2010 PredictingthePopularityofOnline

• (Szabo & Huberman, 2010) ⇒ Gabor Szabo, and Bernardo A. Huberman. (2010). “Predicting the Popularity of Online Content.” In: Communications of the ACM Journal, 53(8). doi:10.1145/1787234.1787254

Subject Headings: Online Content Popularity Prediction, User Interest Prediction.

Cited By

• http://scholar.google.com/scholar?q=%222010%22+Predicting+the+Popularity+of+Online+Content
• http://dl.acm.org/citation.cfm?id=1787234.1787254&preflayout=flat#citedby

Quotes

Abstract

Early patterns of Digg diggs and YouTube views reflect long-term user interest.

Body

The ease of producing online content highlights the problem of predicting how much attention any of it will ultimately receive. Research shows that user attention[9] is allocated in a rather asymmetric way, with most content getting only some views and downloads, whereas a few receive the most attention. While it is possible to predict the distribution of attention over many items, it is notably difficult to predict the amount that will be devoted over time to any given item. We solve this problem here, illustrating our approach with data collected from the portals Digg (http://digg.com) and YouTube (http://youtube.com), two well-known examples of popular content-sharing-and-filtering services.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2010 PredictingthePopularityofOnline	Bernardo A. Huberman Gabor Szabo			Predicting the Popularity of Online Content				10.1145/1787234.1787254		2010