RAPID: TRACKING AND NETWORK ANALYSIS OF THE SPREAD OF MISINFORMATION REGARDING COVID-19

这个项目考察了有关新冠肺炎的错误信息是如何通过社交媒体和新闻媒体传播的。有关新冠肺炎的错误信息的传播是向公众传播准确及时的科学信息的更大利益的一部分。该项目汇集了专门针对新冠肺炎的英文新闻馈送以及脸书、红迪、推特和其他与新冠肺炎相关的社交媒体帖子的数据，以评估科学错误信息通过社交媒体和互联网的传播情况，目的是帮助官员和科学顾问在疫情蔓延时限制错误信息的传播。最终目标是使用机器学习和网络分析工具提供对错误信息起源位置的洞察和对错误信息传播机制的理解。我们的项目使用克莱恩中心现有的基础设施和伊利诺伊大学厄巴纳-香槟分校的社交媒体宏观来跟踪可疑和潜在有害信息的传播，包括新冠肺炎的来源、传播和治疗。研究人员将利用美国疾病控制与预防中心的主流见解来评估目前对新冠肺炎的了解，然后开发一种机器学习算法，以检测网络和社交媒体上将被标记为潜在可疑信息的信息。从这些帖子中提取的索赔、人员、地点和事物将用于跟踪可疑新冠肺炎信息的位置、人员和组织附属关系。由此产生的数据将受到网络分析技术的影响，这些技术寻找错误信息传播的结构性漏洞和薄弱联系，目的是为科学家和政策制定者提供潜在的信息缓解策略。该项目提供的文本和网络模型将立即通过伊利诺伊大学新冠肺炎网站提供，通过新闻稿和向决策者进行外联传播，并将纳入将以开放源码格式出版的科学出版物。我们的分析和识别公共通信网络和社交媒体网站中的热点和断层线的能力的长期影响将有助于抗击未来的流行病以及其他对国家健康至关重要的话题。这一奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project examines the ways that misinformation regarding COVID-19 disseminates through social media and news outlets. The spread of misinformation regarding COVID-19 is part of a larger interest in the communication of accurate and timely scientific information to the public. This project assembles data on English-language news feeds devoted to COVID-19 as well as Facebook, Reddit, twitter, and other social media posts related to COVID-19 to assess the spread of scientific misinformation through social media and the internet with the goal of aiding officials and science advisors in limiting the spread of misinformation as the pandemic unfolds. The ultimate goal is to use machine learning and network analysis tools to provide insight into the locations where misinformation originates and understanding of the mechanisms through which this misinformation spreads.Our project uses the pre-existing infrastructure of the Cline Center and the social media macroscope at the University of Illinois Urbana-Champaign to track the spread of dubious and potentially harmful information on the origins, spread, and treatments for COVID-19. The researchers will use prevailing insights from the Centers for Disease Control to assess what is known currently about COVID-19 and then develop a machine-learning algorithm to detect information on the web and on social media that will be labelled as potentially dubious. The extraction of claims, people, places, and things from these posts will be used to track locations, people, and organizational affiliations of dubious COVID-19 information. The resulting data will be subjected to network analysis techniques that search for structural holes and weak ties through which misinformation spreads with the goal of providing scientists and policy makers with potential information mitigation strategies. The textual and network modelling this project provides will be immediately available through a University of Illinois COVID-19 website, disseminated through press releases and outreach to policy-making community, and integrated into scientific publications that will be published in open-source formats. The long-term implications of our analysis and the ability to identify hot spots and fault-lines in public communication networks and social media sites will be useful for fighting future pandemics as well as other topics that are vital to the health of the Nation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.