Temporal issue trend identifications in blogs

Abstract

Many blog posts deal with current issues, so much attention has been paid to identifying topic trends in blogs. This paper suggests a new metric of selecting topic words. We empirically tested the accuracy and the performance of the metric with a massive blog corpus. First, we created blog site groups to their indegree influence. Second, we ran the metric with blog posts of each group. The test was encouraging because the metric identified key issues matching to the headlines of New York Times when it is applied to the top indegree blog group. We expect that this metric and the source grouping methods will be developed to a new topic analysis framework of a large blog corpus. © 2009 IEEE.