Large Language Model (LLM)-driven Adversarial Social Influences in Online Information Spread: Risks and Interventions

People’s online information processing is strongly shaped by social influence, and large language models (LLMs) now enable social bots to manipulate such influence at scale. This paper examines the effects of LLM-driven adversarial social influence—a strategy in which automated agents employ LLMs to distort truth by making misinformation appear credible or by undermining factual news—on how people evaluate and share information. Across two pre-registered, randomized experiments, we first show that exposure to LLM-driven adversarial social influence significantly reduces people’s ability to judge the veracity of news and lowers their discernment between sharing true versus false content. We then test two credibility prompts: AI-generated content detectors and warnings, as potential interventions. Results show that both prompts mitigate some harms such as by improving misinformation detection, though their effectiveness were dependent on the context. We conclude by discussing the risks of LLM-driven adversarial social bots and the implications for designing interventions to combat misinformation.