CHS: Small: Detecting Misinformation Flows in Social Media Spaces During Crisis Events

CHS：小：在危机事件期间检测社交媒体空间中的错误信息流

• 批准号: 1420255
• 负责人: Kate Starbird
• 金额: $ 46.76万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-09-15 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1420255&HistoricalAwards=false
• 关键词: CHS; Small; Detecting; Misinformation; Flows

This research seeks both to understand the patterns and mechanisms of the diffusion of misinformation on social media and to develop algorithms to automatically detect misinformation as events unfold. During natural disasters and other hazard events, individuals increasingly utilize social media to disseminate, search for and curate event-related information. Eyewitness accounts of event impacts can now be shared by those on the scene in a matter of seconds. There is great potential for this information to be used by affected communities and emergency responders to enhance situational awareness and improve decision-making, facilitating response activities and potentially saving lives. Yet several challenges remain; one is the generation and propagation of misinformation. Indeed, during recent disaster events, including Hurricane Sandy and the Boston Marathon bombings, the spread of misinformation via social media was noted as a significant problem; evidence suggests it spread both within and across social media sites as well as into the broader information space. Taking a novel and transformative approach, this project aims to utilize the collective intelligence of the crowd - the crowdwork of some social media users who challenge and correct questionable information - to distinguish misinformation and aid in its detection. It will both characterize the dynamics of misinformation flow online during crisis events, and develop a machine learning strategy for automatically identifying misinformation by leveraging the collective intelligence of the crowd. The project focuses on identifying distinctive behavioral patterns of social media users in both spreading and challenging or correcting misinformation. It incorporates qualitative and quantitative methods, including manual and machine-based content analysis, to look comprehensively at the spread of misinformation. The primary research site is Twitter, because it is public, it facilitates rapid information dissemination, and it has gained exposure as a highly used medium during disaster events. This investigation expands beyond Twitter to study information flows across other social media and the surrounding Internet by tracing URL links to their original sources. This research offers empirical, theoretical and applied contributions to the field of human-computer interaction in the areas of social computing, crisis informatics, and crowdsourcing. Empirically, it enhances our understanding of the flow of misinformation in online spaces. It builds on previous studies to include a more nuanced view of misinformation by examining several types of behavioral actions, including correction, speculation and challenges to misinformation. Moreover, the project maps information contagion within a particular social media network and across different platforms (using URL analysis) to identify patterns of information diffusion, or signatures, that can be used to detect and classify different types of misinformation. Theoretically, it contributes to a growing understanding of crowdwork, crowdsourcing, and collective intelligence within online social networks, specifically looking to understand and describe how the connected crowd performs as a massive sensor network that detects misinformation during crisis events. Finally, it aims to leverage these empirical and theoretical contributions to develop solutions for the real-time detection of misinformation on social media.

这项研究旨在了解社交媒体上错误信息传播的模式和机制，并开发算法以自动检测随着事件的发展而自动检测错误信息。 在自然灾害和其他危险事件中，个人越来越多地利用社交媒体传播，搜索和策划与事件相关的信息。 现在，现场在几秒钟内可以分享有关事件影响的目击者记载。 受影响的社区和紧急响应者使用这些信息有很大的潜力，以提高情境意识，改善决策，促进响应活动并潜在地挽救生命。 然而，仍然存在一些挑战。一个是错误信息的产生和传播。 确实，在包括桑迪飓风和波士顿马拉松爆炸在内的最近的灾难事件中，通过社交媒体的错误信息传播被认为是一个重大问题。有证据表明，它在社交媒体网站以及更广泛的信息空间内传播。该项目采用一种新颖而变革的方法，旨在利用人群的集体智慧 - 一些社交媒体用户的人群，这些社交媒体用户挑战和纠正可疑信息 - 以区分错误信息和援助。 它既会表征危机事件期间在线错误信息流动的动态，又要制定机器学习策略，以通过利用人群的集体智能来自动识别错误信息。 该项目着重于在传播和挑战或纠正错误信息中确定社交媒体用户的独特行为模式。 它结合了定性和定量方法，包括手动和基于机器的内容分析，以全面地查看错误信息的传播。 主要的研究网站是Twitter，因为它是公开的，它促进了快速的信息传播，并且在灾难事件中，它已成为一种高度使用的媒介。 这项调查扩大了Twitter的扩展，通过追踪到其原始资源的URL链接来研究其他社交媒体和周围互联网的信息流。这项研究为社会计算，危机信息学和众包的领域提供了对人与计算机互动领域的经验，理论和应用贡献。 从经验上讲，它增强了我们对在线空间中错误信息流的理解。 它以先前的研究为基础，包括通过检查几种类型的行为行动，包括纠正，投机和错误信息的挑战，包括对错误信息的更细微的看法。 此外，项目映射特定社交媒体网络内以及跨不同平台（使用URL分析）的信息共同点，以识别可用于检测和对不同类型的错误信息进行分类的信息扩散或签名模式。从理论上讲，它有助于在线社交网络中对人群，众包和集体智能的越来越多的理解，特别是希望理解和描述连接人群的表现如何作为在危机活动中检测错误信息的大规模传感器网络。 最后，它旨在利用这些经验和理论贡献来开发解决方案，以实时检测社交媒体上的错误信息。