Characterization of Misinformation Dynamics in COVID-19 related health information in online social media

在线社交媒体中与 COVID-19 相关的健康信息中错误信息动态的表征

• 批准号: 10176817
• 负责人: SAHITI MYNENI
• 金额: $ 6.31万
• 依托单位: UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10176817
• 关键词: 2019-nCoV; Address; Administrative Supplement; Affect; Age; Area; Awareness; Belief; COVID-19; Cognitive; Communication; Communications Media; Communities; Comprehension; Computational Linguistics; Computer Models; Data Set; Decision Making; Detection; Development; Discourse analysis; Disease; Disease Outbreaks; Educational Intervention; Effectiveness; Emotional; Engineering; Ensure; Financial compensation; Foundations; Health; Healthcare; Human; Individual; Information Dissemination; Infrastructure; Intention; Knowledge; Language; Linguistics; Mediation; Methodology; Methods; Misinformation; Modeling; Pathway interactions; Perception; Persons; Policies; Policy Developments; Population Surveillance; Process; Public Health; Regulation; Research; Risk; Risk Factors; Role; Semantics; Social Distance; Social Environment; Social Processes; Social isolation; Source; Structure; Techniques; Text; Time; Twitter; behavior change; computer infrastructure; contagion; deep learning; digital; health assessment; health belief; network models; pandemic disease; peer; peer support; public education; scale up; social; social inclusion; social media; social situation

Abstract: Social media has become predominant as a source of information for many health care consumers. However false and misleading information are a pervasive problem in this context. Specifically, during CVID-19 pandemic, misinformation has been a significant public health challenge, impeding the effectiveness of public health awareness campaigns and resulting in suboptimal responsiveness to the communication of legitimate risk- related information. In the proposed research, we will apply our “Pragmatics to Reveal Intent in Social Media (PRISM) framework to facilitate automated detection of intent and belief attributes underlying COVID-19 related misinformation. The PRISM framework aims to incorporate and integrate communication intent, semantics and structure of online communication to study social processes and cognitive factors underlying misinformation comprehension. Such analysis forms the foundational step towards characterization of misinformation seeding and perception in digital social settings, ultimately allowing us to develop scalable and reliable computational infrastructure that can help formulate resilient and effective dissemination approaches to negotiate misinformation spread, easing public health burden and informing policy regulations as needed.

摘要： 社交媒体已成为许多医疗保健消费者的主要信息来源。然而 虚假和误导性信息是这方面的一个普遍问题。具体而言，在CVID-19大流行期间， 错误信息一直是一个重大的公共卫生挑战，阻碍了公共卫生的有效性 提高认识运动，导致对合理风险的沟通反应不佳- 相关信息。在本研究中，我们将运用我们的“社交媒体意图揭示语用学 （PRISM）框架，以促进自动检测COVID-19相关的意图和信念属性 错误信息PRISM框架旨在将通信意图、语义和 在线交流的结构，以研究错误信息背后的社会过程和认知因素 理解力这种分析形成了对错误信息播种特征化的基础步骤 和感知，最终使我们能够开发可扩展和可靠的计算能力， 基础设施，可以帮助制定有弹性和有效的传播方法， 传播错误信息，减轻公共卫生负担，并根据需要为政策法规提供信息。