Modeling Rumor Spreading and Debunking Strategies on Social Media During Disasters

灾难期间社交媒体上的谣言传播和揭穿策略建模

• 批准号: 1762807
• 负责人: Jun Zhuang
• 金额: $ 39.21万
• 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: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2022-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1762807&HistoricalAwards=false
• 关键词: Modeling; Rumor; Spreading; Debunking; Strategies

Increasingly, social media has been used as a platform for providing timely crisis communication during a disaster. Unfortunately, rumor spreading, and in particular, rumors that are later proven to be untrue, have been identified as critical issues for the use of social media during disasters. For example, messages that mistakenly describe a particular hospital or evacuation route as closed have circulated during recent events. This project will contribute to the NSF's Big Idea "Harnessing Data for 21st Century Science and Engineering" by conducting fundamental research in data science and engineering in the field of social media and disaster management. In particular, this project will build novel models of how rumors currently spread on social media during disasters to improve rumor control and debunking during future disasters. This scientific research contribution thus supports NSF's mission to promote the progress of science and to advance our national welfare. In this case, the benefits will be insights to improve crisis information distribution and rumor management practices, which will save lives, economic losses, and reduce panic, anger and confusion during disasters. This project will support multiple PhD dissertations and MS theses, and involve undergraduate students from under-represented backgrounds.The research objective of this project is to model and optimize the decision-making processes of misinformed social media users during disasters, to study effective rumor detection strategies, and to design and analyze rumor spreading and debunking models for social media users during disasters. To achieve the research objectives, the researchers will use social network analysis, content analysis, decision analysis, game theory, optimization, and simulation, to: (1) collect and analyze social media data to identify potential rumor responding behaviors during disasters and the impact of their responses on rumor spreading; (2) detect rumors from other information by developing a rumor detection algorithm based on rumor text, comments, and spreading patterns; (3) design and validate a decision making model of individual social media users; and (4) study a new rumor spreading model to study the interaction of spreading and debunking processes of social media information.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.

社会媒体越来越多地被用作在灾害期间提供及时的危机沟通的平台。不幸的是，谣言传播，特别是后来被证明是不真实的谣言，已被确定为灾害期间使用社交媒体的关键问题。例如，在最近的事件中，错误地将特定医院或疏散路线描述为关闭的消息已经传播。该项目将通过在社交媒体和灾害管理领域进行数据科学和工程的基础研究，为NSF的“利用数据促进21世纪世纪科学和工程”的大构想做出贡献。特别是，该项目将建立新的模型，以了解灾难期间谣言如何在社交媒体上传播，以改善未来灾难期间的谣言控制和揭穿。这一科学研究的贡献，从而支持NSF的使命，以促进科学的进步和提高我们的国家福利。在这种情况下，其好处将是改善危机信息发布和谣言管理实践的见解，这将挽救生命，经济损失，并减少灾难期间的恐慌，愤怒和混乱。该项目将资助多篇博士论文和硕士论文，并涉及来自代表性不足背景的本科生。该项目的研究目标是建模和优化灾难期间被误导的社交媒体用户的决策过程，研究有效的谣言检测策略，并设计和分析灾难期间社交媒体用户的谣言传播和揭穿模型。为了实现研究目标，研究人员将使用社会网络分析，内容分析，决策分析，博弈论，优化和模拟，以：（1）收集和分析社会媒体数据，以识别灾难期间潜在的谣言反应行为及其对谣言传播的影响;（2）通过开发基于谣言文本、评论和传播模式的谣言检测算法来从其他信息中检测谣言;（3）设计和验证个人社交媒体用户的决策模型;以及（4）研究一种新的谣言传播模型，以研究社交媒体信息的传播和揭穿过程的相互作用。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Misinformation debunking and cross-platform information sharing through Twitter during Hurricanes Harvey and Irma: a case study on shelters and ID checks

• DOI: 10.1007/s11069-020-04016-6
• 发表时间: 2020-05-27
• 期刊: NATURAL HAZARDS
• 影响因子: 3.7
• 作者: Hunt, Kyle;Wang, Bairong;Zhuang, Jun
• 通讯作者: Zhuang, Jun

Monitoring Misinformation on Twitter During Crisis Events: A Machine Learning Approach

• DOI: 10.1111/risa.13634
• 发表时间: 2020-11-14
• 期刊: RISK ANALYSIS
• 影响因子: 3.8
• 作者: Hunt, Kyle;Agarwal, Puneet;Zhuang, Jun
• 通讯作者: Zhuang, Jun

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