Large language models (LLMs) can generate human-like online comments at scale, raising concerns that coordinated AI campaigns could manipulate public opinion. While prior work has shown that single AI messages can persuade individual readers, it remains untested whether coordinated multi-agent LLM campaigns, in which multiple agents jointly plan, divide roles, and manage visibility, can shift reader opinions in news comment sections, and whether per-comment AI labels can mitigate such influence. We address these questions through a controlled experiment with 1,000 U.S. adults reading news articles on a Reddit-like platform. Each participant was randomly assigned to one of six conditions, crossing three comment sources (human, independently generated AI, or coordinated multi-agent LLM) with two label settings (per-comment "Likely AI-generated" badges from a deployed detector, shown or hidden). Coordinated LLM comments did not increase average persuasion over independently generated AI comments, but disproportionately converted readers who initially disagreed and compressed opinion diversity by 12%, manufacturing apparent consensus where genuine disagreement existed. The labels reduced the perceived believability of comments but did not reliably reduce actual opinion change. These findings reveal what we call a Knowing-Resisting dissociation between source recognition and behavioral resistance, and indicate that per-comment disclosure alone is insufficient to counter coordinated LLM opinion campaigns.

Do LLMs have political leanings and are LLMs able to shift our political views? This paper explores these questions in the context of the 2024 U.S. presidential election. Through a voting simulation, we demonstrate 18 open-weight and closed-source LLMs’ political preference for Biden over Trump. We show how Biden-leaning becomes more pronounced in instruction-tuned and reinforced models compared to their base versions by analyzing their responses to political questions related to the two nominees. We further explore the potential impact of LLMs on voter choice by recruiting 935 U.S. registered voters. Participants interacted with LLMs (Claude-3, Llama-3, and GPT-4) over five exchanges. Intriguingly, although LLMs were not asked to persuade users to support Biden, about 20% of Trump supporters reduced their support for Trump after LLM interaction. This result is noteworthy given that many studies on the persuasiveness of political campaigns have shown minimal effects in presidential elections. Many users also expressed a desire for further interaction with LLMs on political subjects. Further research on how LLMs affect users’ political views is required, as their use becomes more widespread.