Fake News Detection: Methods, Datasets, and Challenges

DOI

10.2991/978-94-6239-789-7_48How to use a DOI?

Keywords

Fake news detection; machine learning; deep learning; artificial intelligence; transformer models; RNN; CNN; Hybrid models; benchmark; datasets; low-resource language; code-mixed datasets; Feature Engineering

Abstract

The exponential growth of online platforms, has accelerated the dissemination of both authentic and misleading information. Researchers adopt the machine learning (ML) and deep learning (DL) based approaches for fake news detection in the recent years, in which the performance of DL models has been found to be quite promising compared to conventional ML techniques. The main focus of this review is to systematically explore the existing research. This review analyses 68 research articles published between 2011 and 2025 focusing on machine and deep learning approaches for fake news detection. It presents a comparative analysis of models, feature engineering techniques, widely used datasets, and language-specific efforts. The aim is to gain clarity as to which approach is more effective in which context, and where limitations exist. The paper highlights the challenges and open issues, like dataset bias, interpretability problems and evaluation inconsistencies. It further identifies critical research gaps and outlines future directions including robust dataset development, integration of explainable AI techniques, and standardized evaluation frameworks. Overall, this survey provides a structured taxonomy and comparative insight that can guide future research and real-world deployment of fake news detection systems.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial 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 license and indicate if changes were made.

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TY  - CONF
AU  - Nimra Mirza
AU  - Vishal Kohli
PY  - 2026
DA  - 2026/10/08
TI  - Fake News Detection: Methods, Datasets, and Challenges
BT  - Proceedings of theInternational Conference on Innovation and Practices in Artificial Intelligence (ICIPAI 2026)
PB  - Atlantis Press
SP  - 715
EP  - 729
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6239-789-7_48
DO  - 10.2991/978-94-6239-789-7_48
ID  - Mirza2026
ER  -