SoCS: Collaborative Research: Social Media Enhanced Organizational Sensemaking in Emergency Response

SoCS：协作研究：社交媒体增强应急响应中的组织意识

• 批准号: 1111118
• 负责人: Srinivasan Parthasarathy
• 金额: $ 27万
• 依托单位: Ohio State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1111118&HistoricalAwards=false
• 关键词: SoCS; Collaborative; Research; Social; Media

This collaborative research leverages expertise of researchers at Wright State University (IIS-1111182) and Ohio State University (IIS-1111118). Online social networks and always-connected mobile devices have created an immense opportunity that empowers citizens and organizations to communicate and coordinate effectively in the wake of critical events. Specifically, there have been many isolated examples of using Twitter to provide timely and situational information about emergencies to relief organizations, and to conduct ad-hoc coordination. However, there are few attempts that try to understand the full ramifications of using social networks in a more concerted manner for effective organizational sensemaking. This project aims to conduct multidisciplinary research involving computer and social scientists fill this gap.This project seeks to leverage Twitter posts (tweets) as the primary source of citizen inputs and couple relevant content and network information along with microworld simulations involving human role players to measure effectiveness of various organized sensemaking strategies. To arrive at meaningful summaries of citizen input, tweet content is analyzed using a semantic content analysis by combining natural language techniques that are suitably fused with existing knowledge bases (GeoNames, Wikipedia). Content analysis is further enhanced by innovatively combining it with the dynamic analysis of the twitter network to realize concise and trustworthy information nuggets of potential interest to organizations and citizens. The resulting summaries will be fed to a suitably designed microworld simulation involving human actors to derive realistic settings for modeling disaster situations and typical organizational structures.This project is expected to have a significant impact in the specific context of disaster and emergency response. However, elements of this research are expected to have much wider utility, for example in the domains of e-commerce, and social reform. From a computational perspective, this project introduces the novel paradigm of people-content-network analysis whose application is not just limited to organized sensemaking. For social scientists, it provides a platform that can be used to assess relative efficacy of various organizational structures using microworld simulations and is expected to provide new insights into the types of social network structures (mix of symmetric and asymmetric) that might be better suitable to propagate information in emergent situations. From an educational standpoint, the majority of funds will be used to train the next generation of interdisciplinary researchers drawn from the computational and social sciences. Research activities will also be integrated with graduate course work. Participation of underrepresented groups will be encouraged. Datasets and software developed as part of this project will be made available to the broader research community via the project page (http://knoesis.org/research/semspc/projects/socs).

这项合作研究利用了莱特州立大学(IIS-1111182)和俄亥俄州立大学(IIS-1111118)研究人员的专业知识。在线社交网络和始终连接的移动设备创造了一个巨大的机会，使公民和组织能够在重大事件发生后进行有效的沟通和协调。具体地说，使用Twitter向救援组织提供有关紧急情况的及时和情势信息并进行临时协调的例子很多。然而，很少有人试图理解以更协调的方式使用社交网络来有效地制造组织轰动效应的全部后果。该项目旨在开展涉及计算机和社会科学家的多学科研究，以填补这一空白。该项目寻求利用推特帖子(Tweet)作为公民输入的主要来源，并将相关内容和网络信息与涉及人类角色扮演者的微观世界模拟结合在一起，以衡量各种有组织的制造轰动效应的策略的有效性。为了获得对公民输入的有意义的摘要，通过结合与现有知识库(地理名称、维基百科)适当融合的自然语言技术，使用语义内容分析来分析推文内容。通过创新地将内容分析与推特网络的动态分析相结合，进一步加强了内容分析，以实现各组织和公民可能感兴趣的简明和可信的信息。由此产生的总结将被提供给一个包括人类行为者在内的适当设计的微观世界模拟，以得出用于模拟灾害情况和典型组织结构的现实环境。该项目预计将在特定的灾难和应急响应背景下产生重大影响。然而，这项研究的内容预计将具有更广泛的实用价值，例如在电子商务和社会改革领域。从计算的角度来看，这个项目引入了人-内容-网络分析的新范式，其应用不仅仅限于有组织的感觉制造。对于社会科学家来说，它提供了一个平台，可以用来使用微观世界模拟来评估各种组织结构的相对有效性，并有望为可能更适合在紧急情况下传播信息的社会网络结构类型(对称和非对称的混合)提供新的见解。从教育的角度来看，大部分资金将用于培训来自计算和社会科学的下一代跨学科研究人员。研究活动也将与研究生课程工作相结合。将鼓励代表人数不足的群体参加。作为该项目的一部分开发的数据集和软件将通过项目页面(http://knoesis.org/research/semspc/projects/socs).提供给更广泛的研究社区