Towards reliable online clickbait video detection: A content-agnostic approach

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 realworld 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.

A unified approach for detection of Clickbait videos on YouTube using cognitive evidences

“…From the analysis, it has been observed that random forest found to be the best classifier, with an accuracy of 91.16%. Whereas, [ 7 ] developed a novel content-agnostic scheme for effectively detecting the clickbait video by exploring and analyzing the comments from the viewers. Along with the machine learning technique, some of the researchers have also explored the deep learning methods.…”

“…The self-generated dataset[MVD] has been proposed in this paper, which further helps to explore the research in this field. Few methodologies have been proposed that aims to detect clickbait video [ 7 ]. This is an emerging field and largely unsolved problem, due to which still very few works have been reported in this area.…”

“…Few methodologies have been proposed that aims to detect clickbait video [ 7 ]. This is an emerging field and largely unsolved problem, due to which still very few works have been reported in this area.…”

“…Some of the previous research is using thumbnail image and title of the video, which are not effective in the case when the two videos [clickbait and Non-Clickbait] having the same title and may also have the same thumbnail, then in that situation, it is critical to discriminate a clickbait and a Non-Clickbait one. That’s why to tackle this problem, the speech is also analysed, which gives a significant clue for discrimination [ 7 ]. In some of the scenarios, the uploaders are not allowing the comments from the viewers, in that case, the comments part is not contributing, and we are losing some crucial features for the reliable prediction.…”

An online reinforcement learning approach to quality-cost-aware task allocation for multi-attribute social sensing

“…A recent survey of social sensing can be found in [1]. Social sensing has been widely used in environment sensing [12], traffic monitoring [22], emergence and disaster response [23], social sensor profiling [24], point-of-interest (POI) identification [25], clickbait video detection [26], and abnormal event identification [27]. Quality-cost-aware optimal task allocation in social sensing remains to be an open challenge that has not been fully addressed [3].…”