使用流行病学模型研究黑人生命物质运动期间错误信息的传播
Using an Epidemiological Model to Study the Spread of Misinformation during the Black Lives Matter Movement
摘要：
Twitter等社交媒体平台的激增加剧了错误信息传播的后果。为了理解和模拟错误信息的传播，在本文中，我们利用SEIZ（易感，暴露，感染，怀疑）流行病学模型来描述Twitter上错误信息传播的基本过程。与其他流行病学模型相比，该模型产生更广泛的结果，因为它包括额外的怀疑论者（Z）区室，其中用户可能暴露于错误信息项但不参与对其的任何反应，以及额外的暴露（E）区室，其中用户可能需要一些时间才能决定传播错误信息项。我们分析了有关2020年3月华盛顿特区骚乱的错误信息，这些信息是由美国各地的不同用户在Twitter上使用#DCblackout标签传播的。我们的分析表明，错误信息可以使用流行病学的概念建模。据我们所知，这项研究是第一次尝试将SEIZ流行病学模型应用于特定错误信息的传播，这是一个不同于谣言的类别，也是在线社交媒体平台上的骗局。应用数学模型可以帮助了解Twitter上错误信息传播的趋势和动态，并最终帮助开发快速识别和控制错误信息的技术。
英文摘要：
The proliferation of social media platforms like Twitter has heightened the consequences of the spread of misinformation. To understand and model the spread of misinformation, in this paper, we leveraged the SEIZ (Susceptible, Exposed, Infected, Skeptics) epidemiological model to describe the underlying process that delineates the spread of misinformation on Twitter. Compared to the other epidemiological models, this model produces broader results because it includes the additional Skeptics (Z) compartment, wherein a user may be exposed to an item of misinformation but not engage in any reaction to it, and the additional Exposed (E) compartment, wherein the user may need some time before deciding to spread a misinformation item. We analyzed misinformation regarding the unrest in Washington, D.C. in the month of March 2020 which was propagated by the use of the #DCblackout hashtag by different users across the U.S. on Twitter. Our analysis shows that misinformation can be modeled using the concept of epidemiology. To the best of our knowledge, this research is the first to attempt to apply the SEIZ epidemiological model to the spread of a specific item of misinformation, which is a category distinct from that of rumor, and a hoax on online social media platforms. Applying a mathematical model can help to understand the trends and dynamics of the spread of misinformation on Twitter and ultimately help to develop techniques to quickly identify and control it.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
