Optimizing Influence Campaigns: Nudging under Bounded Confidence
This work addresses the challenge of effectively influencing large audiences in social networks for organizations and political entities, though it is incremental as it builds on existing bounded confidence models.
The paper tackled the problem of optimizing influence campaigns in online social networks under bounded confidence, where audiences are only swayed by narrow viewpoints, by developing a nudging policy using control theory and showing it outperforms other techniques in simulations on real Twitter networks, shifting mean opinion and decreasing polarization.
Influence campaigns in online social networks are often run by organizations, political parties, and nation states to influence large audiences. These campaigns are employed through the use of agents in the network that share persuasive content. Yet, their impact might be minimal if the audiences remain unswayed, often due to the bounded confidence phenomenon, where only a narrow spectrum of viewpoints can influence them. Here we show that to persuade under bounded confidence, an agent must nudge its targets to gradually shift their opinions. Using a control theory approach, we show how to construct an agent's nudging policy under the bounded confidence opinion dynamics model and also how to select targets for multiple agents in an influence campaign on a social network. Simulations on real Twitter networks show that a multi-agent nudging policy can shift the mean opinion, decrease opinion polarization, or even increase it. We find that our nudging based policies outperform other common techniques that do not consider the bounded confidence effect. Finally, we show how to craft prompts for large language models, such as ChatGPT, to generate text-based content for real nudging policies. This illustrates the practical feasibility of our approach, allowing one to go from mathematical nudging policies to real social media content.