A cooperative deep learning model for fake news detection in online social networks
Author:
Publisher
Springer Science and Business Media LLC
Subject
General Computer Science
Link
https://link.springer.com/content/pdf/10.1007/s12652-023-04562-4.pdf
Reference34 articles.
1. Ahmad I, Yousaf M, Yousaf S, Ahmad MO (2020) Fake news detection using machine learning ensemble methods. Complexity. https://doi.org/10.1155/2020/8885861
2. Ali H et al (2021) All your fake detector are belong to us: evaluating adversarial robustness of fake-news detectors under black-box settings. IEEE Access. https://doi.org/10.1109/ACCESS.2021.3085875
3. Asghar MZ, Habib A, Habib A, Khan A, Ali R, Khattak A (2021) Exploring deep neural networks for rumor detection. J Ambient Intell Human Comput 12(4):4315–4333
4. Aslam N, Khan IU, Alotaibi FS, Aldaej LA, Aldubaikil AK (2021) Fake detect: a deep learning ensemble model for fake news detection. Complexity. https://doi.org/10.1155/2021/5557784
5. Chauhan P, Sharma N, Sikka G (2021) The emergence of social media data and sentiment analysis in election prediction. J Ambient Intell Human Comput 12(2):2601–2627
Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Development and Application of Online Learning System Based on Artificial Intelligence;2024 Third International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE);2024-04-26
2. Classification of Fake News Using Enhanced Capsule Neural Network;2024 IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI);2024-03-14
3. A metaheuristic optimisation-based deep learning model for fake news detection in online social networks;International Journal of Electronic Security and Digital Forensics;2024
4. Combating Fake News on Social Media: A Fusion Approach for Improved Detection and Interpretability;IEEE Access;2024
5. Fake News Detection Using Deep Learning: A Systematic Literature Review;IEEE Access;2024
1.学者识别学者识别
2.学术分析学术分析
3.人才评估人才评估
"同舟云学术"是以全球学者为主线，采集、加工和组织学术论文而形成的新型学术文献查询和分析系统，可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容，当前同舟云学术共收录了国内外主流学术期刊6万余种，收集的期刊论文及会议论文总量共计约1.5亿篇，并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询！咨询电话：010-8811{复制后删除}0370
www.globalauthorid.com
TOP
Copyright © 2019-2024 北京同舟云网络信息技术有限公司 京公网安备11010802033243号 京ICP备18003416号-3