Conspiracy theories can threaten society by spreading misinformation, deepening polarization, and eroding trust in democratic institutions. Social media often fuels the spread of conspiracies, primarily driven by two key actors: Superspreaders -- influential individuals disseminating conspiracy content at disproportionately high rates, and Bots -- automated accounts designed to amplify conspiracies strategically. To counter the spread of conspiracy theories, it is critical to both identify these actors and to better understand their behavior. However, a systematic analysis of these actors as well as real-world-applicable identification methods are still lacking. In this study, we leverage over seven million tweets from the COVID-19 pandemic to analyze key differences between Human Superspreaders and Bots across dimensions such as linguistic complexity, toxicity, and hashtag usage. Our analysis reveals distinct communication strategies: Superspreaders tend to use more complex language and substantive content while relying less on structural elements like hashtags and emojis, likely to enhance credibility and authority. By contrast, Bots favor simpler language and strategic cross-usage of hashtags, likely to increase accessibility, facilitate infiltration into trending discussions, and amplify reach. To counter both Human Superspreaders and Bots, we propose and evaluate 27 novel metrics for quantifying the severity of conspiracy theory spread. Our findings highlight the effectiveness of an adapted H-Index for computationally feasible identification of Human Superspreaders. By identifying behavioral patterns unique to Human Superspreaders and Bots as well as providing suitable identification methods, this study provides a foundation for mitigation strategies, including platform moderation policies, temporary and permanent account suspensions, and public awareness campaigns.

翻译：阴谋论通过传播错误信息、加剧社会两极分化和削弱对民主制度的信任，对社会构成威胁。社交媒体常常助长阴谋论的传播，主要由两类关键行为者驱动：超级传播者——以异常高的频率传播阴谋内容的有影响力个体，以及机器人——旨在策略性放大阴谋论的自动化账户。为遏制阴谋论的扩散，识别这些行为者并深入理解其行为模式至关重要。然而，目前仍缺乏对这些行为者的系统性分析以及适用于现实场景的识别方法。本研究利用新冠疫情时期的超过七百万条推文，从语言复杂性、有害性及话题标签使用等多个维度，分析了人类超级传播者与机器人之间的关键差异。我们的分析揭示了二者截然不同的传播策略：超级传播者倾向于使用更复杂的语言和实质性内容，同时较少依赖话题标签和表情符号等结构元素，这可能是为了增强可信度和权威性。相比之下，机器人偏好更简单的语言和策略性交叉使用话题标签，这可能是为了提高可访问性、便于渗入热门讨论并扩大传播范围。为应对人类超级传播者和机器人，我们提出并评估了27项用于量化阴谋论传播严重性的新指标。我们的研究结果突显了改进版H指数在计算可行的人类超级传播者识别中的有效性。通过识别人类超级传播者与机器人特有的行为模式，并提供适用的识别方法，本研究为制定缓解策略奠定了基础，包括平台内容审核政策、临时性与永久性账户封禁以及公众意识提升活动。