Leveraging Large Language Models to Detect Influence Campaigns in Social Media
This research addresses the challenge of influence campaigns for public discourse and democracy, offering a powerful detection tool, though it appears incremental as it builds on existing LLM capabilities for a specific domain.
The paper tackles the problem of detecting social media influence campaigns by proposing a novel method using Large Language Models (LLMs) that incorporates user metadata and network structures, achieving superior performance in identifying influence efforts as validated on multiple datasets.
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.