Social bots, employed to manipulate public opinion, pose a novel threat to digital societies. Existing bot research has emphasized technological aspects while neglecting psychological factors shaping human–bot interactions. This research addresses this gap within the context of the US‐American electorate. Two datasets provide evidence that partisanship distorts (a) online users' representation of bots, (b) their ability to identify them, and (c) their intentions to interact with them. Study 1 explores global bot perceptions on through survey data from N = 452 Twitter (now X) users. Results suggest that users tend to attribute bot‐related dangers to political adversaries, rather than recognizing bots as a shared threat to political discourse. Study 2 (N = 619) evaluates the consequences of such misrepresentations for the quality of online interactions. In an online experiment, participants were asked to differentiate between human and bot profiles. Results indicate that partisan leanings explained systematic judgement errors. The same data suggest that participants aim to avoid interacting with bots. However, biased judgements may undermine this motivation in praxis. In sum, the presented findings underscore the importance of interdisciplinary strategies that consider technological and human factors to address the threats posed by bots in a rapidly evolving digital landscape.

中文翻译：

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不是我们这种人群！党派偏见如何扭曲 Twitter 上政治机器人的看法（现在是 X）


用于操纵公众舆论的社交机器人对数字社会构成了新的威胁。现有的机器人研究强调技术方面，而忽略了影响人机交互的心理因素。这项研究在美国选民的背景下解决了这一差距。两个数据集提供的证据表明，党派之争扭曲了（a）在线用户对机器人的描述，（b）他们识别机器人的能力，以及（c）他们与机器人互动的意图。研究 1 通过来自 N = 452 位 Twitter（现在是 X）用户的调查数据探讨了全球机器人的看法。结果表明，用户倾向于将与机器人相关的危险归因于政治对手，而不是将机器人视为对政治话语的共同威胁。研究 2（N = 619）评估了此类虚假陈述对在线互动质量的影响。在一项在线实验中，参与者被要求区分人类和机器人的配置文件。结果表明，党派倾向可以解释系统性判断错误。同样的数据表明，参与者的目标是避免与机器人互动。然而，在实践中，有偏见的判断可能会破坏这种动机。总之，所提出的研究结果强调了考虑技术和人为因素的跨学科策略的重要性，以解决快速发展的数字环境中机器人带来的威胁。