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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\u2019s 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\u2019s 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.<\/jats:p>","DOI":"10.1007\/978-3-031-24049-2_3","type":"book-chapter","created":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T16:02:56Z","timestamp":1674057776000},"page":"38-54","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Twitter Bots\u2019 Detection with\u00a0Benford\u2019s Law and\u00a0Machine Learning"],"prefix":"10.1007","author":[{"given":"Sanmesh","family":"Bhosale","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2355-7146","authenticated-orcid":false,"given":"Fabio","family":"Di Troia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,1,19]]},"reference":[{"key":"3_CR1","unstructured":"Anaconda, I.: Conda (2017). https:\/\/docs.conda.io\/. 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