Social Media Analysis for Social Geography

社会地理学的社交媒体分析

• 批准号: ES/M001636/1
• 负责人: Paul Watson
• 金额: $ 58.34万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2014
• 资助国家: 英国
• 起止时间: 2014 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ES%2FM001636%2F1
• 关键词: Social; Media; Analysis; Geography

Tweet My Street (TMS) is a project within the SiDE Social Inclusion Through the Digital Economy Hub that has been exploring the extent to which data derived from Twitter can reveal more about spatial and temporal behaviours, and the meanings attached to these locally. This has been done with a longer-term view to supporting the coproduction and delivery of local services, effective complaint mechanisms and horizontal community support networks.The project has exposed, and is tackling, 2 of the main barriers to realising the full potential for the use of Twitter (and related social media data) in the social sciences:i) The lack of availability of tools that can allow social scientists to extract meaning from social media data. Capturing, storing, querying and visualising data derived from sources is very challenging given: a) the high rate at which it is being generated, b) the vast scale of historic data, c) the complexity involved in querying the dataii) The lack of understanding of methodological issues in the analysis of social media data, for example ethics and the validity of any conclusions drawn from the skewed sample of the population that social media users representAs these issues cannot be addressed by expertise in one field alone, the project is a multi-disciplinary collaboration involving social geographers, computer scientists and the computer industry (Red Hat Inc. who fund a PhD student working in the group, and take an active part in project meetings). The involvement of the latter represents the fact that the outputs of the research have relevance in many other areas, including marketing, e-commerce and policing.To date the project has generated some promising early results, including:- a scalable, cloud-based software infrastructure for the real-time and historic analysis of twitter data. We have demonstrated that this can scale to process data at the full rate that tweets are generated globally.- a web-based tool to provide an easy-to-use way for social scientists to query social media data and visualise the results, for example on a map.Just as importantly, it has established a successful virtuous circle for collaboration: the social scientists use the software tool to analyse twitter data in order to advance their case study; this leads to new requirements on the tool which the computer scientists attempt to address; the tool is enhanced and passed to the social scientists. The cycle then repeats.This project will build on the work carried out in the SiDE Digital Economy Hub, leveraging its results and infrastructure to extend it in five key ways:- to extend the software tool to include connection analysis (e.g. flows of tweets, changes in connection graphs following an event or intervention)- to add to the collaboration a group of statisticians to explore the use of modern statistical techniques to extract understanding from twitter data, including social graph analysis, and modelling of network dynamics and meme propagation- a new case study to explore the use of the tool to augment existing social science methods in connection analysis [Mike's work summarised here]- a new case study to use the tool to augment existing social science methods, to understand the way third sector organisations are using social media and the barriers they have to overcome to do so- to run an event to disseminate the results, the methodologies and give training on how to use the toolingAll the software generated by this project will be made freely available to the community as open-source software.

Twitter My Street （TMS）是SiDE通过数字经济中心进行社会包容的一个项目，该项目一直在探索Twitter数据在多大程度上可以揭示更多关于空间和时间行为的信息，以及这些行为在当地的意义。这样做的长期目标是支持合作生产和提供地方服务、有效的投诉机制和横向社区支助网络。该项目暴露并正在解决在社会科学中充分发挥Twitter（及相关社交媒体数据）潜力的两个主要障碍：1)缺乏可用的工具，使社会科学家能够从社交媒体数据中提取意义。捕获、存储、查询和可视化来自数据源的数据是非常具有挑战性的，因为：a)数据生成的高速率，b)历史数据的巨大规模，c)查询数据所涉及的复杂性，i)对社交媒体数据分析中的方法问题缺乏理解，例如道德和从社交媒体用户所代表的人口样本中得出的任何结论的有效性，因为这些问题不能仅靠一个领域的专业知识来解决。该项目是一个多学科合作的项目，涉及社会地理学家、计算机科学家和计算机行业（红帽公司资助了一名在该小组工作的博士生，并积极参与项目会议）。后者的参与表明，研究成果在许多其他领域具有相关性，包括市场营销、电子商务和警务。到目前为止，该项目已经产生了一些有希望的早期成果，包括：-一个可扩展的，基于云的软件基础设施，用于实时和历史分析twitter数据。我们已经证明，这可以扩展到以全局生成tweet的全部速率处理数据。一个基于网络的工具，为社会科学家提供一种易于使用的方式来查询社会媒体数据并将结果可视化，例如在地图上。同样重要的是，它为合作建立了一个成功的良性循环：社会科学家使用软件工具分析twitter数据，以推进他们的案例研究；这就对计算机科学家试图解决的工具提出了新的要求；这个工具被改进并传递给社会科学家。然后循环往复。该项目将以SiDE数字经济中心开展的工作为基础，利用其成果和基础设施，从五个关键方面对其进行扩展：-扩展软件工具，包括连接分析（例如推文流量，事件或干预后连接图的变化）-在合作中增加一组统计学家，探索使用现代统计技术从推特数据中提取理解，包括社交图分析；网络动力学和模因传播的建模——一个新的案例研究，探索使用该工具来增强现有的社会科学方法在连接分析中[迈克的工作总结在这里]——一个新的案例研究，使用该工具来增强现有的社会科学方法，了解第三部门组织使用社交媒体的方式以及他们必须克服的障碍——运行一个事件来传播结果。该项目生成的所有软件都将作为开源软件免费提供给社区。

项目成果

Enacting the Internet and Social Media on the Public Sector's Frontline

• DOI: 10.1111/ntwe.12054
• 发表时间: 2015-11
• 期刊: Structural Dimensions & Organizational Behavior eJournal
• 作者: G. Mearns;R. Richardson;L. Robson

Tweet My Street: A Cross-Disciplinary Collaboration for the Analysis of Local Twitter Data

Tweet My Street：本地 Twitter 数据分析的跨学科合作

• DOI: 10.3390/fi6020378
• 发表时间: 2014
• 期刊: Future Internet
• 影响因子: 3.4
• 作者: Mearns G