Mitigating Misinformation: A Comparative Analysis of Machine Learning Models for Fake News Detection

DOI

10.2991/978-94-6463-876-9_13How to use a DOI?

Keywords

Fake news detection; Machine learning; Classification algorithms; Deep learning

Abstract

The 21st century has seen a significant increase in fake news, mainly due to the rise of social media and greater internet access. The rapid online spread of fake news poses a significant challenge, as traditional methods of fake news detection are struggling to catch up. To address this, our study centered on developing reliable and empirically validated data-driven algorithms for fake news detection. Through a detailed evaluation of both traditional and advanced machine learning models on datasets, our results show that while traditional models exhibit better generalization across varied datasets, deep learning models, particularly transformer-based architectures, achieve higher accuracy in detecting nuanced textual patterns. The goal of our research is to shed light on the strengths and limitations of distinct model architectures, providing insights to their capacity to generalize across various datasets and adapt to different task needs. Though traditional models frequently exhibit better generalization capabilities, the ideal model choice can depend on particular task and dataset distribution. Systematic fake news detection has the potential to preemptively halt the spread of false information, thus alleviating its damage on society. By utilizing evaluations from both traditional and deep learning models, our study aims to develop an extensive approach to fake news identification. The fundamental aim is to develop a model that shows high accuracy, robustness and generalizability across different datasets and information frameworks. Through this study, we seek to improve systematic fake news detection systems, contributing to ensure the integrity of information flow and democratic functions in the digital age.

Copyright

© 2025 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  - Alam Vamsidhara Reddy
AU  - Allugunti Harika
AU  - Amogh Deshmukh
PY  - 2025
DA  - 2025/10/23
TI  - Mitigating Misinformation: A Comparative Analysis of Machine Learning Models for Fake News Detection
BT  - Proceedings of the International Conference on Sustainable Science and Technology for Tomorrow (SciTech 2024)
PB  - Atlantis Press
SP  - 150
EP  - 162
SN  - 3091-4442
UR  - https://doi.org/10.2991/978-94-6463-876-9_13
DO  - 10.2991/978-94-6463-876-9_13
ID  - Reddy2025
ER  -