Detecting Fake News with Sentiment Analysis and Network Metadata
With the proliferation of the Internet and social media, there is now a voluminous amount of news, articles, and other text available online. These resources, while making communication and information flow easier and faster, have jeopardized the veracity of the news that is being distributed. The impact of fake news has been so deep rooted in society that it even affected the US election of 2016. This project used a combination of sentiment analysis and network metadata in a Machine Learning model to classify fake news. The study created a scraping tool to accumulate data related to the news and leveraged four different sub pipelines for feature engineering and feature extraction. The fake news model was trained on the Random Forest Classifier. Results show that the proposed model achieved a F1 score greater than 88% The study has also developed a web interface to take a URL of the news and display whether the news is fake.