Twitter has provided people with an effective way to communicate and interact with each other. It is an undisputable fact that people’s influence plays an important role in disseminating information over the Twitter social network. Although a number of research work on finding influential users have been reported in the literature, they never really seek to distinguish and analyze different influence roles, which are of great value for various marketing purposes. In this chapter, we move a step forward to further detect five recognized influence roles of Twitter users with regard to a particular topic. By exploring three views of features related to topic, sentiment and popularity respectively, we propose a novel constrained multi-view influence role clustering approach to group potential influential Twitter users into five categories. Experimental results demonstrate the effectiveness of the proposed approach.
|Title of host publication||Social Media Content Analysis|
|Subtitle of host publication||Natural Language Processing and Beyond|
|Publisher||World Scientific Publishing Co. Pte. Ltd.|
|Number of pages||16|
|Publication status||Published - 1 Jan 2017|
ASJC Scopus subject areas
- Computer Science(all)