Spatial Correlations in Social Media Data: Identification and Quantification of Spatial Correlation Structures in Georeferenced Twitter Feeds

社交媒体数据中的空间相关性：地理参考 Twitter 源中空间相关结构的识别和量化

• 批准号: 314646487
• 负责人: Professor Dr. Alexander Zipf
• 金额: --
• 依托单位: Professur für Geoinformatik / GIScience (GIS)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 财政年份: 2016
• 资助国家: 德国
• 起止时间: 2015-12-31 至 2019-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/314646487?language=en
• 关键词: Spatial; Correlations; Social; Media; Data

Social media feeds are one of the growing numbers of sources of volunteered geographic information. Thereby, over recent years, this kind of data has proven to be a rich source of information for many areas of research. This proposal aims to contribute methodological advancements, whereby we focus on Twitter data. Specifically, we aim to explore novel ways to derive spatial correlation structures within social media feeds. Our work builds upon the mature theory of spatial autocorrelation, which is the traditional way of measuring spatial structure.The first research question is concerned with integrating the theory of spatial autocorrelation with the geometric stochasticity of tweets. The latter is typically investigated by means of stochastic geometry. We aim to combine principles from both fields in order to derive more accurate correlation structures within tweets. In a first step we investigate the effect of the stochastic geometries on spatial autocorrelation measures. This includes point pattern modelling and a Monte Carlo simulation study. That investigation will provide insights regarding a better interpretation of autocorrelation results. Moreover, the gained knowledge allows detailed insights into the variability of inter-tweet correlations of certain social activities. After this exploratory study, we investigate a measure of spatial autocorrelation that acknowledges the stochasticity of the underlying geometric structure and is thus able to obtain meaningful patterns within social media data.Secondly we investigate the mutually overlapping character of phenomena that are reflected within the tweets. This overlap is caused by the autonomous behaviour of the users, which report about multiple phenomena simultaneously in space and time. We aim to explore ways of separating relevant tweets from non-relevant ones. This is done by means of Dempster-Shafer theory and Dirichlet processes. The challenge thereby is to disentangle the geometrically overlapping neighbourhoods. In a second step we expand spatial autocorrelation measures towards acknowledging this overlapping character by means of partial autocorrelation functions. This will prevent mixing different phenomena and leads to realistic dependency structures.While the first two packages focus on the point level, the third aspect addresses suitable aggregation strategies. These strategies involve traditional clustering techniques and indices from point pattern analysis. This allows analysing dependencies between different kinds of compound social activities. Further, aggregating tweets allows investigating the relationship of social processes towards their immediate surroundings. This will be a second step of this work package.Overall, our research will enable for gaining an increased and detailed understanding of social activities and their respective spatial mechanisms through improved methods allowing to analyse representations of these within socio-technical systems.

社交媒体是越来越多的自愿地理信息来源之一。因此，近年来，这种数据已被证明是许多研究领域的丰富信息来源。该提案旨在促进方法上的进步，使我们专注于Twitter数据。具体来说，我们的目标是探索新的方法来获得空间相关结构的社交媒体饲料。我们的工作建立在空间自相关理论的基础上，这是传统的测量空间结构的方法。第一个研究问题是将空间自相关理论与推文的几何随机性相结合。后者通常是通过随机几何调查。我们的目标是联合收割机原则，从这两个领域，以获得更准确的相关性结构内推。在第一步中，我们调查的随机几何空间自相关措施的效果。这包括点模式建模和蒙特卡罗模拟研究。该调查将提供关于更好地解释自相关结果的见解。此外，所获得的知识允许详细了解某些社交活动的推特间相关性的变化。在这个探索性的研究之后，我们研究了一种空间自相关性的测量方法，这种方法承认潜在的几何结构的随机性，从而能够在社交媒体数据中获得有意义的模式。其次，我们研究了推文中反映的现象的相互重叠的特征。这种重叠是由用户的自主行为造成的，他们在空间和时间上同时报告多种现象。我们的目标是探索将相关推文与非相关推文分开的方法。这是通过Dempster-Shafer理论和Dirichlet过程完成的。因此，挑战在于理清几何上重叠的邻域。在第二步中，我们扩大空间自相关措施承认这种重叠的特点，通过偏自相关函数。这将防止混合不同的现象，并导致现实的依赖结构。虽然前两个包侧重于点级别，第三个方面解决合适的聚合策略。这些策略涉及传统的聚类技术和点模式分析的指数。这允许分析不同类型的复合社会活动之间的依赖关系。此外，聚合推文允许调查社会进程对其周围环境的关系。这将是该工作包的第二步。总的来说，我们的研究将通过改进的方法来分析社会技术系统中的社会活动及其各自的空间机制，从而提高对社会活动及其空间机制的详细了解。