Research Article
Fake News Detection Based on Multi-view Fuzzy Clustering Algorithm
@INPROCEEDINGS{10.1007/978-3-031-55993-8_12, author={Hoang Thi Canh and Pham Huy Thong and Le Truong Giang and Phan Dang Hung}, title={Fake News Detection Based on Multi-view Fuzzy Clustering Algorithm}, proceedings={Ad Hoc Networks. 14th EAI International Conference, AdHocNets 2023, Hanoi, Vietnam, November 10-11, 2023, Proceedings}, proceedings_a={ADHOCNETS}, year={2024}, month={3}, keywords={Multi-view data multi-view clustering fuzzy clustering}, doi={10.1007/978-3-031-55993-8_12} }- Hoang Thi Canh
Pham Huy Thong
Le Truong Giang
Phan Dang Hung
Year: 2024
Fake News Detection Based on Multi-view Fuzzy Clustering Algorithm
ADHOCNETS
Springer
DOI: 10.1007/978-3-031-55993-8_12
Abstract
The rapid development of technology and the internet has enabled users to access and share a large amount of information from various sources. This brings many benefits, but also the emergence of false and inaccurate information, also known as fake news. Fake news can lead to misunderstandings and significant impacts on the economy and society. Therefore, detecting and minimizing fake news is necessary. Machine learning algorithms and artificial intelligence technology can be used to detect and eliminate fake news. In this paper, we propose a new method for detecting fake news using multi-view fuzzy clustering on multi-view data collected from multiple sources. Our proposed method first extracts features from multi-view data, such as the title, content, and social media engagement of news articles. It then uses multi-view fuzzy clustering to group the news articles into clusters. Finally, it uses a semi-supervised learning algorithm to classify the clusters as either real or fake news. Additionally, the paper provides experimental results to evaluate the effectiveness and accuracy of the proposed algorithm.
