User Response-Based Fake News Detection on Social Media

Social media has been a major information sharing and communication platform for individuals and organizations on a mass scale. Its ability to engage users to react to information posted on this media in the form of like, share, and comment made it a preferable information sharing platform by many. But the contents posted on social media are not filtered, fact checked or judged by an editorial body like any traditional news platform. Therefore, individuals, institutions and communities who consume news from social media are vulnerable to misinformation by malicious authors. In this work, we are proposing an approach that detects fake news by investigating the reaction of users to a post composed by malicious authors. Using features extracted by bagof-words model and TF-IDF from text based replies (comments), and visual emotion responses in the form of categorical data, we built models that predicted news as fake or real. We have designed and conducted a series of experiments to evaluate the performance of our approach. The results show the proposed approach outperforms the baseline in all the six models. In particular, our models from random forest, logistic regression, and XGBoost algorithms produce a precision of 0.97, a recall of 0.99 and an F1 of 0.98.