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 等社交媒体网站托管着与现实世界事件（从总统就职典礼到社区特定活动）相关的捕获或生成的用户内容，数量不断增加。不幸的是，用于查找、组织和呈现与事件相关的社交媒体内容的现有工具极其有限。该项目将解决关键的端到端信息处理和呈现方法，这些方法将改变公众从社交媒体源获取现实世界事件信息的方式。特别是，这项工作将增加目前代表性不足的社区的数字存在，并满足他们的信息需求：对于这些社区来说，主流媒体通常不会报道事件，但社交媒体服务上的事件越来越多。作为一个鲜明的特点，该项目将利用多个研究领域，即信息检索和数据库、人机交互和社交媒体，从而有助于教育多学科的学生。 PI 将继续让本科生和来自弱势群体的学生参与研究。该项目将为基于事件的信息任务带来新的数据分析和可视化技术，解决社交媒体系统中的人为和计算因素，以处理大量嘈杂的、用户贡献的、结构和质量各异的内容。为了实现活动内容的有效浏览、搜索和呈现，这项工作将利用丰富的社交媒体文档来解决几个基本问​​题。第一个问题是社交媒体内容存储库中事件的检测。此类内容越来越多地由用户实时发布，噪音较大且高度异构，但有助于及早检测各种规模的各种事件。第二个问题是全面识别与检测到或已知事件相关的内容，这些内容目前分散在社交媒体网站上，并且通常很难查找和收集。第三个问题是内容呈现，这需要为社交媒体事件内容开发新颖的呈现和可视化技术。即使单个事件的可用内容量也可能令人难以承受，并阻碍数据探索和意义构建。该项目将创建新工具来改变活动信息的观看体验。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，作为主要成果，研究中使用的数据将尽可能提供给其他研究人员。此外，另一个主要成果将是基于这项研究的公开原型系统，旨在帮助将计算和信息科学挑战与不同用户的活动和自然兴趣联系起来。