A Comprehensive Survey on Machine Learning Techniques for Fake News Detection and Analysis
Author(s):
Saurabh Jaiswal, Vivek Rai
Keywords:
Fake News, media, public opinion, machine learning (ML), deep learning
Abstract
The proliferation of fake news poses significant challenges to society, undermining trust in media and impacting public opinion and behavior. As the digital landscape expands, the need for effective detection and analysis of fake news becomes increasingly critical. This comprehensive survey examines the state-of-the-art machine learning techniques employed in the identification and analysis of fake news. We systematically categorize various approaches, including supervised, unsupervised, and semi-supervised learning methods, and evaluate their performance in different contexts. Key features and data sets used in these studies are discussed, alongside an analysis of the strengths and limitations of each technique. Furthermore, we explore the challenges faced in the fake news detection landscape, such as the dynamic nature of fake news, data scarcity, and the need for real-time processing. Future research directions are proposed to address these challenges, emphasizing the integration of multi-modal data, advanced natural language processing techniques, and the potential of deep learning models. This survey aims to provide researchers and practitioners with a detailed understanding of current methodologies and inspire innovative solutions to enhance the effectiveness of fake news detection systems.
Article Details
Unique Paper ID: 166347

Publication Volume & Issue: Volume 11, Issue 2

Page(s): 411 - 418
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