Finding Informative Comments for Video Viewing

Abstract

Video is an increasingly important method of information-sharing on the Web. Services such as YouTube, Vimeo, and Liveleak are platforms that support uploading User-Generated Content. Users tend to seek related information during or after watching an informative video by finding and reading comments on Web services. However, existing services only support sorting by recentness (newest) or rating (LIKES score), as opposed to related information. We suggest a novel method to find informative comments by considering original content and its relevance. We conducted a qualitative study of participants watching informative videos and analyzed how users interacted with the comments, the feature preferences, and the criteria for evaluation. We developed methods to measure informativeness priority, the user-provided level of information, classify intention of information posted, and cluster duplicate themes. Analysis of 1,861 TED talk videos and 380,619 comments show suggested methods can find more informative comments compared to existing methods (LIKES).