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Using Textual Features to Predict Popular Content on Digg
Dissertations, Theses, and Student Research: Department of English
  • Paul H Miller, University of Nebraska-Lincoln
Date of this Version
4-22-2011
Comments

A THESIS Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Master of Arts, Major: English, Under the Supervision of Professor Stephen Ramsay. Lincoln, Nebraska: May, 2011
Copyright 2011 Paul H. Miller

Abstract
Over the past few years, collaborative rating sites, such as Netflix, Digg and Stumble, have become increasingly prevalent sites for users to find trending content. I used various data mining techniques to study Digg, a social news site, to examine the influence of content on popularity. What influence does content have on popularity, and what influence does content have on users’ decisions? Overwhelmingly, prior studies have consistently shown that predicting popularity based on content is difficult and maybe even inherently impossible. The same submission can have multiple outcomes and content neither determines popularity, nor individual user decisions. My results show that content does not determine popularity, but it does influence and limit these outcomes. Adviser: Stephen Ramsay
Citation Information
Paul H Miller. "Using Textual Features to Predict Popular Content on Digg" (2011)
Available at: http://works.bepress.com/paul_miller1/1/