  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.

Social and Information Networks;Information Retrieval