优化影响力活动：有限信任下的助推
Optimizing Influence Campaigns: Nudging under Bounded Confidence
AI总结：
本研究针对有限信任现象下影响力活动效果受限的问题，基于控制论构建社交网络多智能体助推策略与目标选择方法，经真实Twitter网络仿真验证效果优于现有技术，并实现了从数学策略到社交媒体内容的落地。
摘要：
在线社交网络中的影响力活动通常由组织、政党和民族国家开展，旨在影响大量受众。这类活动通过网络中分享说服性内容的智能体来实施。然而，如果受众始终不为所动，活动的影响可能微乎其微，这往往源于有限信任（bounded confidence）现象——即只有窄范围的观点能对他们产生影响。本研究表明，要在有限信任条件下实现说服，智能体必须助推目标受众逐步转变观点。我们采用控制论方法，阐释了如何在有限信任观点动力学模型下构建智能体的助推策略，以及如何在社交网络的影响力活动中为多个智能体选择目标。在真实Twitter网络上的仿真实验显示，多智能体助推策略能够改变平均观点、降低观点极化程度，甚至提升极化程度。研究发现，我们的基于助推的策略优于其他未考虑有限信任效应的常用技术。最后，我们展示了如何为ChatGPT等大语言模型设计提示词，以生成真实助推策略所需的文本内容。这证明了我们方法的实际可行性，实现了从数学层面的助推策略到真实社交媒体内容的转化。
英文摘要：
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.
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