|Autor:||X. Wang, T. Gaugel, M. Keller||Links:||Link zum Artikel auf Springer.com|
|Quelle:||In: Big Data Analytics in the Social and Ubiquitous Context, Springer, ISBN 978-3-319-29008-9, pp. 70-89, 2016|
Recently, geotagged social media contents became increasingly available to researchers and were subject to more and more studies. Different spatial measures such as Focus, Entropy and Spread have been applied to describe geospatial characteristics of social media contents. In this paper, we draw the attention to the fact that these popular measures do not necessarily show the geographic relevance or dependence of social content, but mix up geographic relevance, the distribution of the user population, and sample size. Therefore, results based on these measures cannot be interpreted as geographic effects alone. By means of an assessment, based on Twitter data collected over a time span of six weeks, we highlight potential misinterpretations and we furthermore propose normalized measures which show less dependency on the underlying user population and are able to mitigate the effect of outliers.