Methodologies for Fake News Detection using Natural Language Processing and Machine Learning
Shamla Mantri, Gayatri Gattani, Sarvesh Dhapte, Yash Jain
News, Fake News identification, NLP, Machine Learning, Social media, Information legitimacy.
Having easy access to the internet and an intuitive user interface has made e-reader’s growth extremely tremendous. These led to the gradual increase of fake news activity over social media and other websites. Using NLP an expeditiously emerging method of detecting fake content with help of machine learning algorithms is done. As part of this paper, we provide a recapitulation of the methods for collecting and classifying fake news, as well as a discussion of future directions for research in this area. In this experiment, data preprocessing is a first step, where data is created and transformed in a format used to model training. That preprocessed data is then programmed for feature extraction. Next a pipeline is created for all ML algorithms namely, Naive Bayes, SVM, Logistic Regression, KNN, LGBM Classifier, Random Forest. An analysis of all algorithms' performance is conducted in a comparative study. Detailed analyses of each model are provided, with an emphasis on its performance. According to our experiment, Random Forest is an overfitted model for this purpose. With an accuracy of 99.09%, the SVM classifier performs best followed by the LGBM classifier with a 99.79% accuracy.
Article Details
Unique Paper ID: 157063

Publication Volume & Issue: Volume 9, Issue 5

Page(s): 775 - 782
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