In Proceedings of Silicon Valley Cybersecurity Conference. SVCC 2022. Communications in Computer and Information Science, vol 1683, Bathen, L., Saldamli, G., Sun, X., Austin, T.H., Nelson, A.J. (eds). Springer, Cham.
ISSN/ISBN: Not available at this time. DOI: 10.1007/978-3-031-24049-2_3
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Abstract: Online Social Networks (OSNs) have grown exponentially in terms of active users and have now become an influential factor in the formation of public opinions. For this reason, the use of bots and botnets for spreading misinformation on OSNs has become a widespread concern. Identifying bots and botnets on Twitter can require complex statistical methods to score a profile based on multiple features. Benford’s Law, or the Law of Anomalous Numbers, states that, in any naturally occurring sequence of numbers, the First Significant Leading Digit (FSLD) frequency follows a particular pattern such that they are unevenly distributed and reducing. This principle can be applied to the first-degree egocentric network of a Twitter profile to assess its conformity to such law and, thus, classify it as a bot profile or normal profile. This paper focuses on leveraging Benford’s Law in combination with various Machine Learning (ML) classifiers to identify bot profiles on Twitter. In addition, a comparison with other statistical methods is produced to confirm our classification results.
Bibtex:
@InProceedings{10.1007/978-3-031-24049-2_3,
author="Bhosale, Sanmesh and Di Troia, Fabio",
editor="Bathen, Luis and Saldamli, Gokay and Sun, Xiaoyan and Austin, Thomas H. and Nelson, Alex J.",
title="Twitter Bots' Detection with Benford's Law and Machine Learning",
booktitle="Silicon Valley Cybersecurity Conference",
year="2022",
publisher="Springer Nature Switzerland",
address="Cham",
pages="38--54",
isbn="978-3-031-24049-2",
}
Reference Type: Conference Paper
Subject Area(s): Computer Science