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Social Network Analysis and Mining
Erschienen in: Social Network Analysis and Mining 1/2022

01.12.2022 | Original Article

UACD: A Local Approach for Identifying the Most Influential Spreaders in Twitter in a Distributed Environment

verfasst von: T M Tariq Adnan, Md Saiful Islam, Tarikul Islam Papon, Shourav Nath, Muhammad Abdullah Adnan

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2022

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Abstract

Efficient spreading of important information through social media can be highly beneficial, while quick spreading of false content is alarming. Finding the users who are the most influential at information spreading can help develop efficient strategies. However, with the increasing growth of gigantic social networks, existing methods either lack accuracy or have high latency, sometimes being infeasible within limited memory. In this study, we find that rich user-specific information can guide us toward designing more effective methods. We propose UACD, a novel method for identifying the most influential spreaders on the Twitter social network by combining both user-specific and topological information. We provide a distributed implementation of our proposed algorithm on the Amazon EC2 and compare our ranking result with the state-of-the-art methods. Results suggest that UACD is scalable and can process a very large network while being on average \(\mathbf {12.5}\%\) more accurate and \(\mathbf {175}{\times }\) faster.
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Metadaten
Titel
UACD: A Local Approach for Identifying the Most Influential Spreaders in Twitter in a Distributed Environment
verfasst von
T M Tariq Adnan
Md Saiful Islam
Tarikul Islam Papon
Shourav Nath
Muhammad Abdullah Adnan
Publikationsdatum
01.12.2022
Verlag
Springer Vienna
Erschienen in
Social Network Analysis and Mining / Ausgabe 1/2022
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-022-00862-3

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