Xie, Lexing; Natsev, Apostol; Kender, John R; Hill, Matthew L.; Smith, John R
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 inuence. These abstractions, using...[Show more]
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