Identifying Fake News on ISOT Data using Stemming Method with a Subdomain of AI Algorithms
Keywords:
Fake news, Social media, TF-IDF, Word2Vec, Machine learningAbstract
Nowadays, social media platforms have played a significant role in disseminating information throughout the world without any hindrance. Some people take this opportunity to propagate fake news in order to make money, by damaging the reputations of others. To tackle this issue, we proposed a methodology for detecting fake news on social media. This methodology extracts a feature of TF-IDF using N grams and Word2Vec in two ways i) with stemming method ii) without stemming method. Both of the process is performed and they fed an into supervised machine learning algorithms (ML) such as logistic regression (LR), random forest (RF), support vector machine (SVM), gradient boosting (Grad), adaptive boosting (Adaboost), and stochastic gradient descent (SGD) to detect a fake information. Evaluation shows that the unigram gives a better result with random forest when compared to the bigram and trigram. All classification algorithms were outperformed by Trigram. Unigram is more exact both with and without a stemming method. Word2vec has lower accuracy to detect fake information in the given dataset.Downloads
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