Tracking and Identifying International Propaganda and Influence Networks Online

Abstract

Misinformation and propaganda undermine trust in institutions, spread falsehoods, and sometimes incite violence. However, recent advancements in transformer-based AI models can help combat the proliferation of disinformation globally and in real time. In this work, I propose and develop a system using these models to scalably identify, track, and analyze the spread of narratives from over 40,000 international news websites. First, by employing novel multilingual Matryoshka embeddings and hierarchical level-wise clustering, my proposed system identifies news stories, topics, and themes across these thousands of news websites. Second, by utilizing multilingual stance detection, my system assesses the biases and factual inconsistencies in news articles, enabling the identification of websites that spread propaganda or misinformation. Finally, through network inference methods, my system uncovers connections among websites disseminating slanted or false content. My approach illustrates how AI can be utilized to mitigate the global spread of harmful misinformation and propaganda.