Integrating Machine Learning Techniques in Semantic Fake News Detection

The nuances of languages, as well as the varying degrees of truth observed in news items, make fake news detection a difficult problem to solve. A news item is never launched without a purpose, therefore in order to understand its motivation it is best to analyze the relations between the speaker an...

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Bibliographic Details
Published in:Neural processing letters 2021-10, Vol.53 (5), p.3055-3072
Main Authors: Braşoveanu, Adrian M. P., Andonie, Răzvan
Format: Article
Language:English
Subjects:
Artificial Intelligence
Classification
Complex Systems
Computational Intelligence
Computer Science
Credibility
Credit scoring
Datasets
Graphs
Knowledge representation
Language
Machine learning
Natural language processing
News
Propaganda
Query expansion
Semantics
Sentiment analysis
Social networks
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Summary:The nuances of languages, as well as the varying degrees of truth observed in news items, make fake news detection a difficult problem to solve. A news item is never launched without a purpose, therefore in order to understand its motivation it is best to analyze the relations between the speaker and its subject, as well as different credibility metrics. Inferring details about the various actors involved in a news item is a problem that requires a hybrid approach that mixes machine learning, semantics and natural language processing. This article discusses a semantic fake news detection method built around relational features like sentiment, entities or facts extracted directly from text. Our experiments are focused on short texts with different degrees of truth and show that adding semantic features improves accuracy significantly.
ISSN:1370-4621
1573-773X
DOI:10.1007/s11063-020-10365-x