BBAuthors: Kafeza, Eleanna; Advisor: -; Participants: Kanavos, Andreas; Makris, Christos; Pispirigos, Georgios; Vikatos, Pantelis (2019)
The identification of social media communities has recently been of major concern, since users participating in such communities can contribute to viral marketing campaigns. In this work we focus on users’ communication considering personality as a key characteristic for identifying communicative networks i.e. networks with high information flows. We describe the Twitter Personality based Communicative Communities Extraction (T-PCCE) system that identifies the most communicative 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 behaviour u... 
