FaNDeR: Fake News Detection Model Using Media Reliability

摘要

With the development of media including newspaper written by robots and many unreliable sources, it’s getting hard to distinguish whether the news is true or not. In this paper, we shall present a novel fake news detection model, FaNDeR(Fake News Detection model using media Reliability) which can efficiently classify the level of truth for the news in the question answering system based on modified CNN deep learning model. Our model reflects the reliability of various medias by training with the input dataset which contains the truthfulness of each media as well as that of the proposition. Our model is designed for higher accuracy with media dataset in terms of data augmentation, batch size control and model modification. We shall show that our model has higher accuracy over statistical approach by reflecting the tendency of truth level for each media through the training of the dataset collected so far.