| dc.contributor | 資管系 | |
| dc.creator (作者) | 杜雨儒 | |
| dc.creator (作者) | Tu, Yu-Ju;Kao, Hanchun;Huang, Yu-Hsiang (John);Strader, Troy | |
| dc.date (日期) | 2024-03 | |
| dc.date.accessioned | 28-Oct-2024 11:42:54 (UTC+8) | - |
| dc.date.available | 28-Oct-2024 11:42:54 (UTC+8) | - |
| dc.date.issued (上傳時間) | 28-Oct-2024 11:42:54 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/154117 | - |
| dc.description.abstract (摘要) | 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. | |
| dc.format.extent | 109 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Behaviour & Information Technology, pp.1-15 | |
| dc.subject (關鍵詞) | Misinformation; fake news; detection; machine learning; majority-based | |
| dc.title (題名) | A majority-based learning system for detecting misinformation | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.1080/0144929X.2024.2326562 | |
| dc.doi.uri (DOI) | https://doi.org/10.1080/0144929X.2024.2326562 | |
