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首页> 外文期刊>ISPRS International Journal of Geo-Information >Spatial Characteristics of Twitter Users—Toward the Understanding of Geosocial Media Production
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Spatial Characteristics of Twitter Users—Toward the Understanding of Geosocial Media Production

机译：Twitter用户的空间特征：对地理社会媒体生产的理解

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摘要

Social media is a rich source of spatial data but it has also many flaws and well-known limitations, especially in regard to representation and representativeness, since very little is known about the demographics of the user population. At the same time, the use of locational services, is in fact, dependent on those characteristics. We address this gap in knowledge by exploring divides between Twitter users, based on the spatial and temporal distribution of the content they produce. We chose five cities and data from 2015 to represent different socio-spatial contexts. Users were classified according to spatial and non-spatial measures: home range estimation; standard distance; nearest neighbor index, and; proposed localness index. There are distinct groups of geosocial media producers, which suggests that such datasets cannot be treated as uniform representations. We found a positive correlation between spatial behavior and posting activity. It is suggested that there are universal patterns of behavior that are conditioned by software services—the example of Foucauldian “technologies of self”. They can also represent the dominance of the most prolific users over the whole data stream. Results are discussed in the context of the importance and role of user location in social media.
机译：社交媒体是空间数据的丰富来源，但它也有许多缺陷和众所周知的局限性，尤其是在表示和代表性方面，因为对用户群体的人口统计知之甚少。同时，位置服务的使用实际上取决于这些特征。我们通过根据Twitter用户所产生内容的时空分布来探究他们之间的鸿沟，来解决知识方面的空白。我们选择了2015年以来的五个城市和数据来代表不同的社会空间环境。根据空间和非空间度量对用户进行分类：家庭范围估计;标准距离最近邻居索引;以及建议的局部性指数。地理社会媒体生产者有不同的类别，这表明不能将此类数据集视为统一的表示形式。我们发现空间行为与发布活动之间存在正相关。建议以软件服务为条件的普遍行为模式，例如，富考“自我技术”的例子。它们还可以代表最多产用户在整个数据流中的优势。在社交媒体中用户位置的重要性和作用的背景下讨论了结果。

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