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Journal of Information and Optimization Sciences cover

Journal of Information and Optimization Sciences

(Since 1980)
Hybrid ·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667

WoS  JIF 2026 : 0.4 (Q4)

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Monthly Journal: Publishes theoretical and applied research on topics in information and optimization sciences.

Issues up to 2022 co-published with and available at:Taylor & Francis
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Journal of Information and Optimization Sciences
Open Access Research Article

Design of an efficient deep pattern analysis model for the identification of bot-based fake profiles

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pp. 1851–1859Vol. 46Issue 6September 2025DOI: 10.47974/JIOS-2010XML
Received:
10 Dec 2024
Published Online:
30 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

Bot-generated profilesDeep dyna-Q networksFake profile detectionMultidomain feature analysis

Subject Classifications

68T0568T0968T2768R10

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