Cool Blog Identification using Topic-based Models

Abstract

Among a huge number of blogs on the internet, only some of them are considered to have great contents and worth to be explored. We call such kind of blogs cool blogs and attempt to identify them. To solve the cool blog iden-tification problem, we consider three assumptions on cool blogs: (1) cool blogs tend to have definite topics, (2) cool blogs tend to have sufficient amount of blog entries, and (3) cool blogs tend to have certain levels of topic consistency among their blog entries. Corresponding to these assump-tions, we extract a mixture of topic probabilities using a topic model, exploit the number of blog entries of each blog, and calculate the topic consistency among blog entries us-ing distance functions over topic probabilities, respectively. We show the benefits of the proposed assumptions through these features. A feature unification model is also presented to achieve highest effectiveness. The experimental results on Japanese blog data show that we can improve the clas-sification results by applying proposed assumptions. 1.