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Novel authorship verification model for social media accounts compromised by a human

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A Correction to this article was published on 16 February 2021

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Abstract

Social media networks usage is spreading but accompanied by a new shape of the social engineering attacks in which users’ accounts are compromised by attackers to spread malicious messages for different purposes. To overcome these attacks, authorship verification, a classification problem for classifying a text, whether it belongs to a user or not, is needed. Moreover, the verification must be accurate and fast. Herein, an authorship verification model proposed. The model uses XGBoost, as a preprocessor, to discover functional features of the text message, which ranked using MCDM methods to build a classification model. Twitter messages are used to test the model; however, any social media’s data might be used. The suggested model was evaluated against a crawled dataset from Twitter composed of 16124 tweets with 280 characters. The proposed method achieved F-score over 0.94.

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Authors and Affiliations

  1. Kırıkkale University, Kırıkkale, Turkey

    Suleyman Alterkavı

  2. Computer Engineering Department, Engineering Faculty, University of Turkish Aeronautical Association, 06790, Etimesgut, Ankara, Turkey

    Hasan Erbay

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  1. Suleyman Alterkavı
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  2. Hasan Erbay
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Correspondence to Suleyman Alterkavı.

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The original online version of this article was revised: The author name “Suleyman Alterkavı” was incorrectly presented.

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Alterkavı, S., Erbay, H. Novel authorship verification model for social media accounts compromised by a human. Multimed Tools Appl 80, 13575–13591 (2021). https://doi.org/10.1007/s11042-020-10361-2

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