Open Access
Research Article
Design of an efficient deep pattern analysis model for the identification of bot-based fake profiles
*Bhrugumalla L. V. S. AdityaCorresponding authorblvsaditya@gmail.comSchool of Computer Science and Engineering VIT-AP UniversityAmaravati, Andhra Pradesh, 522237, India0000-0002-5988-0757View full profile → , Sachi Nandan Mohantysachinandan09@gmail.comSchool of Computer Science and Engineering VIT-AP UniversitySchool of Computer Science & Engineering (SCOPE) VIT-AP UniversityAmaravati, Andhra Pradesh, 522237, India0000-0002-4939-0797View full profile →
* Corresponding author · click or hover a name for details
- Received:
- 10 Dec 2024
- Published Online:
- 01 Sep 2025
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JIOS-2010
- Pages:
- 1851–1859
Abstract
To ensure the integrity and safety of online interactions, many fake profiles on social media are often created by harmful bots. Despite the tactics already employed, their efficacy is frequently compromised by flaws like the reliance on static profile traits and the inability to consider changing behavioral patterns. These flaws call for a more reliable and flexible method to identify bot-based fake profiles precisely. This paper offers a novel model that uses Deep Dyna-Q Networks (DDQN) to fill this gap, improving the precision and speed of bogus profile identification. In contrast to conventional approaches, the proposed model takes a closer look at an account’s activity, followers, and following in addition to its profile traits. This method considers the number and quality of these parameters and, as a result, can spot subtle patterns that may point to a fake account for real-time scenarios. The suggested methodology uses multidomain feature analysis, making understanding intricate, linked behavioral patterns easier. This in-depth investigation makes it easier to spot the abnormalities in activity, correlations in followers and followings, and discrepancies in profile information that are direct signs of fake profiles created by bots. The study’s findings show that the suggested model performs better than others. In addition to a decrease in detection delay, there is an improvement in precision, accuracy, recall, A.U.C., sensitivity, and specificity levels. These enhancements demonstrate the model’s potency and highlight the possibility of using cutting-edge deep learning algorithms to address the growing problem of fake profiles on social media sites.
Keywords
Subject Classifications
68T0568T0968T2768R10
References
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