Towards Reliable Online Clickbait Video Detection: A Content-Agnostic Approach
This addresses the issue of clickbait videos for users and platforms on video sharing sites like YouTube, offering a robust solution against sophisticated creators, though it is incremental as it builds on existing detection methods by shifting focus to comments.
The paper tackles the problem of detecting clickbait videos on online platforms by proposing a content-agnostic approach that uses audience comments instead of analyzing video content, titles, or thumbnails, and it shows significant outperformance over state-of-the-art models and human annotators on a real-world YouTube dataset.
Online video sharing platforms (e.g., YouTube, Vimeo) have become an increasingly popular paradigm for people to consume video contents. Clickbait video, whose content clearly deviates from its title/thumbnail, has emerged as a critical problem on online video sharing platforms. Current clickbait detection solutions that mainly focus on analyzing the text of the title, the image of the thumbnail, or the content of the video are shown to be suboptimal in detecting the online clickbait videos. In this paper, we develop a novel content-agnostic scheme, Online Video Clickbait Protector (OVCP), to effectively detect clickbait videos by exploring the comments from the audience who watched the video. Different from existing solutions, OVCP does not directly analyze the content of the video and its pre-click information (e.g., title and thumbnail). Therefore, it is robust against sophisticated content creators who often generate clickbait videos that can bypass the current clickbait detectors. We evaluate OVCP with a real-world dataset collected from YouTube. Experimental results demonstrate that OVCP is effective in identifying clickbait videos and significantly outperforms both state-of-the-art baseline models and human annotators.