Using Topic Modeling and Adversarial Neural Networks for Fake News Video Detection

Fake news videos are being actively produced and uploaded on YouTube to attract public attention. In this paper, we propose a topic-agnostic fake news video detection model based on adversarial learning and topic modeling. The proposed model estimates the topic distribution of a video using its title/description and comments by topic modeling and tries to identify the differences in stance by the topic distribution difference between title/description and comments. Then, it constructs an adversarial neural network to extract topic-agnostic features effectively. The proposed model can effectively detect topic changes for stance analysis and easily shift among various topics. In this study, it achieves an F1-score 2.68% point greater than previous models in fake news video detection.