A Classification Method for Micro-Blog Popularity Prediction: Considering the Semantic Information
Predicting the scale and quantity of reposting in micro-blog network have significances to the future network marketing, hot topic detection and public opinion monitor. This study proposed a novel two-stage method to predict the popularity of a micro-blog prior to its release. By focusing on the text content of the specific micro-blog as well as its source of publication (user’s attributes), a special classification method—Labeled Latent Dirichlet allocation (LLDA) was trained to predict the volume range of future reposts for a new message. To the authors’ knowledge, this paper is the first research to utilize this multi-label text classifier to investigate the influence of one micro-blog’s topic on its reposting scale. The experiment was conducted on a large scale dataset, and the results show that it’s possible to estimate ranges of popularity with an overall accuracy of 72.56%.
KeywordsPopularity prediction Classification Semantic information Short contents LLDA Micro-blog
This work is supported in part by National Natural Science Foundation of China (Project No. 71701134), The Humanity and Social Science Youth Foundation of Ministry of Education of China (Project No. 16YJC630153), National Taipei University of Technology- Shenzhen University Joint Research Program (Project No. 2018003), and Natural Science Foundation of Guangdong Province of China (Project No. 2017A030310427).
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