Fake News Detection Using Machine Learning
Background: Fake news detection is an interesting topic for computer scientists and social science. Because of the recent growth of the online social media fake news has great impact to the society. Twitter is one of the most popular applications that are able to deliver appealing data in timely manner. Developing a technique that can detect fake news from Twitter is becoming a necessary and challenging task.
Objective: The main objective is to detect the fake news, which is a classic text classification problem with a straight forward proposition. It is needed to build a model that can differentiate between “Real” news and “Fake” news.
Methodology: Due to the complexity of fake news detection in social media, it is evident that a feasible method must contain several aspects to accurately tackle the issue.
Result and discussion: In this research we tried to anticipate & find out the fake news that is being spread over by some pinpointed multiple sources with an intention to a line on de fame. It summarizes the accuracy achieved by each algorithm on the four considered datasets. It is evident that the maximum accuracy achieved on DS1 (ISOT Fake News Dataset) is 99%, achieved by random forest algorithm and Perez-LSVM. Linear SVM, multilayer perceptron, bagging classifiers, and boosting classifiers achieved an accuracy of 98%. The average accuracy attained by ensemble learners is 97.67% on DS1, whereas the corresponding average for individual learners is 95.25%.
Future Work: It is significant to find the accuracy of news which is available on internet. In the paper the component for recognizing fake news are discussed.
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