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

这项合作研究利用了赖特州立大学（IIS-1111182）和俄亥俄州州立大学（IIS-111118）研究人员的专业知识。在线社交网络和始终连接的移动的设备创造了巨大的机会，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。 具体而言，有许多孤立的例子表明，使用Twitter向救济组织提供有关紧急情况的及时和情况信息，并进行特别协调。然而，很少有人试图理解以更协调的方式使用社交网络的全部后果，以实现有效的组织意义构建。 本项目旨在通过计算机和社会科学家参与的多学科研究填补这一空白。本项目旨在利用Twitter帖子（tweets）作为公民输入的主要来源，并将相关内容和网络信息沿着与涉及人类角色扮演者的微观世界模拟相结合，以衡量各种有组织的意义构建策略的有效性。 为了得到公民输入的有意义的摘要，通过结合自然语言，使用语义内容分析来分析推文内容 与现有知识库（GeoNames，Wikipedia）适当融合的技术。通过创新性地将内容分析与twitter网络的动态分析相结合，内容分析得到了进一步的增强，从而实现了组织和公民潜在兴趣的简洁和可信的信息。由此产生的摘要将被馈送到一个适当设计的微观世界模拟，包括人类行为者，以获得模拟灾害情况和典型组织结构的现实设置。该项目预计将在灾害和应急响应的特定背景下产生重大影响。然而，这项研究的内容预计将有更广泛的用途，例如在电子商务和社会改革领域。 从计算的角度来看，这个项目介绍了新的范式的人的内容网络分析，其应用不仅限于有组织的意义。 对于社会科学家来说，它提供了一个平台，可用于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交网络结构类型（对称和不对称的混合）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。将鼓励代表性不足的群体参加。作为该项目一部分开发的数据集和软件将通过项目网页（http：//knoesis.org/research/semspc/projects/socs）提供给更广泛的研究界。

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).