United States

Deceptive online networks are coordinated efforts that use identity deception to pursue strategic political or financial goals. During the US 2020 elections, these networks reached at least 37 million Facebook and 3 million Instagram users, representing 15% and 2% of the platforms’ active US adult users, respectively. Only 3 networks out of 49—1 network with explicitly political aims and 2 that appeared to use politics as a lure for profit—were responsible for over 70% of users reached. Notably, accounts unaffiliated with the networks played an important role in facilitating this reach by resharing content the three networks produced. Deceptive networks, regardless of whether their goals were political or financial, reached users who were older, more conservative, more frequently exposed to content from untrustworthy sources, and spent more time on Facebook.

Generative artificial intelligence agents are becoming increasingly active participants in conversations on social media platforms, yet little is known about how they shape public discussion of social problems. We present two preregistered online experiments testing AI-generated summaries in simulated, interactive social media environments. AI summaries increased the quality of user comments, without systematically increasing toxicity or negative affect. At the same time, AI exposure reduced engagement with conversation threads. AI summaries also increased the semantic similarity between user comments and the AI-generated summaries, suggesting that these systems function as informational anchors that shape discussion. Together, the findings reveal a tradeoff: AI-generated summaries can improve conversation quality while narrowing conversational engagement and channeling how users articulate political arguments. These results speak to growing concerns about how embedded AI systems fundamentally alter platform dynamics and shape public discourse.