Automatic detection of interesting moments in video has many real-world applications such as video summarization and efficient online video browsing. In this paper, we present a lightweight and scalable solution to this problem based on user mouse activity while watching video. Unlike previous approaches that analyze video content to infer the interestingness, we leverage the implicit user feedback obtained from thousands of online video watching sessions. This makes our method computationally efficient and scalable to billions of videos. Most importantly, our approach can handle a variety of video genres because we make no assumption on what constitutes interestingness: we let the crowd tell us through their mouse activity. By analyzing 106,212 user sessions collected from a popular online video website, we show that mouse activity is highly indicative of interestingness, and that our approach has competitive performance to several state-of-the-art methods.