Tracking Visual Memes in Rich-Media Social Communities

Abstract

We propose tools and methods to track visual memes on community-centric rich-media repositories, such as YouTube. Visual memes refer to frequently reposted short video segments. Our method can be used to monitor the reflections of real-world events in rich media, including images and videos. We first design a large-scale event-based social video collection system to continuously monitor events that unfold in real-time. We design a scalable detection algorithms that can detect visual memes with over 96% precision and 80% recall. Visual memes are used for various analysis such as tracking the fraction of original content, extracting the iconic picture of an event, inferring influential users in the community, and so on. We present example observations on several real-world video collections from YouTube, containing up to 1.2 million video shots, including a compact taxonomy of authors into “traditional news media”, “citizen buzz leaders”, and “mavens”.