Cinelli Matteo, Gesualdo Francesco
Department of Computer Science, Sapienza University of Rome, Via Regina Elena 295, Rome, 00100, Italy, 39 3397898012.
Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Pisa, Italy.
JMIR Infodemiology. 2025 May 7;5:e57455. doi: 10.2196/57455.
As we move beyond the COVID-19 pandemic, the risk of future infodemics remains significant, driven by emerging health crises and the increasing influence of artificial intelligence in the information ecosystem. During periods of apparent stability, proactive efforts to advance infodemiology are essential for enhancing preparedness and improving public health outcomes. This requires a thorough examination of the foundations of this evolving discipline, particularly in understanding how to accurately identify an infodemic at the appropriate time and scale, and how to distinguish it from other processes of viral information spread, both within and outside the realm of public health. In this paper, we integrate expertise from data science and public health to examine the key differences between information production during an infodemic and viral information spread. We explore both clear and subtle distinctions, including context and contingency (ie, the association of an infodemic and viral information spread with a health crisis); information dynamics in terms of volume, spread, and predictability; the role of misinformation and information voids; societal impact; and mitigation strategies. By analyzing these differences, we highlight challenges and open questions. These include whether an infodemic is solely associated with pandemics or whether it could arise from other health emergencies; if infodemics are limited to health-related issues or if they could emerge from crises initially unrelated to health (like climate events); and whether infodemics are exclusively global phenomena or if they can occur on national or local scales. Finally, we propose directions for future quantitative research to help the scientific community more robustly differentiate between these phenomena and develop tailored management strategies.
随着我们迈过新冠疫情，未来信息疫情的风险依然巨大，这是由新出现的健康危机以及人工智能在信息生态系统中日益增长的影响力所驱动的。在表面稳定的时期，积极推进信息流行病学对于增强防范能力和改善公共卫生成果至关重要。这需要对这一不断发展的学科基础进行全面审视，尤其是要理解如何在适当的时间和规模准确识别信息疫情，以及如何将其与公共卫生领域内外的其他病毒式信息传播过程区分开来。在本文中，我们整合数据科学和公共卫生领域的专业知识，来研究信息疫情期间的信息产生与病毒式信息传播之间的关键差异。我们探讨了清晰和微妙的区别，包括背景和偶然性（即信息疫情和病毒式信息传播与健康危机的关联）；在数量、传播和可预测性方面的信息动态；错误信息和信息空白的作用；社会影响；以及缓解策略。通过分析这些差异，我们突出了挑战和开放性问题。这些问题包括信息疫情是否仅与大流行相关，还是可能由其他健康紧急情况引发；信息疫情是否仅限于与健康相关的问题，还是可能源于最初与健康无关的危机（如气候事件）；以及信息疫情是仅仅是全球现象，还是也可能在国家或地方层面发生。最后，我们提出了未来定量研究的方向，以帮助科学界更有力地区分这些现象，并制定针对性的管理策略。
