Fake News Detection on Social Networks: A Survey
Source abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Misinformation and Its Impacts · Spam and Phishing Detection · Advanced Malware Detection Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Information Engineering, Institute of Disaster Prevention, Beijing 101601, China
College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
Funding
The Fundamental Research Funds for the Central Universities · The Natural Science Project of Xinjiang University Scientific Research Program
References (92)
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