Fake news detection using machine learning method

Computer Engineering

In the modern time the internet is ubiquitous, everyone relies on various online resources for news. Along with the increase in the use of social media platforms like Facebook, Twitter, etc. news spread rapidly among millions of users within a very short span of time. With the rising popularity of social media, people have become more aware of current events and important news, often through sources such as Twitter. One issue with these sources of news is the prevalence of false information, or fake news. Even as some social media platforms take initiative with labels or warnings, fake news continues to have dangerous consequences beyond misinformation. Fake news detection is an emerging research area which is gaining big interest. The goal of this research is to implement a highly effective method of identifying fake news spread on Twitter through the use of Machine Learning method Support Vector Machine. In this work, we propose a system for Fake news detection that uses machine learning techniques. We used term frequency inverse document frequency (TF-IDF) of bag of words and n-grams as feature extraction technique, and Support Vector Machine (SVM) as a classifier. Using tweets as Twitter dataset, the experiments are considering n-grams as features and passing their TF-IDF values to machine learning model. After tuning the model giving the best results. Also create a module which serves as an intermediate between user and Twitter.

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