Abstract
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.
Highlights
The mining and analysis of location-based social media (LBSM) has become an important task for the better understanding of social events and the functioning of the human society by evaluating information about public opinion on a topic or event
This paper aims at developing a taxonomy for describing and classifying user groups in LBSM, thereby benefiting three areas: 1.To support the selection of appropriate and valid LBSM data by choosing only those user groups that provide relevant information to answer the respective research questions; 2.To improve the analysis of LBSM data and the interpretation of the results by identifying significant user groups and recognizing their over- or underrepresentation; 3.To provide a clear terminology for distinguishing user groups on various social media platforms by merging different classification approaches
Without a detailed knowledge of the social media users, this can lead to misinterpretations of the results of the analysis – especially for self-selected datasets provided by publicly available application programming interfaces (APIs)
Summary
The mining and analysis of location-based social media (LBSM) has become an important task for the better understanding of social events and the functioning of the human society by evaluating information about public opinion on a topic or event. This paper aims at developing a taxonomy for describing and classifying user groups in LBSM, thereby benefiting three areas: 1.To support the selection of appropriate and valid LBSM data by choosing only those user groups that provide relevant information to answer the respective research questions (e.g. users discussing a particular topic or who are active in a particular region); 2.To improve the analysis of LBSM data and the interpretation of the results by identifying significant user groups and recognizing their over- or underrepresentation; 3.To provide a clear terminology for distinguishing user groups on various social media platforms by merging different classification approaches. The master dataset serves as a comparison for the over- and under-representation of the groups of the Twitter dataset
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