Fake News Detection on Social Networks: A Survey

Author:

Shen Yanping1,Liu Qingjie1,Guo Na1,Yuan Jing1,Yang Yanqing2ORCID

Affiliation:

1. School of Information Engineering, Institute of Disaster Prevention, Beijing 101601, China

2. College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China

Abstract

In recent years, social networks have developed rapidly and have become the main platform for the release and dissemination of fake news. The research on fake news detection has attracted extensive attention in the field of computer science. Fake news detection technology has made many breakthroughs recently, but many challenges remain. Although there are some review papers on fake news detection, a more detailed picture for carrying out a comprehensive review is presented in this paper. The concepts related to fake news detection, including fundamental theory, feature type, detection technique and detection approach, are introduced. Specifically, through extensive investigation and complex organization, a classification method for fake news detection is proposed. The datasets of fake news detection in different fields are also compared and analyzed. In addition, the tables and pictures summarized here help researchers easily grasp the full picture of fake news detection.

Funder

The Fundamental Research Funds for the Central Universities

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference92 articles.

1. Fake News Propagation and Detection: A Sequential Model;Papanastasiou;Manag. Sci.,2020

2. Social media and fake news in the 2016 election;Allcott;J. Econ. Perspect.,2017

3. Fake news, disinformation and misinformation in social media: A review;Amri;Soc. Netw. Anal. Min.,2023

4. Deep learning for misinformation detection on online social networks: A survey and new perspectives;Islam;Soc. Netw. Anal. Min.,2020

5. Alam, F., Cresci, S., Chakraborty, T., Silvestri, F., Dimitrov, D., Martino, G.D.S., Shaar, S., Firooz, H., and Nakov, P. (2021). A survey on multimodal disinformation detection. arXiv.

Cited by 2 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

同舟云学术

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