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Fake News Detection in Microblogging Through Quantifier-Guided Aggregation
- Source :
- Modeling Decisions for Artificial Intelligence ISBN: 9783030267728, MDAI
- Publication Year :
- 2019
- Publisher :
- Springer International Publishing, 2019.
-
Abstract
- Nowadays, big volumes of User-Generated Content (UGC) spread across various kinds of social media. In microblogging, UCG can be generated in the form of ‘newsworthy’ posts, i.e., related to information that has a public utility for the people. In this context, being the UGC diffused without almost any traditional form of trusted external control, the possibility of incurring in possible fake news is far from remote. For this reason, several approaches for fake news detection in microblogging have been proposed upto now, mostly based on machine learning techniques. In this paper, an ongoing work based on the use of the Multi-Criteria Decision Making (MCDM) paradigm to detect fake news is proposed. The aim is to reduce data dependency in building the model, and to have flexible control over the choices behind the fake news detection process.
- Subjects :
- Process (engineering)
Microblogging
Computer science
Credibility
INF/01 - INFORMATICA
User-generated content
020206 networking & telecommunications
Context (language use)
Aggregation operator
02 engineering and technology
Fake new
Multi-Criteria Decision Making
Multiple-criteria decision analysis
Social media
World Wide Web
Data dependency
0202 electrical engineering, electronic engineering, information engineering
User-Generated Content
020201 artificial intelligence & image processing
Subjects
Details
- ISBN :
- 978-3-030-26772-8
- ISBNs :
- 9783030267728
- Database :
- OpenAIRE
- Journal :
- Modeling Decisions for Artificial Intelligence ISBN: 9783030267728, MDAI
- Accession number :
- edsair.doi.dedup.....3e95d0999ed40f98496e70ca492008e7