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

RAPID：通过情境感知视觉信息治疗来反击 COVID-19 错误信息

• 批准号: 2027713
• 负责人: Yu-Ru Lin
• 金额: $ 10.45万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2022-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2027713&HistoricalAwards=false
• 关键词: RAPID; Countering; COVID; 19; Misinformation

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.

随着COVID-19疫情蔓延，地球仪各国及城市已采取严格措施，包括隔离及区域封锁。越来越多的孤立，沿着而来的恐慌和焦虑，给抵制错误信息带来了挑战--人们越来越多地利用他们已经熟悉的在线信息来源，而获得替代故事的机会越来越少。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，以便在持续的COVID-19危机期间及以后及时用于对抗错误信息。 该方法的一个新特征是通过众包真实图像来应对错误信息的具体实例。 这项研究将有助于科学地理解错误信息和有说服力的叙事结构，评估错误信息传播的风险，并建立对抗错误信息的机制。该项目的技术目标分为三个方面。第一个重点将调查多模态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，带有嵌入式口号的图像）将获得更强的响应，从而增加帖子被共享的可能性。第二个重点是创建指标，根据从用户接触的内容中学习到的预测因素来评估错误信息传播的可能性。第三个重点将侧重于开发一个系统，根据公民记者对实地调查的投入和机器学习技术，打击错误信息。最后，该系统将通过调查研究和访谈进行评估，以检查该系统的可用性，有用性和有效性，在减少传播和影响的错误信息。这个奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响审查标准的支持。

项目成果

Argumentatively Phony? Detecting Misinformation via Argument Mining

争论虚伪？

• 发表时间: 2021
• 期刊: 1st KDD Workshop on AI-enabled Cybersecurity Analytics
• 作者: Yan, Muheng;Lin, Yu-Ru;Litman, Diane
• 通讯作者: Litman, Diane

Whose Advantage? Measuring Attention Dynamics across YouTube and Twitter on Controversial Topics

• DOI: 10.48550/arxiv.2204.00988
• 发表时间: 2022-04
• 作者: Jooyoung Lee;Siqi Wu;A. Ertugrul;Yu-Ru Lin;Lexing Xie

Visual Persuasion in COVID-19 Social Media Content: A Multi-Modal Characterization

• DOI: 10.1145/3487553.3524647
• 发表时间: 2021-12
• 期刊: Companion Proceedings of the Web Conference 2022
• 作者: Mesut Erhan Unal;Adriana Kovashka;Wen-Ting Chung;Yu-Ru Lin

Characterizing User Susceptibility to COVID-19 Misinformation on Twitter

• DOI: 10.1609/icwsm.v16i1.19353
• 发表时间: 2021-09
• 期刊: ArXiv
• 作者: Xian Teng;Yu-Ru Lin;Wen-Ting Chung;Ang Li;Adriana Kovashka

Are Mutated Misinformation More Contagious? A Case Study of COVID-19 Misinformation on Twitter

变异的错误信息是否更具传染性？

• DOI: 10.1145/3501247.3531562
• 发表时间: 2022
• 期刊: Proceedings of Web Science 2022 (WebSci 2022
• 作者: Yan, Muheng;Chung, Wen-Ting;Lin, Yu-Ru
• 通讯作者: Lin, Yu-Ru