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Enhancing Social Media Bot Detection with Cross-Feature Gating and Residual Learning
Research Article | Published: 02 January 2026
ICCK Transactions on Emerging Topics in Artificial Intelligence Volume 3, Issue 1, 2026: 20-32
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ICCK Transactions on Emerging Topics in Artificial Intelligence, Volume 3, Issue 1, 2026: 20-32

Open Access | Research Article | 02 January 2026
Enhancing Social Media Bot Detection with Cross-Feature Gating and Residual Learning
by
Abdullah Khan Abdullah Khan 1
Arooj Fatima Arooj Fatima 1
Ridda Jamil Ridda Jamil 1
Hassan Ahmed Hassan Ahmed 2 *
Aini Saba Aini Saba 1
1 Department of Computer Science, University of Wah, Wah Cantt 47040, Pakistan
2 Department of Computer Science, FAST—National University of Computer and Emerging Sciences, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan
* Corresponding Author: Hassan Ahmed, [email protected]
DOI: 10.62762/TETAI.2025.791029
ARK: ark:/57805/tetai.2025.791029
Received: 22 July 2025, Accepted: 23 November 2025, Published: 02 January 2026  
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Abstract
The growing presence of malicious bot accounts on social media poses a threat to the authenticity of online communities, as they amplify misinformation, spread spam, and manipulate engagement. Reliable detection of these accounts is therefore essential to protect the integrity of platforms such as Instagram. This study introduces a deep learning–based detection framework built on the CrossGatedTabular (CGT) architecture, designed to learn complex patterns in user activity. To strengthen evaluation, two publicly available datasets of Instagram accounts were merged into a comprehensive benchmark representing diverse user behaviors. Natural language processing (NLP) was applied to refine textual content and metadata, enhancing the quality of feature representation. For classification, a CrossGatedTabular neural architecture is employed, which integrates cross-feature interactions, gated multilayer perceptron (MLP) layers, and regularization mechanisms to effectively capture complex patterns within the dataset. Experimental results demonstrate that the proposed approach achieves 0.9340 accuracy along with high precision, recall, and F1-scores, consistently outperforming baseline classifiers. These findings highlight the effectiveness of deep neural architectures in malicious account detection and provide a scalable solution for enhancing the trustworthiness of social media platforms.
Graphical Abstract
Enhancing Social Media Bot Detection with Cross-Feature Gating and Residual Learning
Keywords
malicious bot detection
social media security
NLP
CrossGatedTabularV4
Instagram account classification
Data Availability Statement
Data will be made available on request.
Funding
This work was supported without any funding.
Conflicts of Interest
The authors declare no conflicts of interest.
Ethical Approval and Consent to Participate
Not applicable.
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Cite This Article
APA Style
Khan, A., Fatima, A., Jamil, R., Ahmed, H., & Saba, A. (2026). Enhancing Social Media Bot Detection with Cross-Feature Gating and Residual Learning. ICCK Transactions on Emerging Topics in Artificial Intelligence, 3(1), 20–32. https://doi.org/10.62762/TETAI.2025.791029
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TY  - JOUR
AU  - Khan, Abdullah
AU  - Fatima, Arooj
AU  - Jamil, Ridda
AU  - Ahmed, Hassan
AU  - Saba, Aini
PY  - 2026
DA  - 2026/01/02
TI  - Enhancing Social Media Bot Detection with Cross-Feature Gating and Residual Learning
JO  - ICCK Transactions on Emerging Topics in Artificial Intelligence
T2  - ICCK Transactions on Emerging Topics in Artificial Intelligence
JF  - ICCK Transactions on Emerging Topics in Artificial Intelligence
VL  - 3
IS  - 1
SP  - 20
EP  - 32
DO  - 10.62762/TETAI.2025.791029
UR  - https://www.icck.org/article/abs/TETAI.2025.791029
KW  - malicious bot detection
KW  - social media security
KW  - NLP
KW  - CrossGatedTabularV4
KW  - Instagram account classification
AB  - The growing presence of malicious bot accounts on social media poses a threat to the authenticity of online communities, as they amplify misinformation, spread spam, and manipulate engagement. Reliable detection of these accounts is therefore essential to protect the integrity of platforms such as Instagram. This study introduces a deep learning–based detection framework built on the CrossGatedTabular (CGT) architecture, designed to learn complex patterns in user activity. To strengthen evaluation, two publicly available datasets of Instagram accounts were merged into a comprehensive benchmark representing diverse user behaviors. Natural language processing (NLP) was applied to refine textual content and metadata, enhancing the quality of feature representation. For classification, a CrossGatedTabular neural architecture is employed, which integrates cross-feature interactions, gated multilayer perceptron (MLP) layers, and regularization mechanisms to effectively capture complex patterns within the dataset. Experimental results demonstrate that the proposed approach achieves 0.9340 accuracy along with high precision, recall, and F1-scores, consistently outperforming baseline classifiers. These findings highlight the effectiveness of deep neural architectures in malicious account detection and provide a scalable solution for enhancing the trustworthiness of social media platforms.
SN  - 3068-6652
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  -
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@article{Khan2026Enhancing,
  author = {Abdullah Khan and Arooj Fatima and Ridda Jamil and Hassan Ahmed and Aini Saba},
  title = {Enhancing Social Media Bot Detection with Cross-Feature Gating and Residual Learning},
  journal = {ICCK Transactions on Emerging Topics in Artificial Intelligence},
  year = {2026},
  volume = {3},
  number = {1},
  pages = {20-32},
  doi = {10.62762/TETAI.2025.791029},
  url = {https://www.icck.org/article/abs/TETAI.2025.791029},
  abstract = {The growing presence of malicious bot accounts on social media poses a threat to the authenticity of online communities, as they amplify misinformation, spread spam, and manipulate engagement. Reliable detection of these accounts is therefore essential to protect the integrity of platforms such as Instagram. This study introduces a deep learning–based detection framework built on the CrossGatedTabular (CGT) architecture, designed to learn complex patterns in user activity. To strengthen evaluation, two publicly available datasets of Instagram accounts were merged into a comprehensive benchmark representing diverse user behaviors. Natural language processing (NLP) was applied to refine textual content and metadata, enhancing the quality of feature representation. For classification, a CrossGatedTabular neural architecture is employed, which integrates cross-feature interactions, gated multilayer perceptron (MLP) layers, and regularization mechanisms to effectively capture complex patterns within the dataset. Experimental results demonstrate that the proposed approach achieves 0.9340 accuracy along with high precision, recall, and F1-scores, consistently outperforming baseline classifiers. These findings highlight the effectiveness of deep neural architectures in malicious account detection and provide a scalable solution for enhancing the trustworthiness of social media platforms.},
  keywords = {malicious bot detection, social media security, NLP, CrossGatedTabularV4, Instagram account classification},
  issn = {3068-6652},
  publisher = {Institute of Central Computation and Knowledge}
}
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CC BY Copyright © 2026 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
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