UMBC Information Systems Department
Predicting Demographics and Affects in Social Networks
Dr. Svitlana Volkova
Pacific Northwest National Laboratory
11am Friday, 13 May 2016, ITE 459
Social media predictive analytics bring unique opportunities to study people and their behaviors in real time, at an unprecedented scale: who they are, what they like and what they think and feel. Such large-scale real-time social media predictive analytics provide a novel set of conditions for the construction of predictive models. This talk focuses on various approaches to handling this dynamic data for predicting latent user demographics, from constrained-resource batch classification, to incremental bootstrapping, and then iterative learning via interactive rationale (feature) crowdsourcing. In addition, we present the relationships between a variety of perceived user properties e.g., income, education etc. and opinions, emotions and interests in a social network.
Svitlana Volkova received her PhD in Computer Science from Johns Hopkins University. She was affiliated with the Center for Language and Speech Processing and the Human Language Technology Center of Excellence. Her PhD research focused on building predictive models for sociolinguistic content analysis in social media. She built online models for streaming social media analytics, fine-grained emotion detection and multilingual sentiment analysis, and effective annotation techniques via crowdsourcing incorporated into the active learning framework. She interned at Microsoft Research in 2011, 2012 and 2014 at the Natural Language Processing and Machine Learning and Perception teams. She was awarded the Google Anita Borg Memorial Scholarship in 2010 and the Fulbright Scholarship in 2008.