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，并开发算法来自动检测错误信息。 在自然灾害和其他危害事件期间，人们越来越多地利用社交媒体来传播、搜索和管理与事件有关的信息。 目击者对事件影响的描述现在可以在几秒钟内被现场的人分享。 受影响社区和应急人员很有可能利用这些信息来提高对情况的认识，改进决策，促进应对活动，并有可能拯救生命。 然而，仍然存在若干挑战;其中之一是错误信息的产生和传播。 事实上，在最近的灾难事件中，包括飓风桑迪和波士顿马拉松爆炸案，通过社交媒体传播错误信息被认为是一个重大问题;有证据表明，它在社交媒体网站内部和之间传播，并进入更广泛的信息空间。该项目采用了一种新颖的变革性方法，旨在利用群体的集体智慧-一些社交媒体用户的群体活动，他们质疑和纠正有问题的信息-来区分错误信息，并帮助发现错误信息。 它将描述危机事件期间在线错误信息流的动态，并开发一种机器学习策略，通过利用人群的集体智慧来自动识别错误信息。 该项目的重点是识别社交媒体用户在传播和挑战或纠正错误信息方面的独特行为模式。 它采用了定性和定量方法，包括人工和基于机器的内容分析，以全面了解错误信息的传播情况。 主要的研究网站是Twitter，因为它是公开的，便于迅速传播信息，并在灾害事件中作为一种高度使用的媒体而获得曝光。 这项调查超越了Twitter，通过追踪URL链接到其原始来源来研究其他社交媒体和周围互联网上的信息流。这项研究提供了经验，理论和应用的贡献，人机交互领域的社会计算，危机信息学和众包。 从经验上讲，它增强了我们对在线空间中错误信息流动的理解。 它建立在以前的研究，包括通过检查几种类型的行为行动，包括纠正，猜测和挑战错误信息，包括一个更微妙的错误信息的看法。 此外，该项目还绘制了特定社交媒体网络内和不同平台之间的信息传播图（使用URL分析），以确定信息传播模式或签名，可用于检测和分类不同类型的错误信息。从理论上讲，它有助于人们对在线社交网络中的众包、众包和集体智慧的理解，特别是希望了解和描述连接的人群如何作为一个大规模的传感器网络在危机事件中检测错误信息。 最后，它旨在利用这些经验和理论贡献来开发实时检测社交媒体上错误信息的解决方案。