Implementation and Evaluation of an Interpretable Fake News Detector

DOI

Bibliographic Information

Other Title
  • 解釈性のあるフェイクニュース検出器の実装と評価

Abstract

<p>Interpretability is an important element of fake news detection so that readers can assess the credibility of news by themselves. We implemented a naive Bayes fake news detection model proposed by Granik and Mesyur and evaluated it with the LIAR dataset in terms of recall, effect of stop words, and interpretability. The recall was affected by the imbalanced data and eliminating stop words did not improve the accuracy but slightly deteriorated it. Some high probability words were interpretable as reasons for fake news but longer phrases had better be considered as clues for fake news.</p>

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Details 詳細情報について

  • CRID
    1390282763118533120
  • NII Article ID
    130007658782
  • DOI
    10.11517/pjsai.jsai2019.0_3rin237
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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