RAPID: Countering COVID-19 Misinformation via Situation-Aware Visually Informed Treatment

随着新冠肺炎疫情的蔓延，全球各国和城市都采取了严格的措施，包括隔离和区域封锁。与日俱增的孤立，加上恐慌和焦虑，给打击虚假信息带来了挑战--人们越来越多地利用他们已经熟悉的在线信息来源，而获取另类故事的机会正在下降。该项目将开发基于文本和图像分析、社会心理学和众包的机制，这些机制可以及时用于打击正在进行的新冠肺炎危机期间及以后的错误信息。这种方法的一个新特点是通过众包真实图像来处理错误信息的特定实例，以对抗这种错误信息。这项研究将有助于对错误信息的科学理解和有说服力的叙事结构，有助于评估错误信息传播的风险，并有助于制定反错误信息的机制。该项目的技术目标分为三个方面。第一个推力将调查多模式社交媒体帖子的哪些信息内容和哪些特定部分(例如，一段文本、带有图像的文本、带有嵌入标语的图像)会收到更强烈的响应，从而增加帖子被分享的可能性。第二个推力将创建指标，根据从用户接触的内容中学习的预测值来评估错误信息传播的可能性。第三个重点将是根据公民记者对实地调查的投入和机器学习技术开发一个打击错误信息的系统。最后，该系统将通过调查研究和访谈进行评估，以检查该系统在减少错误信息的传播和影响方面的可用性、有用性和有效性。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

As the COVID-19 pandemic spreads, countries and cities around the globe have taken stringent measures including quarantine and regional lockdown. The increasing isolation, along with the panic and anxiety, creates challenges for countering misinformation--people are increasingly tapping into online information sources already familiar to them with declining chances of accessing alternative stories. This project will develop mechanisms based on text and image analysis, social psychology, and crowd-sourcing that can be used in a timely manner to counter misinformation during the ongoing COVID-19 crisis and beyond. One of the novel features of the approach is to deal with a specific instance of misinformation by crowd-sourcing authentic images that counter this misinformation. This research will contribute to the scientific understanding of misinformation and of persuasive narrative construction, to the assessment of risk for the spread of misinformation, and to the development of mechanisms to counter misinformation. The technical aims of this project are divided into three thrusts. The first thrust will investigate what information content and which specific part of a multimodal social media post (e.g, a piece of text, text with an image, image with an embedded slogan) will receive stronger responses and hence increase the likelihood of the post being shared. The second thrust will create metrics to assess the likelihood of the spread of misinformation based on predictors learned from the content to which users are exposed. The third thrust will focus on the development of a system to counter misinformation based on citizen journalists’ inputs of field investigations and on machine learning techniques. Finally, the system will be evaluated by survey studies and interviews to examine the system’s usability, usefulness, and effectiveness in reducing the spreading and impact of misinformation.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.