Constructing Topic Hierarchies from Social Media Data

Abstract

Constructing topic hierarchies from the data automatically can help us better understand the contents and structure of information and benefit many applications in security informatics. The existing topic hierarchy construction methods either need to specify the structure manually, or are not robust enough for sparse and noisy social media data such as microblog. In this paper, we propose an approach to automatically construct topic hierarchies from microblog data in a bottom up manner. We detect topics first and then build the topic structure based on a tree combination method. We conduct a preliminary empirical study based on the Weibo data. The experimental results show that the topic hierarchies generated by our method provide meaningful results.