Citation :

Pundlik Dattatray Jadhav, Rajesh k Shukla, "Synthesizing Political Context Understanding with AI Techniques to Improve Fake News Detection Performance across Regions," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 6, pp. 365-385, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I6P125

Abstract

Fake news is spreading quickly on the internet, which is very bad for society and the security of the government. The significant issue that was talked about in the paper was the creation of automatic systems that can detect fake news better and adapt to various areas. The dataset used in the study is the LIAR dataset, which is a standard set of various political statements labeled with varying degrees of truthfulness. Text is also cleaned up, tokenized, and represented with existing trained word embeddings such as GloVe and Word2Vec as a step in data preparation. To identify complex trends in the text, most language and contextual features are removed, such as syntactic, semantic, and sentiment-based ones. The primary contribution of this study is a way of grouping various features into one representation. A set of models is subjected to performance tests, and it includes Random Forest, Naive Bayes, Convolutional Neural Network (CNN), Autoencoder, and a proposed Hybrid CNN-Autoencoder architecture. The hybrid model performs the most, having the greatest precision and the most equalized classification scores. Comparative analysis demonstrates that the combination of deep learning and knowledge of the environment significantly enhances the level of detection in domains. It is a flexible AI-based system that can work in the context of language and political differences and is a big step forward in searching for fake information automatically.

Keywords

Fake news detection, Political context analysis, Feature fusion, Deep Learning, CNN–autoencoder, LIAR dataset.

References

[1] Maialen Berrondo-Otermin, and Antonio Sarasa-Cabezuelo, “Application of Artificial Intelligence Techniques to Detect Fake News: A Review,” Electronics, vol. 12, no. 24, pp. 1-12, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[2] P. Deepak et al., “Geo-Political Bias in Fake News Detection AI: the Case of Affect,” AI and Ethics, vol. 5, no. 2, pp. 1865-1870, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[3] Suhaib Kh Hamed, Mohd Juzaiddin Ab Aziz, and Mohd Ridzwan Yaakub, “A Review of Fake News Detection Approaches: A Critical Analysis of Relevant Studies and Highlighting Key Challenges Associated with the Dataset, Feature Representation, and Data Fusion,” Heliyon, vol. 9, no. 10, pp. 1-21, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[4] Pervaiz Akhtar et al., “Detecting Fake News and Disinformation using Artificial Intelligence and Machine Learning to Avoid Supply Chain Disruptions,” Annals of Operations Research, vol. 327, no. 2, pp. 633-357, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[5] Bo Hu, Zhendong Mao, and Yongdong Zhang, “An Overview of Fake News Detection: From A New Perspective,” Fundamental Research, vol. 5, no. 1, pp. 332-346, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[6] Sanjeev Kumar et al., “Peeping into the Future: Understanding and Combating Generative AI-based Fake News,” Cognitive Computation, vol. 17, no. 3, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[7] Ankita Bhandarkar et al., “Deep Learning Framework for Robust Deep Fake Image Detection: A Review,” 2024 International Conference on Artificial Intelligence and Quantum Computation-based Sensor Application (ICAIQSA), Nagpur, India, pp. 1-7, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[8] Raffaele Guarasci et al., “BERT Syntactic Transfer: A Computational Experiment on Italian, French and English Languages,” Computer Speech and Language, vol. 71, 2022.
[CrossRef] [Google Scholar] [Publisher Link]

[9] Alexander Romanishyn, Olena Malytska, and Vitaliy Goncharuk, “AI-Driven Disinformation: Policy Recommendations for Democratic Resilience,” Frontiers in Artificial Intelligence, vol. 8, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[10] Jingyuan Yi et al., “Challenges and Innovations in LLM-Powered Fake News Detection: A Synthesis of Approaches and Future Directions,” Proceedings of the 2025 2^nd International Conference on Generative Artificial Intelligence and Information Security, Association for Computing Machinery, New York, NY, United States, pp. 87-93, 2025. [CrossRef] [Google Scholar] [Publisher Link]

