III: Small: Collaborative Research: Detection and Presentation of Community and Global Event Content from Social Media Sources

III：小型：协作研究：从社交媒体源检测和呈现社区和全球活动内容

• 批准号: 1017845
• 负责人: Mor Naaman
• 金额: $ 24.99万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1017845&HistoricalAwards=false
• 关键词: III; Small; Collaborative; Research; Detection

Social media sites such as Twitter, Facebook, YouTube, and Flickr host an ever-increasing amount of user content captured or produced in association with real-world events, from presidential inaugurations to community-specific events. Unfortunately, the existing tools to find, organize, and present the social media content associated with events are extremely limited. This project will address critical end-to-end information processing and presentation methods that will transform public access to real-world event information from social media sources. In particular, this work will increase the digital presence of currently underrepresented communities and address their information needs: for these communities, events are often not covered by mainstream media, but are increasingly available on social media services. As a distinctive characteristic, the project will draw on several research areas, namely, information retrieval and databases, human-computer interaction, and social media, thus contributing to educating multidisciplinary students. The PIs will continue to include undergraduate students and students from underrepresented populations in the research.The project will result in new data analysis and visualization techniques for event-based information tasks, addressing human and computational factors in social media systems to handle vast collections of noisy, user-contributed content of widely varying structure and quality. To enable effective browsing, search, and presentation of event content, this work will use the wealth of social media documents to address several fundamental problems. The firstproblem is the detection of events in repositories of social media content. Such content, increasingly posted by users in real time, is noisy and highly heterogeneous, but can help in the early detection of a wide range of events of all sizes. The second problem is the comprehensive identification of content related to detected or known events, currently fragmented across social media sites and often hard to find and collect. The third problem is content presentation, which requires the development of novel presentation and visualization techniques for social media event content. The amount of contentavailable even for a single event can be overwhelming and hinder data exploration and sense-making. The project will create new tools that will transform the viewing experience of the event information. These tools will allow users to create and share personalized views of the event data as a story-telling practice. Finally, as a main outcome, the data used in the research will be made available to other researchers whenever possible. Moreover, another main outcome will be a publicly available prototype system based on this research, designed to help connect computing and information science challenges to the activities and natural interests of a diverse set of users.

Twitter，Facebook，YouTube和Flickr等社交媒体网站与现实事件相关的捕获或生产的用户内容不断增加，从总统就职典礼到社区特定的活动。不幸的是，发现，组织和介绍与事件相关的社交媒体内容的现有工具极为有限。该项目将解决关键的端到端信息处理和演示方法，这些方法将从社交媒体来源转化对现实世界事件信息的访问。特别是，这项工作将增加当前代表性不足的社区的数字存在并满足其信息需求：对于这些社区，主流媒体通常不涵盖事件，而是越来越多地在社交媒体服务上获得。作为一个独特的特征，该项目将借鉴多个研究领域，即信息检索和数据库，人类计算机互动和社交媒体，从而有助于教育多学科学生。 PI将继续包括研究中代表性不足人群的本科生和学生。该项目将为基于事件的信息任务提供新的数据分析和可视化技术，以解决社交媒体系统中的人类和计算因素，以处理大量嘈杂，用户限制的内容，这些内容广泛，差异差异很大。为了有效地浏览，搜索和展示事件内容，这项工作将使用大量社交媒体文件来解决几个基本问​​题。第一个问题是对社交媒体内容存储库中事件的检测。这种内容越来越多地由用户实时发布，这是嘈杂且高度异质性的，但是可以帮助早期发现各种大小的各种事件。第二个问题是对与检测或已知事件有关的内容的全面识别，目前跨社交媒体网站分散，通常很难找到和收集。第三个问题是内容演示，它需要开发新颖的表现和可视化技术来为社交媒体事件内容开发。即使是单个事件的内容量也可能是压倒性的，并且会阻碍数据探索和感知。该项目将创建新工具，以改变事件信息的观看体验。这些工具将允许用户创建和共享事件数据的个性化视图，作为讲故事的实践。最后，作为主要结果，研究中使用的数据将在可能的情况下向其他研究人员提供。此外，另一个主要结果将是基于这项研究的公开可用的原型系统，旨在帮助将计算和信息科学挑战与各种用户的活动和自然利益联系起来。