Lessons Learned From Interdisciplinary Efforts to Combat COVID-19 Misinformation: Development of Agile Integrative Methods From Behavioral Science, Data Science, and Implementation Science.
Lessons Learned From Interdisciplinary Efforts to Combat COVID-19 Misinformation: Development of Agile Integrative Methods From Behavioral Science, Data Science, and Implementation Science.
复制标题
DOI：
10.2196/40156
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Fernandez, Maria E.
中科院分区：
文献类型：
--
作者：
Myneni, Sahiti;Cuccaro, Paula;Montgomery, Sarah;Pakanati, Vivek;Tang, Jinni;Singh, Tavleen;Dominguez, Olivia;Cohen, Trevor;Reininger, Belinda;Savas, Lara S.;Fernandez, Maria E.
尽管人们对社交媒体虚假信息的认识不断提高，并在这方面取得了进展，但新冠肺炎虚假信息的自由流动仍在继续，影响了个人的预防行为，包括蒙面、检测和接种疫苗。在本文中，我们描述了我们的多学科努力，并特别关注以下方法：(1)收集社区需求，(2)开发干预措施，以及(3)进行大规模、敏捷和快速的社区评估，以检查和打击新冠肺炎错误信息。我们使用干预地图框架来执行社区需求评估，并开发理论知情的干预措施。为了通过大规模在线社交倾听来补充这些快速和响应的努力，我们开发了一个新的方法框架，包括定性查询、计算方法和定量网络模型，以分析公开可用的社交媒体数据集，以建模特定于内容的错误信息动态并指导内容定制工作。作为社区需求评估的一部分，我们与社区科学家进行了11次半结构化访谈、4次倾听会议和3个焦点小组。此外，我们使用我们的数据存储库和416,927条新冠肺炎社交媒体帖子通过数字渠道收集信息传播模式。我们的社区需求评估结果揭示了错误信息对个人行为和参与的个人、文化和社会影响的复杂交织。我们的社交媒体干预导致社区参与有限，并表明有必要为消费者辩护和招募有影响力的人。使用我们的计算模型，通过语义和句法特征，将潜在健康行为的理论构建与新冠肺炎相关的社交媒体互动联系起来，揭示了事实微博帖子和误导性微博帖子中频繁出现的互动类型，并表明程度等网络指标存在显著差异。我们的深度学习分类器的性能是合理的，言语行为的F度量为0.80，行为构建的F度量为0.81。我们的研究强调了以社区为基础的实地研究的优势，并强调了大规模社交媒体数据集在实现快速干预定制以适应基层社区干预以阻止错误信息在少数族裔社区中播种和传播方面的效用。讨论了社交媒体解决方案在公共卫生中的可持续作用对消费者倡导、数据治理和行业激励的影响。
Despite increasing awareness about and advances in addressing social media misinformation, the free flow of false COVID-19 information has continued, affecting individuals’ preventive behaviors, including masking, testing, and vaccine uptake. In this paper, we describe our multidisciplinary efforts with a specific focus on methods to (1) gather community needs, (2) develop interventions, and (3) conduct large-scale agile and rapid community assessments to examine and combat COVID-19 misinformation. We used the Intervention Mapping framework to perform community needs assessment and develop theory-informed interventions. To supplement these rapid and responsive efforts through large-scale online social listening, we developed a novel methodological framework, comprising qualitative inquiry, computational methods, and quantitative network models to analyze publicly available social media data sets to model content-specific misinformation dynamics and guide content tailoring efforts. As part of community needs assessment, we conducted 11 semistructured interviews, 4 listening sessions, and 3 focus groups with community scientists. Further, we used our data repository with 416,927 COVID-19 social media posts to gather information diffusion patterns through digital channels. Our results from community needs assessment revealed the complex intertwining of personal, cultural, and social influences of misinformation on individual behaviors and engagement. Our social media interventions resulted in limited community engagement and indicated the need for consumer advocacy and influencer recruitment. The linking of theoretical constructs underlying health behaviors to COVID-19–related social media interactions through semantic and syntactic features using our computational models has revealed frequent interaction typologies in factual and misleading COVID-19 posts and indicated significant differences in network metrics such as degree. The performance of our deep learning classifiers was reasonable, with an F-measure of 0.80 for speech acts and 0.81 for behavior constructs. Our study highlights the strengths of community-based field studies and emphasizes the utility of large-scale social media data sets in enabling rapid intervention tailoring to adapt grassroots community interventions to thwart misinformation seeding and spread among minority communities. Implications for consumer advocacy, data governance, and industry incentives are discussed for the sustainable role of social media solutions in public health.
登录
查看更多内容
影响因子：
4.6
作者：
Cinelli M;Quattrociocchi W;Galeazzi A;Valensise CM;Brugnoli E;Schmidt AL;Zola P;Zollo F;Scala A
通讯作者：
Scala A
影响因子：
19.6
作者：
Hébert-Dufresne L;Scarpino SV;Young JG
通讯作者：
Young JG
影响因子：
3.2
作者：
Hsieh, HF;Shannon, SE
通讯作者：
Shannon, SE
影响因子：
4.4
作者：
Chou, Wen-Ying Sylvia;Gaysynsky, Anna;Vanderpool, Robin C.
通讯作者：
Vanderpool, Robin C.
DOI：
10.1016/0749-5978(91)90020-t
发表时间：
1991-12-01
影响因子：
4.6
作者：
AJZEN, I
通讯作者：
AJZEN, I