  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.
