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  • 1Department of Informatics, Constantine the Philosopher University in Nitra, Nitra, Slovakia.

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Summary
This summary is machine-generated.

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.

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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.