A stratified learning approach for predicting the popularity of twitter idioms

Abstract

Twitter Idioms are one of the important types of hash-tags that spread in Twitter. In this paper, we propose a classifier that can stratify the Idioms from the other kind of hash-tags with 86.93% accuracy and high precision and recall rate. We then learn regression models on the stratified samples (Idioms and non-Idioms) separately to predict the popularity of the Idioms. This stratification not only itself allows us to make more accurate predictions but also makes it possible to include Idiomspecific features to separately improve the accuracy for the Idioms. Experimental results show that such stratification during the training phase followed by inclusion of Idiom-specific features leads to an overall improvement of 11.13% and 19.56% in correlation coefficient over the baseline method after the 7th and the 11th month respectively.