Social Media Predictive Analytics: Methods and Applications

Date

March 20, 2015

Speaker

Svitlana Volkova

Affiliation

Johns Hopkins University

Overview

Large-scale real-time social media analytics provides a novel set of conditions for the construction of predictive models. With individual users as training and test instances, their associated content (“lexical features”) and context (“network features”) are made available incrementally over time, as they converse over discussion forums. We propose various approaches to handling this dynamic data for predicting latent user properties, from traditional batch training and testing, to incremental bootstrapping, and then active learning via interactive rationale crowdsourcing.

We also study the relationships between a variety of predicted user properties, opinions and emotions on a large sample of users in online social network. We first correlate user demographics and personality with the emotional profile emanating from user tweets. We then analyze the relationships between predicted user properties and user-environment emotional contrast estimated over various neighborhoods including friends, retweeted and mentioned users. Finally, we analyze and compare predictive power of latent user properties, emotions and interests for automatically inferring showing off and self-promoting behaviors projected in online social networks.

Speakers

Svitlana Volkova

Svitlana Volkova is a PhD candidate in Computer Science at the Center for Language and Speech Processing, Johns Hopkins University. Her PhD research is focused on building text-driven predictive models for socio-linguistic content analysis in social media. She has been mainly working on online Bayesian models for streaming social media analytics, fine-grained emotion detection and sentiment analysis for under-explored languages, 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 Google Anita Borg Memorial Scholarship in 2010 and Fulbright Scholarship in 2008.