Statistics and Data Science Seminar
Prof. Kunpeng Zhang
UIC
A Probabilistic Graphical Model for Brand Reputation Assessment in Social Networks
Abstract: Social media has become a popular platform that connects people who share information, in particular personal opinions. Through such a fast information exchange mechanism, reputation of individuals, consumer products, or business companies can be quickly built up within a social network. Recently, applications mining social network data start emerging to find the communities sharing the same interests for marketing purposes. Knowing the reputation of social network entities, such as celebrities or business companies, can help develop better strategies for election campaigns or new product advertisements. In this work, we propose a probabilistic graphical model to collectively measure reputations of entities in social networks. By collecting and analyzing large amount of user activities on Facebook, our model can effectively and efficiently rank entities, such as presidential candidates, professional sport teams, musician bands, and companies, based on their social reputation. The proposed model produces results largely consistent with the two publicly available systems - movie ranking in Internet Movie Database and business school ranking by the US news & World Report - with the correlation coefficients of 0.75 and −0.71, respectively. In addition, I will briefly talk about other projects I am working on: (1) sentiment identification of social media data, and (2) finding target users for online brand advertising based on a large amount of user historical activities.
Wednesday January 29, 2014 at 4:00 PM in SEO 636