Prasanta Bhattacharya

TL;DR: An actor-oriented continuous-time model is adopted and enhanced to jointly estimate the co-evolution of the users' social network structure and their content production behavior using a Markov Chain Monte Carlo (MCMC)-based simulation approach and provides researchers and practitioners a statistically rigorous approach to analyze network effects in observational data.

TL;DR: This work jointly leverage insights from Bayesian nonparametrics and word embeddings to identify and extract sub-tasks from a given collection of ontask queries and can inform the design of the next generation of task-based search systems that leverage user’s task behavior for better support and personalization.

TL;DR: This work quantifies user search task behavior for both single- as well as multi-task search sessions and relates it to tasks and topics and analyzes user-disposition, topic and user-interest level heterogeneities that are prevalent in search task Behavior.

TL;DR: This paper explores the use of emotionintensity analysis in predicting and understanding the ingredients of happiness as expressed in text and shows that by using just the five dimensions of emotion intensity features, this model can achieve good accuracies in classifying agency and social aspects of happiness.

TL;DR: This work quantifies multi-tasking behavior of web search users and provides a method to categorize users into focused, multi-taskers or supertaskers depending on their level of task-multiplicity and shows that the search effort expended by users varies across the groups.