Background: Online toxicity, encompassing behaviors such as harassment, bullying, hate speech, and the dissemination of misinformation, has become a pressing social concern in the digital age. The 2022 Mpox outbreak, initially termed "Monkeypox" but subsequently renamed to mitigate associated stigmas and societal concerns, serves as a poignant backdrop to this issue. Objective: In this research, we undertake a comprehensive analysis of the toxic online discourse surrounding the 2022 Mpox outbreak. Our objective is to dissect its origins, characterize its nature and content, trace its dissemination patterns, and assess its broader societal implications, with the goal of providing insights that can inform strategies to mitigate such toxicity in future crises. Methods: We collected more than 1.6 million unique tweets and analyzed them from five dimensions, including context, extent, content, speaker, and intent. Utilizing BERT-based topic modeling and social network community clustering, we delineated the toxic dynamics on Twitter. Results: We identified five high-level topic categories in the toxic online discourse on Twitter, including disease (46.6%), health policy and healthcare (19.3%), homophobia (23.9%), politics (6.0%), and racism (4.1%). Through the toxicity diffusion networks of mentions, retweets, and the top users, we found that retweets of toxic content were widespread, while influential users rarely engaged with or countered this toxicity through retweets. Conclusions: By tracking topical dynamics, we can track the changing popularity of toxic content online, providing a better understanding of societal challenges. Network dynamics spotlight key social media influencers and their intents, indicating that addressing these central figures in toxic discourse can enhance crisis communication and inform policy-making.

翻译：背景：网络毒性行为，包括骚扰、欺凌、仇恨言论及虚假信息传播等，已成为数字时代亟待解决的社会问题。2022年猴痘疫情（最初称为"猴痘"，后为减轻相关污名化与社会担忧而更名）为此议题提供了典型研究场景。目标：本研究对2022年猴痘疫情相关的网络毒性言论进行全面分析，旨在解析其起源、特征与内容，追踪传播模式，评估社会影响，从而为未来危机中缓解此类毒性提供策略参考。方法：我们收集超过160万条独立推文，从语境、程度、内容、发言者及意图五个维度进行分析。通过基于BERT的主题建模与社交网络社区聚类技术，系统描绘了Twitter平台的毒性动态。结果：在Twitter毒性讨论中识别出五大主题类别：疾病（46.6%）、卫生政策与医疗（19.3%）、恐同言论（23.9%）、政治（6.0%）和种族歧视（4.1%）。通过提及转发毒性传播网络及核心用户分析发现，毒性内容转发行为普遍存在，而具有影响力的用户很少通过转发参与或抵制这些毒性内容。结论：通过追踪主题动态可监测网络毒性内容的流行度变化，从而深化对社会挑战的理解。网络动态分析能揭示关键社交媒体影响者及其意图，表明针对毒性言论核心节点的干预可优化危机沟通机制并为政策制定提供依据。