The pervasive presence and influence of political bots have become the subject of extensive research in recent years. Studies have revealed that a significant percentage of active accounts are bots, contributing to the polarization of public sentiment online. This study employs an agent-based model in conducting computer simulations of complex social networks, to elucidate how bots, representing diverse ideological perspectives, exacerbate societal divisions. To investigate the dynamics of opinion diffusion and shed light on the phenomenon of polarization caused by the activities of political bots, we introduced bots into a bounded-confidence opinion dynamic model for different social networks, whereby the effects of bots on other agents were studied to provide a comprehensive understanding of their influence on opinion dynamics. The simulations showed that the symmetrical deployment of bots on both sides of the opinion spectrum intensifies polarization. These effects were observed within specific tolerance and homophily ranges, with low and high user tolerances slowing down polarization. Moreover, the average path length of the network and the centrality of the bots had a significant impact on the result. Finally, polarization tends to be lower when humans exhibit reduced confidence in bots. This research not only offers valuable insights into the implications of bot activities on the polarization of public opinion and current state of digital society but also provides suggestions to mitigate bot-driven polarization.

中文翻译：

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不和谐的代理人：模拟政治机器人对社交网络中意见极化的影响


近年来，政治机器人的普遍存在和影响已成为广泛研究的主题。研究表明，很大一部分活跃账户是机器人，导致网上公众情绪两极分化。这项研究采用基于代理的模型对复杂的社交网络进行计算机模拟，以阐明代表不同意识形态观点的机器人如何加剧社会分歧。为了研究意见传播的动态并揭示政治机器人活动引起的两极分化现象，我们将机器人引入不同社交网络的有限置信意见动态模型中，从而研究机器人对其他代理的影响全面了解它们对舆论动态的影响。模拟表明，意见谱两边对称部署机器人会加剧两极分化。这些影响是在特定的容差和同质性范围内观察到的，低和高的用户容差会减缓极化。此外，网络的平均路径长度和机器人的中心性对结果有显着影响。最后，当人类对机器人的信心降低时，两极分化往往会降低。这项研究不仅为机器人活动对公众舆论两极分化和数字社会现状的影响提供了宝贵的见解，而且还为缓解机器人驱动的两极分化提供了建议。