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We propose visual memes, or frequently reposted short video segments, for tracking large-scale video remix in social media. Visual memes are extracted by novel and highly scalable 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, usingdoi:10.1145/2072298.2072307 dblp:conf/mm/XieNKHS11 fatcat:wnuekiljfnhixpxbcj32enynou