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

N. Pescetelli, D. Barkoczi, M. Cebrian

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


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 indirectly affect people’s views via a platform’s content recommendation system's mediating role, thus influencing opinions without 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 of rational Bayesian agents when a simple recommendation system mediates the agents’ content consumption. We compare this experimental condition with an identical baseline condition where such a bot is absent. Across conditions, we use the same random seed and a psychologically realistic Bayesian opinion update rule so that conditions remain identical except for the bot presence. Results show that, even with limited direct interactions, the mere presence of the bot is sufficient to shift the average population’s opinion. Virtually all nodes—not only nodes directly interacting with the bot—shifted towards more extreme opinions. Furthermore, the mere bot’s presence significantly affected the internal representation of the recommender system. 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.

Original languageEnglish (US)
Article number46
JournalApplied Network Science
Issue number1
StatePublished - Dec 2022

All Science Journal Classification (ASJC) codes

  • General
  • Computer Networks and Communications
  • Computational Mathematics


  • Bayesian belief update
  • Bots
  • Opinion dynamics
  • Recommender systems
  • Social influence


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