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政大典藏 > College of Commerce > Department of MIS > Periodical Articles > Item 140.119/154117

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/154117

Title:	A majority-based learning system for detecting misinformation
Authors:	杜雨儒 Tu, Yu-Ju;Kao, Hanchun;Huang, Yu-Hsiang (John);Strader, Troy
Contributors:	資管系
Keywords:	Misinformation;fake news;detection;machine learning;majority-based
Date:	2024-03
Issue Date:	2024-10-28 11:42:54 (UTC+8)
Abstract:	Combating misinformation is both a multifaceted problem and a pressing societal concern. In response, we propose a user-centric system founded on the majority vote model, offering flexibility and synergy in integrating established machine-learning methods or classifiers such as SVM, MLP, LSTM, RF, and XGB. Computational experiments demonstrate promising results in implementing our proposed system to identify text-based fake news, advertorials, and plagiarised information in social media. The dataset employed in these experiments is primarily sourced from volunteer contributors and fact-checking websites. The result evaluation indicators encompass balanced accuracy and F1 score. Overall, this study introduces a significant and autonomous countermeasure to address misinformation.
Relation:	Behaviour & Information Technology, pp.1-15
Data Type:	article
DOI link:	https://doi.org/10.1080/0144929X.2024.2326562
DOI:	10.1080/0144929X.2024.2326562
Appears in Collections:	[Department of MIS] Periodical Articles

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