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FakeTouch: Machine Learning Based Framework for Detecting Fake News
Abu Bakkar Siddikk, Mahmuda Rawnak Jahan, Md. Fahim Muntasir, Md. Shohel Arman, Rifat Jahan Lia, Sheikh Shah Mohammad Motiur Rahman,

Fake news is any content or information that is false and often generated to mislead its readers in believing something which is not true. Fake news has become one of major threats that can harm someone’s reputation. It often circulates wrong or made up information about various products, events, people or entity. The deliberate making of such news is escalating drastically these days. Fake news deceives us in taking wrong decisions. Therefore, Fake News Detection has attained immense deal of interest from researchers all over the world. In this chapter, a machine learning approach has been proposed named FakeTouch starting with Natural Language Processing based concept by applying text processing, cleaning and extraction techniques. This approach aim to arrange the information to be “obeyed” into each classification model for training and tuning parameters for every model to bring out the optimized and best prediction to find out the Fake news. To evaluate the proposed framework, three use cases with three different datasets has been developed during this study. The proposed framework will also help to understand what amount of data is responsible for detecting fake news, trying to stage the linguistic differences between fake and true articles providing a visualization of the results using different visualization tools. This chapter also presents a comprehensive performance evaluation to compare different well known machine learning classifiers like Support Vector Machine, Naïve Bayes Method, Decision Tree Classifier, Random Forest, Logistic Regression as well as to develop an ensemble method (Bagging & Boosting) like XGBClassifier, Bagging Classifier of different combinations of classification models to identify which will give the best optimal results for three part of datasets. As a result, it has been found that with an appropriate set of features extracted from the texts and the headlines, XGB classifier can effectively classify fake news with very high detection rate. This framework also provides a strong baseline of an intelligent anti-fake news detector.

Fake news detection, Scraping, Social media, Text classification, Comparison of algorithms, Machine learning, Natural language processing
Journal or Conference Name
Studies in Computational Intelligence
Publication Year