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Social Network Analysis and Mining

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01-12-2021 | Original Article

Leveraging node neighborhoods and egograph topology for better bot detection in social graphs

Authors: Björn Bebensee, Nagmat Nazarov, Byoung-Tak Zhang

Published in: Social Network Analysis and Mining | Issue 1/2021

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Abstract

Due to their popularity, online social networks are a popular target for spam, scams, malware distribution and more recently state-actor propaganda. In this paper, we review a number of recent approaches to fake account and bot classification. Based on this review and our experiments, we propose our own method which leverages the social graph’s topology and differences in egographs of legitimate and fake user accounts to improve identification of the latter. We evaluate our approach against other common approaches on a real-world dataset of users of the social network Twitter.

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Title: Leveraging node neighborhoods and egograph topology for better bot detection in social graphs
Authors: Björn Bebensee
Nagmat Nazarov
Byoung-Tak Zhang
Publication date: 01-12-2021
Publisher: Springer Vienna
Published in: Social Network Analysis and Mining / Issue 1/2021
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-020-00713-z

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