Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/131126
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Type: Conference paper
Title: A method to evaluate the reliability of social media data for social network analysis
Author: Weber, D.
Nasim, M.
Mitchell, L.
Falzon, L.
Citation: Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM2020), 2020 / Atzmüller, M., Coscia, M., Missaoui, R. (ed./s), pp.317-321
Publisher: IEEE
Publisher Place: online
Issue Date: 2020
Series/Report no.: Proceedings of the IEEE-ACM International Conference on Advances in Social Networks Analysis and Mining
ISBN: 9781728110561
ISSN: 2473-9928
2473-991X
Conference Name: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) (7 Dec 2020 - 10 Dec 2020 : Virtual online)
Editor: Atzmüller, M.
Coscia, M.
Missaoui, R.
Statement of
Responsibility: 
Derek Weber, Mehwish Nasim, Lewis Mitchell, Lucia Falzon
Abstract: In order 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 a measurement case study showing how the reliability of OSN data affects social network analyses. To this end we developed a systematic comparison methodology, which we applied to two parallel datasets we collected from Twitter. We found considerable differences in 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.
Rights: © 2020 IEEE
DOI: 10.1109/ASONAM49781.2020.9381461
Published version: https://doi.org/10.1109/ASONAM49781.2020
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Computer Science publications

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