Mitigating Misinformation: A Comparative Analysis of Machine Learning Models for Fake News Detection
Year: 2025
Type: book-chapter
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.
Authors: Alam Vamsidhara Reddy, Allugunti Harika, Amogh Deshmukh
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: gold
OpenAlex ID: W4415435776