[11] Wajiha Shahid et al., “Detecting and Mitigating the Dissemination of Fake News: Challenges and Future Research Opportunities,” IEEE Transactions on Computational Social Systems, vol. 11, no. 4, pp. 4649-4662, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[12] Bahman Jamshidi, Saqib Hakak, and Rongxing Lu, “A Self-Attention Mechanism-based Model for Early Detection of Fake News,” IEEE Transactions on Computational Social Systems, vol. 11, no. 4, pp. 5241-5252, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[13] Asma Sormeily et al., “MEFaND: A Multimodel Framework for Early Fake News Detection,” IEEE Transactions on Computational Social Systems, vol. 11, no. 4, pp. 5337-5353, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[14] Fiza Gulzar Hussain et al., “Fake News Detection Landscape: Datasets, Data Modalities, AI Approaches, their Challenges, and Future Perspectives,” IEEE Access, vol. 13, pp. 54757-54778, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[15] Sanjay Kumar et al., “OptNet-Fake: Fake News Detection in Socio-Cyber Platforms using Grasshopper Optimization and Deep Neural Network,” IEEE Transactions on Computational Social Systems, vol. 11, no. 4, pp. 4965-4974, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[16] Cheng Xu, and M-Tahar Kechad, “An Enhanced Fake News Detection System with Fuzzy Deep Learning,” IEEE Access, vol. 12, pp. 88006-88021, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[17] Guofan Liu et al., “Uni-Modal Event-Agnostic Knowledge Distillation for Multimodal Fake News Detection,” IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 12, pp. 9490-9503, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[18] Hanen Himdi et al., “Arabic Fake News Dataset Development: Humans and AI-Generated Contributions,” IEEE Access, vol. 13, pp. 62234-62253, 2025.
[CrossRef] [Google Scholar] [Publisher Link]

[19] Amila Silva et al., “Unsupervised Domain-Agnostic Fake News Detection using Multi-Modal Weak Signals,” IEEE Transactions on Knowledge and Data Engineering, vol. 36, no. 11, pp. 7283-7295, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[20] Jiao Shi et al., “Rough-Fuzzy Graph Learning Domain Adaptation for Fake News Detection,” IEEE Transactions on Computational Social Systems, vol. 11, no. 4, pp. 5275-5286, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[21] Sajjad Dadkhah et al., “The Largest Social Media Ground-Truth Dataset for Real/Fake Content: TruthSeeker,” IEEE Transactions on Computational Social Systems, vol. 11, no. 3, pp. 3376-3390, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[22] Zhibo Qu et al., “Temporal Enhanced Multimodal Graph Neural Networks for Fake News Detection,” IEEE Transactions on Computational Social Systems, vol. 11, no. 6, pp. 7286-7298, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[23] Rohit Kumar Kaliyar et al., “Understanding the use and Abuse of Social Media: Generalized Fake News Detection with a Multichannel Deep Neural Network,” IEEE Transactions on Computational Social Systems, vol. 11, no. 4, pp. 4878-4887, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[24] Balaji Adusumalli et al., “LSTM-Powered Spam Detection: A Deep Learning Approach for Sequential Text Classification,” International Journal of Recent Advances in Engineering and Technology, vol. 14, no. 1, pp. 11-20, 2024.
[Google Scholar]

[25] Gülsüm Kayabaşi Koru, and Çelebı Uluyol, “Detection of Turkish Fake News from Tweets with BERT Models,” IEEE Access, vol. 12, pp. 14918-14931, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[26] Fadwa Alrowais et al., “Boosting Deep Feature Fusion-based Detection Model for Fake Faces Generated by Generative Adversarial Networks for Consumer Space Environment,” IEEE Access, vol. 12, pp. 147680-147693, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[27] Jinxia Wang et al., “Fake News in Virtual Community, Virtual Society, and Metaverse: A Survey,” IEEE Transactions on Computational Social Systems, vol. 11, no. 4, pp. 4828-4842, 2024.
[CrossRef] [Google Scholar] [Publisher Link]