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. 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 2.68%p greater F1-score than previous models used for fake news video detection.
Highlights
Several studies have been conducted on detecting fake news videos uploaded on various video-sharing platforms such as YouTube
Fake news video detection methods should extract the features of fake news without relying on particular topics to find any such videos with a new topic
When the adversarial neural network was added to FANVMBase the Label method improved the F1-score by 2.01%p while the Distribution method improved the F1score by 2.88%p indicating that the extraction of topicagnostic features based on the topic distribution and the adversarial neural network is more effective in detecting fake news videos
Summary
Several studies have been conducted on detecting fake news videos uploaded on various video-sharing platforms such as YouTube. The topics of fake news videos characteristically change very quickly because their contents involve current societal issues. Conventional fake news detection studies that employ machine learning focus on the training data. Fake news training data are highly volatile compared to those of other research areas. If a fake news detection model is trained using the training data on fake news disseminated during the U.S presidential election, it will not effectively detect fake news about COVID-19. Fake news video detection methods should extract the features of fake news without relying on particular topics to find any such videos with a new topic. Fake news video detection methods should determine whether the topics in the same video are consistent
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