Bots influence opinion dynamics without direct human-bot interaction: the mediating role of recommender systems

Bots’ ability to influence public discourse is difficult to estimate. Recent studies found that hyperpartisan bots are unlikely to influence public opinion because bots often interact with already highly polarized users. However, previous studies focused on direct human-bot interactions (e.g., retweets, at-mentions, and likes). The present study suggests that political bots, zealots, and trolls may affect people’s views indirectly via the mediating role of a platform’s content recommendation system, thus influencing opinions even in the absence of direct human-bot interaction. Using an agent-based opinion dynamics simulation, we isolated the effect of a single bot – representing 1% of nodes in a network – on the opinion formed by rational Bayesian agents after removing direct human-bot connections. We compare this experimental condition with an identical baseline condition where such a bot is absent. We used the same random seed in both simulations so that conditions remained identical except for the presence of the bot. Results show that, even in the absence of direct interactions, the mere presence of the bot is sufficient to shift the average population opinion. Virtually all nodes – not only nodes directly interacting with the bot – shifted towards more extreme opinions. Overall, these findings offer a proof of concept that bots and hyperpartisan accounts can influence population opinions not only by directly interacting with humans but also by secondary effects, such as shifting platforms’ recommendation engines’ internal representations. The mediating role of recommender systems creates indirect causal pathways of algorithmic opinion manipulation.