CAREER: Novel Approaches for Reasoning about Local Communities from Social Awareness Streams Data

职业：从社会意识流数据推理当地社区的新方法

• 批准号: 1446374
• 负责人: Mor Naaman
• 金额: $ 34.89万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1446374&HistoricalAwards=false
• 关键词: CAREER; Novel; Approaches; Reasoning; about

This project will examine temporal, social, and geographic patterns in large-scale social awareness streams (SAS) data for local communities. SASs, such as Twitter and Facebook, are radically altering our society's information fabric. These new communication platforms are used by millions of people to share brief status messages in socially connected public forums. These messages expose vast amounts of data from, and about, local geographic communities -- data that reflect people's activities, interests, and attention, in thousands of localities worldwide. Using this vast and still emerging sources of data, this research program will make SAS into a viable and significant source of information with capacity to transform our understanding of local communities. As a first activity, the research will adapt algorithms from other fields to identify temporal patterns in SAS data that are stable across multiple communities. Importantly, the work will reason about how and when these patterns break. For example, SAS data may expose sleeping patterns in a community, and help identify mass anxiety when these patterns break. Further, the project will develop methods to identify differences in SAS patterns between local communities, and connect these findings to other sources of data. Next, the research will develop methods to compare how different groups (e.g., by age or ethnicity) use specific neighborhoods and cities. Finally, the work will examine the relations between local communities and network ties as reflected in SAS data. The findings will form the basis for developing novel models of computation for SAS systems, and inform the creation of tools and applications geared to re-imagine SAS as reliable information systems for local communities. The project is rooted in social computing and in human-centered approaches to development of new technology. As such, the work entails interdisciplinary investigation using methods and research questions drawing on fields as diverse as information and computer science, sociology, and communication. The project will tackle significant information challenges that these SAS and other social computing platforms present, such as the scale, bias, and the increasing amount of noise and spam, as well as the brevity and lack of context of posted messages. The research will develop novel methods and approaches to using these new information sources to extract knowledge about, and for, local communities. The research focus on social media and local communities lends itself well to outreach and education activities. The outreach efforts will enhance the connection of public libraries to the communities they serve, and relate the social media experiences of people?s everyday lives to scientific challenges. Participatory design workshops and visits to select educational institutions will engage individuals currently underrepresented in the sciences. An interdisciplinary education program will prepare a diverse set of students at all levels to lead the next generation of innovation, research, and education in socio-technical systems. Finally, this project will have a significant impact on our society. By leveraging SAS as novel sources of information, the research will lay the foundation for new studies about local communities. The resulting technologies and insights will inform and transform the work of local governments, news organizations, planners, and researchers, as well as local residents and activists, allowing them to take full advantage of these new repositories of human expression and thought with relevance to such diverse social challenges as emergency response, resource planning, and public health.

该项目将研究当地社区大规模社会意识流(SAS)数据中的时间、社会和地理模式。Twitter和Facebook等Sass正在从根本上改变我们社会的信息结构。这些新的交流平台被数百万人用来在社交网络公共论坛上分享简短的状态信息。这些信息暴露了来自当地地理社区以及与之相关的大量数据--这些数据反映了人们在全球数千个地方的活动、兴趣和关注。利用这些仍在发展中的海量数据来源，这项研究计划将使SA成为一个可行且重要的信息来源，有能力改变我们对当地社区的理解。作为第一项活动，这项研究将采用其他领域的算法来识别SAS数据中跨多个社区稳定的时间模式。重要的是，这项工作将对这些模式如何以及何时打破进行推理。例如，SAS数据可能会揭示一个社区的睡眠模式，并在这些模式被打破时帮助识别大规模焦虑。此外，该项目将开发方法来确定当地社区之间SAS模式的差异，并将这些发现与其他数据来源联系起来。接下来，研究将开发方法来比较不同群体(例如，按年龄或种族)如何使用特定的社区和城市。最后，这项工作将审查SAS数据所反映的地方社区和网络联系之间的关系。这些发现将成为为SAS系统开发新的计算模型的基础，并为创建工具和应用程序提供信息，这些工具和应用程序旨在将SAS重新想象为当地社区可靠的信息系统。该项目植根于社会计算和以人为中心的新技术开发方法。因此，这项工作需要使用方法进行跨学科调查，并利用信息和计算机科学、社会学和传播学等不同领域的研究问题。该项目将解决这些SA和其他社交计算平台带来的重大信息挑战，如规模、偏见、不断增加的噪音和垃圾邮件，以及发布的消息简短和缺乏上下文。这项研究将开发新的方法和途径，利用这些新的信息源来获取关于当地社区的知识，并为之服务。这项研究侧重于社交媒体和当地社区，很好地开展了外联和教育活动。外展工作将加强公共图书馆与其服务社区的联系，并将人们的社交媒体体验与科学挑战联系起来？S日常生活。参与式设计讲习班和对选定教育机构的访问将使目前在科学界代表性不足的个人参与进来。跨学科教育计划将培养不同层次的学生，以领导下一代社会技术系统的创新、研究和教育。最后，这个项目将对我们的社会产生重大影响。通过利用SA作为新的信息来源，这项研究将为关于当地社区的新研究奠定基础。由此产生的技术和见解将为地方政府、新闻机构、规划者和研究人员以及当地居民和活动家的工作提供信息和改变，使他们能够充分利用这些与应急响应、资源规划和公共卫生等不同社会挑战相关的人类表达和思想的新宝库。