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For example, malicious users often create fake accounts and fake followers to increase their popularity and attract more sponsors, followers, and so on, potentially producing several negative implications that impact the whole society. To deal with these issues, it is necessary to increase the capability to properly identify fake accounts and followers. By exploiting automatically extracted data correlations characterizing meaningful patterns of malicious accounts, in this article we propose a new feature engineering strategy to augment the social network account dataset with additional features, aiming to enhance the capability of existing machine learning strategies to discriminate fake accounts. Experimental results produced through several machine learning models on account datasets of both the Twitter and the Instagram platforms highlight the effectiveness of the proposed approach toward the automatic discrimination of fake accounts. The choice of Twitter is mainly due to its strict privacy laws, and because its the only social network platform making data of their accounts publicly available.<\/jats:p>","DOI":"10.1145\/3625097","type":"journal-article","created":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T11:33:24Z","timestamp":1695209604000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Malicious Account Identification in Social Network Platforms"],"prefix":"10.1145","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2418-1606","authenticated-orcid":false,"given":"Loredana","family":"Caruccio","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Salerno, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8061-7104","authenticated-orcid":false,"given":"Gaetano","family":"Cimino","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Salerno, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0201-2753","authenticated-orcid":false,"given":"Stefano","family":"Cirillo","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Salerno, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6327-459X","authenticated-orcid":false,"given":"Domenico","family":"Desiato","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Bari Aldo Moro, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8496-2658","authenticated-orcid":false,"given":"Giuseppe","family":"Polese","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Salerno, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4765-8371","authenticated-orcid":false,"given":"Genoveffa","family":"Tortora","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Salerno, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,11,17]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/I2CACIS54679.2022.9815466"},{"issue":"20","key":"e_1_3_2_3_2","first-page":"3267","article-title":"Spammer detection in social network using na\u00efve Bayes","volume":"118","author":"Anitha R.","year":"2018","unstructured":"R. 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