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Type: Journal article
Title: Exploring the effect of streamed social media data variations on social network analysis
Author: Weber, D.
Nasim, M.
Mitchell, L.
Falzon, L.
Citation: Social Network Analysis and Mining, 2021; 11(1):62-1-62-38
Publisher: Springer-Verlag
Issue Date: 2021
ISSN: 1869-5450
Statement of
Derek Weber, Mehwish Nasim, Lewis Mitchell, Lucia Falzon
Abstract: To study the effects of online social network (OSN) activity on real-world offline events, researchers need access to OSN data, the reliability of which has particular implications for social network analysis. This relates not only to the completeness of any collected dataset, but also to constructing meaningful social and information networks from them. In this multidisciplinary study, we consider the question of constructing traditional social networks from OSN data and then present several measurement case studies showing how variations in collected OSN data affect social network analyses. To this end, we developed a systematic comparison methodology, which we applied to five pairs of parallel datasets collected from Twitter in four case studies. We found considerable differences in several of the datasets collected with different tools and that these variations significantly alter the results of subsequent analyses. Our results lead to a set of guidelines for researchers planning to collect online data streams to infer social networks.
Keywords: Social media analytics; Dataset reliability; Social network analysis
Rights: © Crown 2021
DOI: 10.1007/s13278-021-00770-y
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Computer Science publications

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