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Improving fake news classification using dependency grammar
Kitti Nagy1, Jozef Kapusta1,2
1Department of Informatics, Constantine the Philosopher University in Nitra, Nitra, Slovakia.
Plos One
|September 14, 2021
Summary
Dependency grammar improves fake news detection by analyzing word importance in sentences. This method enhances existing techniques like TfIdf for more accurate content-based classification.
Area of Science:
- Natural Language Processing
- Computational Linguistics
- Information Science
Background:
- Fake news detection is a significant challenge.
- Content-based analysis offers a promising approach.
- Existing methods require enhancement for better accuracy.
Purpose of the Study:
- To investigate the efficacy of dependency grammar in fake news classification.
- To determine if syntactic analysis can improve fake news identification.
- To compare dependency grammar methods with traditional techniques like TfIdf.
Main Methods:
- Dataset pre-processing using syntactic analysis.
- Application of dependency grammar to determine word importance within sentences.
- Creation of input vectors for classification based on word importance.
- Comparison with the TfIdf method.
Main Results:
- Dependency grammar information can be utilized for fake news classification with acceptable accuracy.
- Syntactic analysis using dependency grammar shows potential for improving fake news detection.
- The study successfully enhanced the traditional TfIdf technique.
Conclusions:
- Dependency grammar is a viable method for improving fake news classification.
- Integrating syntactic analysis offers a valuable enhancement to existing fake news detection techniques.
- Further research can explore advanced applications of dependency grammar in this domain.
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