A Comprehensive Study on Data-Driven Fake News Detection Methods

Social Media has become the new hub to obtain news rather than more traditional platforms. The news available on such effortlessly accessible platform has a significant influence on people's lives politically, socially, and psychologically. Fake news spread from this platform is an issue of gra...

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Bibliographic Details
Main Authors: Singh, Kavinder, Aditi, Aditi, Jaiswal, Amar Kumar, Shivam, Ananya
Format: Conference Proceeding
Language:English
Subjects:
Analytical models
Biological system modeling
Computational modeling
Data-driven approaches
Fake news
Psychology
Semisupervised learning
Social media
Social networking (online)
Stacking
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Summary:Social Media has become the new hub to obtain news rather than more traditional platforms. The news available on such effortlessly accessible platform has a significant influence on people's lives politically, socially, and psychologically. Fake news spread from this platform is an issue of grave concern. Spreading fake news is easy on social media, which may cause many issues in the society. Thus, Detecting fake news is one of the crucial task. Many algorithms and data- driven methods are available for detection of fake news. This paper presents a comprehensive analysis of different fake news detection algorithms that are based on data-driven approach. Different models are analysed to study their impact, advantages and limitations. Further, this paper contains a discussion of various details related to the multiple datasets available for the task of fake-news detection.
ISSN:2768-5330
DOI:10.1109/ICICCS53718.2022.9788406