T-PICE: Twitter Personality Based Influential Communities Extraction System

Abstract

The identification of influential users in social media communities has been recently of major concern, since these users can contribute to viral marketing campaigns. In our approach we extend the notion of influence from users to networks and consider personality as a key characteristic for identifying influential networks. We describe the Twitter Personality based Influential Communities Extraction (T-PICE) system that creates the best influential communities in a Twitter network graph considering users' personality. We then expand existing approaches in users' personality extraction by aggregating data that represent several aspects of user behavior using machine learning techniques. We use an existing modularity based community detection algorithm and we extend it by inserting a pre-processing step that eliminates graph edges based on users' personality. The effectiveness of our approach is demonstrated by sampling the twitter graph and comparing the influence of the created communities with and without considering the personality factor. We define several metrics to count the influence of communities. Our results show that the T-PICE system creates the most influential communities.