Visual memes in social media

Lexing Xie, Apostol Natsev, John R. Kender, Matthew Hill, John R. Smith
2011 Proceedings of the 19th ACM international conference on Multimedia - MM '11  
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, using
more » ... more than two million video shots from several large-scale event datasets, enable us to quantify and efficiently extract several important observations: over half of the videos contain re-mixed content, 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 journalists 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.
doi:10.1145/2072298.2072307 dblp:conf/mm/XieNKHS11 fatcat:wnuekiljfnhixpxbcj32enynou