FAKE NEWS DETECTION USING MACHINE LEARNING TECHNIQUES
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Date
2018
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UMT, Lahore
Abstract
With the expansion of social media, fake news detection topic gains a lot of popularity for the researchers in the world. The fabricated and false information is spread on the online network to manipulate the views of the people. Using misleading words, individuals can get contaminated by the fake news effectively and can share them without verification. The widespread of false information in current years increases great concerns. Fake news increased significant consideration in the 2016 United States Presidential Elections. To eliminate the bad impact of fake news, it is necessary to make a plan or system to stop such kinds of misinformation on the online networks.
In our research, we purpose a systematic identification of fake news using machine learning techniques. We obtain fake news data from Kaggle and real news data from popular news agencies websites. We implement and compare results of six different machine learning algorithms and two different feature extraction techniques. We extract the sentiment features form the dataset and find a correlation in all sentiments of each news. Our research results find that support vector machine classifier is the best classifier, on the basis of obtain accuracy is 93% and F1 score is 94%. The results show that TF-IDF is the good technique for feature extraction from text.