Leveraging Large Language Models to Detect Influence Campaigns in Social Media

TL;DR

This paper introduces a novel detection method using Large Language Models that integrates user metadata and network structures to identify influence campaigns on social media, demonstrating superior performance across multiple datasets.

Contribution

It presents a new approach leveraging LLMs with metadata and network data, enhancing detection of influence campaigns in multilingual and evolving social media environments.

Findings

Superior detection accuracy on multiple datasets

Effective handling of multilingual social media content

Adaptability to evolving influence tactics

Abstract

Social media influence campaigns pose significant challenges to public discourse and democracy. Traditional detection methods fall short due to the complexity and dynamic nature of social media. Addressing this, we propose a novel detection method using Large Language Models (LLMs) that incorporates both user metadata and network structures. By converting these elements into a text format, our approach effectively processes multilingual content and adapts to the shifting tactics of malicious campaign actors. We validate our model through rigorous testing on multiple datasets, showcasing its superior performance in identifying influence efforts. This research not only offers a powerful tool for detecting campaigns, but also sets the stage for future enhancements to keep up with the fast-paced evolution of social media-based influence tactics.