refining the model, and exploring interdisciplinary approaches to further enhance the detection and understanding of conspiracy theories on Telegram.