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A survey on fake news and rumour detection techniques
- Publication Year :
- 2019
-
Abstract
- False or unverified information spreads just like accurate information on the web, thus possibly going viral and influencing the public opinion and its decisions. Fake news and rumours represent the most popular forms of false and unverified information, respectively, and should be detected as soon as possible for avoiding their dramatic effects. The interest in effective detection techniques has been therefore growing very fast in the last years. In this paper we survey the different approaches to automatic detection of fake news and rumours proposed in the recent literature. In particular, we focus on five main aspects. First, we report and discuss the various definitions of fake news and rumours that have been considered in the literature. Second, we highlight how the collection of relevant data for performing fake news and rumours detection is problematic and we present the various approaches, which have been adopted to gather these data, as well as the publicly available datasets. Third, we describe the features that have been considered in fake news and rumour detection approaches. Fourth, we provide a comprehensive analysis on the various techniques used to perform rumour and fake news detection. Finally, we identify and discuss future directions.
- Subjects :
- Information Systems and Management
Text mining
Computer science
Rumours
02 engineering and technology
Public opinion
Classification
Data mining
Deep learning
Fake news
Machine learning
Natural language processing
Theoretical Computer Science
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
Focus (computing)
business.industry
05 social sciences
050301 education
Data science
Computer Science Applications
Control and Systems Engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
0503 education
Software
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Accession number :
- edsair.doi.dedup.....fce4f89220f08e0ad299fea1d2ac8cc1