Integrating Machine Learning Techniques in Semantic Fake News Detection
| Autor: | Adrian M. P. Brasoveanu, Razvan Andonie |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
0209 industrial biotechnology
Computer Networks and Communications Computer science business.industry General Neuroscience Deep learning Computational intelligence 02 engineering and technology Semantics Machine learning computer.software_genre Relationship extraction 020901 industrial engineering & automation Artificial Intelligence Order (business) Credibility Subject (grammar) 0202 electrical engineering electronic engineering information engineering Disinformation 020201 artificial intelligence & image processing Artificial intelligence business computer Software |
| Zdroj: | Neural Processing Letters. 53:3055-3072 |
| ISSN: | 1573-773X 1370-4621 |
| DOI: | 10.1007/s11063-020-10365-x |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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