Detecting Fake News with Machine Learning
Published in International Conference on Deep Learning, Artificial Intelligence and Robotics, 2019
Recommended citation: Nagender Aneja, Sandhya Aneja "Detecting Fake News with Machine Learning." International Conference on Deep Learning, Artificial Intelligence and Robotics, 2019. pp. 53--64 doi: 10.1007/978-3-030-67187-7_7 https://link.springer.com/chapter/10.1007/978-3-030-67187-7_7
(Conference Publication)
Abstract: Fake news is intentionally written to influence individuals and their belief system. Detection of fake news has become extremely important since it is impacting society and politics negatively. Most existing works have used supervised learning but given importance to the words used in the dataset. The approach may work well when the dataset is huge and covers a wide domain. However, getting the labeled dataset of fake news is a challenging problem. Additionally, the algorithms are trained after the news has already been disseminated. In contrast, this research gives importance to content-based prediction based on language statistical features. Our assumption of using language statistical features is relevant since the fake news is written to impact human psychology. A pattern in the language features can predict whether the news is fake or not. We extracted 43 features that include Parts of Speech and Sentiment Analysis and shown that AdaBoost gave accuracy and F-score close to 1 when using 43 features. Results also show that the top ten features instead of all 43 features give the accuracy of 0.85 and F-Score of 0.87.