Introduction
With the rise of web 2.0 there is an ever-expanding source of interesting media because of the proliferation of usergenerated content. However, mixed in with this is a large amount of noise that creates a proverbial needle in the haystack when searching for relevant content. Although there is hope that the rich network of interwoven metadata may contain enough structure to eventually help sift through this noise, currently many sites serve up only the most popular things.
Identifying only the most popular items can be useful, but doing so fails to take into account the famous long tail behavior of the web the notion that the collective effect of small, niche interests can outweigh the market share of the few blockbuster (i.e. most-popular) items thus providing only content that has mass appeal and masking the interests of the idiosyncratic many.
YouTube, for example, hosts over 40 million videos enough content to keep one occupied for more than 200 years. Are there intelligent tools to search through this information-rich environment and identify interesting and relevant content? Is there a way to identify emerging trends or hot topics in addition to indexing the long tail for content that has real value?
Crane, Riley, and Didier Sornette.
Viral, Quality, and Junk Videos on YouTube: Separating Content From Noise in an Information-Rich Environment.
In Proceedings of AAAI symposium on Social Information Processing, 2008.
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