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Improving fake news classification using dependency grammar

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  • Kitti Nagy
  • Jozef Kapusta

Abstract

Fake news is a complex problem that leads to different approaches used to identify them. In our paper, we focus on identifying fake news using its content. The used dataset containing fake and real news was pre-processed using syntactic analysis. Dependency grammar methods were used for the sentences of the dataset and based on them the importance of each word within the sentence was determined. This information about the importance of words in sentences was utilized to create the input vectors for classifications. The paper aims to find out whether it is possible to use the dependency grammar to improve the classification of fake news. We compared these methods with the TfIdf method. The results show that it is possible to use the dependency grammar information with acceptable accuracy for the classification of fake news. An important finding is that the dependency grammar can improve existing techniques. We have improved the traditional TfIdf technique in our experiment.

Suggested Citation

  • Kitti Nagy & Jozef Kapusta, 2021. "Improving fake news classification using dependency grammar," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-22, September.
    • Handle: RePEc:plo:pone00:0256940
    DOI: 10.1371/journal.pone.0256940
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    References listed on IDEAS

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    4. Kai Shu & Deepak Mahudeswaran & Huan Liu, 2019. "FakeNewsTracker: a tool for fake news collection, detection, and visualization," Computational and Mathematical Organization Theory, Springer, vol. 25(1), pages 60-71, March.
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