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 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.The study was funded by the Max Planck Institute for Human Development. D.B. was partly funded by a research grant from the Institute of Psychology at the Chinese Academy of Sciences

Beyond collective intelligence: Collective adaptation

Data accessibility: This article has no additional data.Copyright © 2023 The Authors. We develop a conceptual framework for studying collective adaptation in complex socio-cognitive systems, driven by dynamic interactions of social integration strategies, social environments and problem structures. Going beyond searching for ‘intelligent’ collectives, we integrate research from different disciplines and outline modelling approaches that can be used to begin answering questions such as why collectives sometimes fail to reach seemingly obvious solutions, how they change their strategies and network structures in response to different problems and how we can anticipate and perhaps change future harmful societal trajectories. We discuss the importance of considering path dependence, lack of optimization and collective myopia to understand the sometimes counterintuitive outcomes of collective adaptation. We call for a transdisciplinary, quantitative and societally useful social science that can help us to understand our rapidly changing and ever more complex societies, avoid collective disasters and reach the full potential of our ability to organize in adaptive collectives.This research originated at a workshop at the Santa Fe Institute, funded by the National Science Foundation grant NSF BCS 1745154. A.M.B. was supported by the H. Mason Keeler Endowed Professorship in Sports Fisheries Management. C.G. was partially supported by the Defense Advanced Research Projects Agency, award no. FP00002636. M.G. was partially supported by NSF DRMS 1757211. M.G. and H.O. were partially supported by NSF SocPsych 1918490. E.L. was partially supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT) (no. RS-2022-00165916)