Abstract

We propose visual memes, or frequently reposted short video segments, for tracking large-scale video remix in social me-dia. Visual memes are extracted by novel and highly scal-able detection algorithms that we develop, with over 96% precision and 80 % recall. We monitor real-world events on YouTube, and we model interactions using a graph model over memes, with people and content as nodes and meme postings as links. This allows us to define several measures of influence. These abstractions, using more than two mil-lion video shots from several large-scale event datasets, en-able us to quantify and efficiently extract several important observations: over half of the videos contain re-mixed con-tent, which appears rapidly; video view counts, particularly high ones, are poorly correlated with the virality of content; the influence of traditional news media versus citizen jour-nalists varies from event to event; iconic single images of an event are easily extracted; and content that will have long lifespan can be predicted within a day after it first appears. Visual memes can be applied to a number of social media scenarios: brand monitoring, social buzz tracking, ranking content and users, among others.