用6个模型联合判断视频标题和封面是否为诱导点击内容
Baitradar: A Multi-Model Clickbait Detection Algorithm Using Deep Learning
- 融合标题、评论、封面等6类信息，多模型协同判断
- 在1400个视频上达到98%准确率，单次推理小于2秒
- 适合需要快速识别虚假宣传视频的平台或研究者
随着YouTube日益流行，点击诱饵问题愈发严重：视频标题和封面吸引用户点击，但实际内容与宣传不符。本文提出BaitRadar算法，通过深度学习技术，联合六个推理模型对视频的不同属性（标题、评论、缩略图、标签、视频统计、音频转录）进行分析，最终通过平均多个模型输出实现稳健分类，即使部分数据缺失也能保持高精度。该方法在1,400个YouTube视频上测试，平均准确率达98%，单次推理时间低于2秒。
原文摘要 · Abstract (English)
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.
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