Using Adversarial Learning and Biterm Topic Model for an Effective Fake News Video Detection System on Heterogeneous Topics and Short Texts

Abstract

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. However, directly applying conventional topic models (e.g. LDA and PLSA) on such short texts may not work well. Therefore, we use BTM (Biterm Topic Model) that is robust even in short texts to estimate topic distribution. 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 shifts among various topics. In this study, it achieves a 3.41%p greater F1-score than previous models used for fake news video detection.

Keywords

• Fake news video detection
• topic modeling
• adversarial neural network
• YouTube