Twitter, as one of the most popular social media platforms, provides a convenient way for people to communicate and interact with each other. It has been well recognized that influence exists during users’ interactions. Some pioneer studies on finding influential users have been reported in the literature, but they do not distinguish different influence roles, which are of great value for various marketing purposes. In this chapter, we move a step forward trying to further distinguish influence roles of Twitter users in a certain topic. By defining three views of features relating to topic, sentiment and popularity respectively, we propose a Multi-view Influence Role Clustering (MIRC) algorithm to group Twitter users into five categories.
|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||11|
|Publication status||Published - 1 Jan 2017|
ASJC Scopus subject areas
- Computer Science(all)