Baitradar: A Multi-Model Clickbait Detection Algorithm Using Deep Learning

TL;DR

Baitradar is a deep learning-based multi-model algorithm that detects YouTube clickbait by analyzing various video attributes, achieving high accuracy and robustness even with missing data.

Contribution

This study introduces Baitradar, a novel multi-model deep learning approach that combines six different video attributes for accurate clickbait detection on YouTube.

Findings

Achieves 98% accuracy on test data

Operates with inference time under 2 seconds

Effective even with missing data in some attributes

Abstract

Following the rising popularity of YouTube, there is an emerging problem on this platform called clickbait, which provokes users to click on videos using attractive titles and thumbnails. As a result, users ended up watching a video that does not have the content as publicized in the title. This issue is addressed in this study by proposing an algorithm called BaitRadar, which uses a deep learning technique where six inference models are jointly consulted to make the final classification decision. These models focus on different attributes of the video, including title, comments, thumbnail, tags, video statistics and audio transcript. The final classification is attained by computing the average of multiple models to provide a robust and accurate output even in situation where there is missing data. The proposed method is tested on 1,400 YouTube videos. On average, a test accuracy of 98% is achieved with an inference time of less than 2s.