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About Semantic Fake News Detection: A Machine Learning Perspective
This document discusses using machine learning and semantic features to improve fake news detection. It proposes extracting entities, sentiments, and relations from news articles and linking them to external knowledge bases. These semantic features would then be added to existing syntactic features and used to augment neural network classifiers for fake news. Experimental results showed semantic features improved fake news classification accuracy by 5-10% on average. The approach involves building knowledge graphs of entities from news articles to provide more context for analysis.
Detailed Information
| Author: | ['Neha Jain'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 12 |
| Language: | English |
| Format: | |
| Price: | FREE |
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