政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/150569

English | 正體中文 | 简体中文 | Post-Print筆數 : 27 | Items with full text/Total items : 113656/144643 (79%)
Visitors : 51713099 Online Users : 133

RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.

Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer

政大機構典藏 > 商學院 > 資訊管理學系 > 期刊論文 > Item 140.119/150569

Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/150569

Title:	Combating Online Malicious Behavior: Integrating Machine Learning and Deep Learning Methods for Harmful News and Toxic Comments
Authors:	簡士鎰 Chien, Shih-Yi;Lin, Szu-Yin;Chen, Yi-Zhen;Chien, Yu-Hang
Contributors:	資管系
Keywords:	Artifcial intelligence;Machine learning;Deep learning;Malicious behavior;Harmful news;Toxic comments
Date:	2024-09
Issue Date:	2024-03-26 15:24:08 (UTC+8)
Abstract:	The surge in online media has inundated the public with information, prompting the use of sensational and provocative language to capture attention, worsening the prevalence of online malicious behavior. This study delves into machine learning (ML) and deep learning (DL) techniques to identify and recognize harmful news and toxic comments, aiming to counteract the detrimental impact on public perception. Effective methods for detecting and categorizing malicious content are proposed and discussed, highlighting the differences between ML and DL approaches in combating malicious behavior. The study employs feature selection methods to scrutinize the distinctive feature set and keywords linked to harmful news and toxic comments. The proposed approach yields promising outcomes, achieving a 94% accuracy rate in recognizing toxic comments, a 68% recognition accuracy for harmful news, and an 81% accuracy in classifying malicious behavior content (combining harmful news and toxic comments). By harnessing the capabilities of ML and DL, this research enriches our comprehension of and ability to mitigate malicious behavior in online media. It provides valuable insights into the practical identification and categorization of harmful news and toxic comments, highlighting the unique facets of these advanced computational strategies as they address the pressing challenges of our digital society.
Relation:	Information Systems Frontiers, pp.1-16
Data Type:	article
Appears in Collections:	[資訊管理學系] 期刊論文

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	191	View/Open

社群 sharing

著作權政策宣告 Copyright Announcement

1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.

Loading...