Articles | Volume 1
15 Jul 2020
 | 15 Jul 2020

A taxonomy for classifying user groups in location-based social media

Thomas Gründemann and Dirk Burghardt

Keywords: Taxonomy, User groups, Classification, Social media, Population bias

Abstract. Location-based social media provide great opportunities to monitor and map social, natural or health-related events. Due to the vast amount of data, it is appropriate for many researchers to use a judiciously selected sample of data. However, many of the datasets from social media sources do not consist of representative samples of the overall population because they do not take into account the users who generate the social media content. The consequences can be a bias of particular user groups and a misinterpretation of the analysis results. To overcome these shortcomings, this paper develops a taxonomy of user groups in social media based on a thorough literature analysis. The different approaches can be summarized to the five dimensions: character, connectivity, communication, content and coordinates. The expected use of the taxonomy is to support the selection of social media datasets by choosing only those user groups that provide relevant information and to improve the analysis by identifying significant groups. Both application areas are illustrated by using a dataset that includes the members of the German parliament who registered on Twitter.

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