This article presents the results of a comprehensive study examining the influence of bots on the dissemination of COVID-19 misinformation and negative vaccine stance on Twitter over a period of three years. The research employed a tripartite methodology: text classification, topic modeling, and network analysis to explore this phenomenon. Text classification, leveraging the Turku University FinBERT pre-trained embeddings model, differentiated between misinformation and vaccine stance detection. Bot-like Twitter accounts were identified using the Botometer software, and further analysis was implemented to distinguish COVID-19 specific bot accounts from regular bots. Network analysis illuminated the communication patterns of COVID-19 bots within retweet and mention networks. The findings reveal that these bots exhibit distinct characteristics and tactics that enable them to influence public discourse, particularly showing an increased activity in COVID-19-related conversations. Topic modeling analysis uncovers that COVID-19 bots predominantly focused on themes such as safety, political/conspiracy theories, and personal choice. The study highlights the critical need to develop effective strategies for detecting and countering bot influence. Essential actions include using clear and concise language in health communications, establishing strategic partnerships during crises, and ensuring the authenticity of user accounts on digital platforms. The findings underscore the pivotal role of bots in propagating misinformation related to COVID-19 and vaccines, highlighting the necessity of identifying and mitigating bot activities for effective intervention.

中文翻译：

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揭开隐藏的威胁：机器人对 COVID-19 健康传播的影响


本文介绍了一项综合研究的结果，该研究考察了三年来机器人对 Twitter 上传播 COVID-19 错误信息和负面疫苗立场的影响。该研究采用了三重方法：文本分类、主题建模和网络分析来探索这一现象。文本分类利用图尔库大学 FinBERT 预训练嵌入模型，区分错误信息和疫苗立场检测。使用 Botometer 软件识别了类似机器人的 Twitter 帐户，并进行了进一步分析，以区分 COVID-19 特定机器人帐户和常规机器人。网络分析阐明了转发和提及网络中 COVID-19 机器人的通信模式。调查结果显示，这些机器人表现出独特的特征和策略，使它们能够影响公众话语，特别是在与 COVID-19 相关的对话中表现出活动增加。主题建模分析发现，COVID-19 机器人主要关注安全、政治/阴谋论和个人选择等主题。该研究强调了制定有效策略来检测和对抗机器人影响的迫切需要。基本行动包括在健康沟通中使用清晰简洁的语言、在危机期间建立战略伙伴关系以及确保数字平台上用户帐户的真实性。研究结果强调了机器人在传播与 COVID-19 和疫苗相关的错误信息方面的关键作用，并强调了识别和减少机器人活动以进行有效干预的必要性。