SmartBullets: A Cloud-Assisted Bullet Screen Filter based on Deep Learning
This addresses the issue of reduced enjoyment for video viewers on websites due to poor-quality comments, though it is an incremental improvement over existing filter methods.
The paper tackled the problem of low-quality real-time comments (bullet screens) in video streaming by developing SmartBullets, a deep learning-based filter that uses a convolutional neural network to classify and remove such comments, with evaluations showing it effectively improves viewer experience.
Bullet-screen is a technique that enables the website users to send real-time comment `bullet' cross the screen. Compared with the traditional review of a video, bullet-screen provides new features of feeling expression to video watching and more iterations between video viewers. However, since all the comments from the viewers are shown on the screen publicly and simultaneously, some low-quality bullets will reduce the watching enjoyment of the users. Although the bullet-screen video websites have provided filter functions based on regular expression, bad bullets can still easily pass the filter through making a small modification. In this paper, we present SmartBullets, a user-centered bullet-screen filter based on deep learning techniques. A convolutional neural network is trained as the classifier to determine whether a bullet need to be removed according to its quality. Moreover, to increase the scalability of the filter, we employ a cloud-assisted framework by developing a backend cloud server and a front-end browser extension. The evaluation of 40 volunteers shows that SmartBullets can effectively remove the low-quality bullets and improve the overall watching experience of viewers.