Towards Reliable Online Clickbait Video Detection: A Content-Agnostic Approach

TL;DR

This paper introduces OVCP, a novel content-agnostic method that detects clickbait videos by analyzing audience comments, effectively overcoming content manipulation and outperforming existing detection techniques.

Contribution

The paper presents OVCP, a new approach that detects clickbait videos without analyzing video content, relying instead on audience comments for improved robustness and accuracy.

Findings

OVCP outperforms state-of-the-art models in clickbait detection.

OVCP is robust against sophisticated content creators.

Experimental results confirm effectiveness on real-world data.

Abstract

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.