Learning influence propagation of personal blogs with content and network analyses

Abstract

Weblogs (blogs) serve as a gateway to a large blog reader population, so blog authors can potentially influence a large reader population by expressing their thoughts and expertise in their blog posts. An important and complex problem, then, is figuring out why and how influence propagates through the blogosphere. While a number of previous research has looked at the network characteristics of blogs to analyze influence propagation through the blogspace, we hypothesize that a blog's influence depends on its contents as well as its network positions. Thus, in this paper, we explore two different influence propagation metrics showing different influence characteristics: Digg score and comment counts. Then, we present the results of our experiments to predict the level of influence propagation of a blog by applying machine learning algorithms to its contents and network positions. We observed over 70,000 blog posts, pruned from over 20,000,000 posts, and we found that the prediction accuracy using the content and the network features simultaneously shows the best F-score in various measures. We expect that this research result will contribute to understanding the problem of influence propagation through the blogosphere, and to developing applications for recommending influential blogs to social web users.