RAPID: Identification of Key Dynamics for Rumor Spread and Control during Hurricanes Harvey and Irma

RAPID：飓风哈维和艾尔玛期间谣言传播和控制的关键动态的识别

• 批准号: 1760586
• 负责人: Jun Zhuang
• 金额: $ 17.57万
• 依托单位: SUNY at Buffalo
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-10-01 至 2018-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1760586&HistoricalAwards=false
• 关键词: RAPID; Identification; Key; Dynamics; Rumor

Forms of social media like Twitter are increasingly relied upon by members of the public as sources of information in emergencies like hurricanes and floods. The downside of these sources of information is that they may also be the source of unfounded rumors as were spread regarding a host of issues during hurricanes Harvey and Irma. The instant sharing features of social media make rumors difficult to control because information from official sources to debunk misinformation often comes late. Motivated by those issue, this project will collect and analyze data related to how rumors were spread and controlled during Hurricane Harvey and Hurricane Irma. The objectives of this project are to study (a) how rumor spreads on social media; (b) what effective debunking network constitutes; and (c) how the public deals with risk information that stipulates subsequent communication behaviors such as information processing and sharing.The study will use social network analysis, content analysis, survey, interviews, optimization and simulation, to (a) identify rumor response behaviors of social media users during disasters; (b) identify motivations behind social media users' risk communication behaviors such as information processing and information sharing; (c) build a decision making model of individual social media users to predict future responses to rumors; (d) investigate social media usage in rumor control and potential collaborations among disaster response agencies in rumor management; and (e) design potential collaboration methods among disaster response agencies on social media to minimize the costs and damages generated by rumors.

公众越来越依赖Twitter等社交媒体形式，作为飓风和洪水等紧急情况的信息来源。这些信息来源的缺点是，它们也可能是毫无根据的谣言的来源，因为在飓风哈维和厄玛期间传播了一系列问题。 社交媒体的即时分享功能使谣言难以控制，因为来自官方来源的揭穿错误信息的信息往往来得很晚。受这些问题的启发，该项目将收集和分析有关飓风哈维和飓风厄玛期间谣言如何传播和控制的数据。这个项目的目标是研究（a）谣言如何在社交媒体上传播;（B）什么是有效的揭穿网络;及（c）公众如何处理规定其后的信息处理及分享等沟通行为的风险信息。研究将采用社会网络分析、内容分析、调查、访谈、优化及模拟等方法，（a）识别社交媒体用户在灾难期间的谣言反应行为;（B）识别社交媒体用户风险沟通行为背后的动机，如信息处理和信息共享;（c）建立个人社交媒体用户的决策模型，以预测未来对谣言的反应;（d）调查社交媒体在谣言控制方面的使用情况，以及灾害应对机构在谣言管理方面的潜在合作;以及（e）设计灾害应对机构在社交媒体上的潜在合作方法，以尽量减少谣言造成的成本和损害。

项目成果

Rumor response, debunking response, and decision makings of misinformed Twitter users during disasters

• DOI: 10.1007/s11069-018-3344-6
• 发表时间: 2018-09-01
• 期刊: NATURAL HAZARDS
• 影响因子: 3.7
• 作者: Wang, Bairong;Zhuang, Jun
• 通讯作者: Zhuang, Jun

A Multi-Algorithm Approach for Classifying Misinformed Twitter Data during Crisis Events

危机事件期间对虚假 Twitter 数据进行分类的多算法方法

• 发表时间: 2019
• 期刊: Proceedings of the 2019 IISE Annual Conference
• 作者: Hunt, Kyle;Agarwal, Puneet;Zhuang, Jun
• 通讯作者: Zhuang, Jun