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A Framework for Fake News Detection Based on the Wisdom of Crowds and the Ensemble Learning Model

Hai Bang Truong^{1, 2} and Van Cuong Tran³

1. Faculty of Computer Science, University of Information Technology
2. Vietnam National University
   700000 Ho Chi Minh City, Vietnam
   bangth@uit.edu.vn
3. Faculty of Engineering and Information Technology, Quang Binh University
   47100 Quang Binh, Vietnam
   vancuongqbuni@gmail.com

Abstract

Nowadays, the rapid development of social networks has led to the proliferation of social news. However, the spreading of fake news is a critical issue. Fake news is news written to intentionally misinform or deceive readers. News on social networks is short and lacks context. This makes it difficult for detecting fake news based on shared content. In this paper, we propose an ensemble classification model to detect fake news based on exploiting the wisdom of crowds. The social interactions and the user’s credibility are mined to automatically detect fake news on Twitter without considering news content. The proposed method extracts the features from a Twitter dataset and then a voting ensemble classifier comprising three classifiers namely, Support Vector Machine (SVM), Naive Bayes, and Softmax is used to classify news into two categories which are fake and real news. The experiments on real datasets achieved the highest F1 score of 78.8% which was better than the baseline by 6.8%. The proposed method significantly improved the accuracy of fake news detection in comparison to other methods.

Key words

Fake news detection, Social interaction, User's credibility, User's opinion

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS230315048T

Publication information

Volume 20, Issue 4 (September 2023)
Year of Publication: 2023
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Truong, H. B., Tran, V. C.: A Framework for Fake News Detection Based on the Wisdom of Crowds and the Ensemble Learning Model. Computer Science and Information Systems, Vol. 20, No. 4. (2023), https://doi.org/10.2298/CSIS230315048T