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Exploring the Effect of N-grams with BOW and TF-IDF Representations on Detecting Fake News

Publicado en:International Conference On Data Analytics For Business And Industry (Icdabi). (741-746): - 2022-10-25 (741-746), DOI:

Autores: Amal Esmail Qasem; Mohammad Sajid

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Resumen

The Internet is used by millions of users daily, who publish news content on social media like (Twitter, Facebook, etc.). These platforms are becoming the most significant source of spreading fake news, which plays a significant issue for the individual and society. Fake news is incorrect information written to mislead readers. Fake news' text available on these platforms is unstructured and needs to be preprocessed and converted to a numerical format to be used later. Some fake news has seemed natural, making it challenging even for humans to identify them. Therefore, automated fake news detection tools leveraging machine learning methods have become an essential requirement. This paper investigates and compares two feature extraction approaches, Bag of Words (BoW) and Term Frequency-Inverse Document Frequency (TF-IDF), with N-grams, and three conventional machine classifiers, Support Vector

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