Prasanta Bhattacharya

Characterizing Users' Multi-Tasking Behavior in Web Search

Multi-tasking within a single online search sessions is an increasingly popular phenomenon. In this work, we quantify multi-tasking behavior of web search users. Using insights from large-scale search logs, we seek to characterize user groups and search sessions with a focus on multi-task sessions. Our findings show that dual-task sessions are more prevalent than single-task sessions in online search, and that over 50\% of search sessions have more than 2 tasks. Further, we provide a method to categorize users into focused, multi-taskers or supertaskers depending on their level of task-multiplicity and show that the search effort expended by these users varies across the groups. The findings from this analysis provide useful insights about task-multiplicity in an online search environment and hold potential value for search engines that wish to personalize and support search experiences of users based on their task behavior.

Uncovering Task Based Behavioral Heterogeneities in Online Search Behavior

While a major share of prior work have considered search sessions as the focal unit of analysis for seeking behavioral insights, search tasks are emerging as a competing perspective in this space. In the current work, we quantify user search task behavior for both single- as well as multi-task search sessions and relate it to tasks and topics. Specifically, we analyze user-disposition, topic and user-interest level heterogeneities that are prevalent in search task behavior. Our results show that while search multi-tasking is a common phenomenon among the search engine users, the extent and choice of multi-tasking topics vary significantly across users. We find that not only do users have varying propensities to multi-task, they also search for distinct topics across single-task and multi-task sessions. To our knowledge, this is among the first studies to fully characterize online search tasks with a focus on user- and topic-level differences that are observable from search sessions.

Deconstructing Complex Search Tasks: a Bayesian Nonparametric Approach for Extracting Sub-tasks

Search tasks, comprising a series of search queries serving a common informational need, have steadily emerged as accurate units for developing the next generation of task-aware web search systems. Most prior research in this area has focused on segmenting chronologically ordered search queries into higher level tasks. A more naturalistic viewpoint would involve treating query logs as convoluted structures of tasks-subtasks, with complex search tasks being decomposed into more focused sub-tasks. In this work, we focus on extracting sub-tasks from a given collection of on-task search queries. We jointly leverage insights from Bayesian nonparametrics and word embeddings to identify and extract sub-tasks from a given collection of ontask queries. Our proposed model can inform the design of the next generation of task-based search systems that leverage user’s task behavior for better support and personalization.

The Information Network: Exploiting Causal Dependencies in Online Information Seeking

The Internet has emerged as a leading source of information about the world and its daily occurrences. Platforms like Wikipedia act as information conduits through which informational elements (e.g. topic pages) cater to the information seeking needs of users worldwide. While usage data from these informational elements help us to predict the information seeking behavior of users, especially in reaction to external news events, what has been largely ignored in past literature is the predictive value of the underlying informational network that connects these elements. In this study, we uncover causal linkages in information seeking behavior among related informational elements on Wikipedia. We demonstrate that incorporating this causal information leads to better predictions of page view counts of relevant Wikipedia pages, when compared to models that ignore such underlying causal linkages. We also provide additional evidence about the efficacy of our approach from the real world, by performing a judgment study with human annotators. This research is among the first to investigate and uncover the value of understanding the underlying relationships among informational elements.

Modeling the Evolution of User-generated Content on a Large Video Sharing Platform

Video sharing and entertainment websites have rapidly grown in popularity and now constitute some of the most visited websites on the Internet. Despite the high usage and user engagement, most of recent research on online media platforms have restricted themselves to networking based social media sites like Facebook or Twitter. The current study is among the first to perform a large-scale empirical study using longitudinal video upload data from one of the largest online video sites. Unlike previous studies in the online media space that have focused exclusively on demand-side research questions, we model the supply-side of the crowd contributed video ecosystem on this platform. The modeling and subsequent prediction of video uploads is made complicated by the heterogeneity of video types (e.g. popular vs. niche video genres), and the inherent time trend effects. We identify distinct genre-clusters from our dataset and employ a self-exciting Hawkes point-process model on each of these clusters to fully specify and estimate the video upload process. Our findings show that using a relatively parsimonious point-process model, we are able to achieve higher model fit, and predict video uploads to the platform with a higher accuracy than competing models.

Characterizing and Predicting Supply-side Engagement on Video Sharing Platforms Using a Hawkes Process Model

Video sharing platforms are one of the most popular and engaging platforms on the Internet today. Despite the increasing levels of user activity on these video platforms, current research on digital platforms have largely focused on social media and networking websites like Facebook and Twitter. We depart from previous work that have focused primarily on user demands (i.e. activity of viewers), and instead focus our attention to the supply-side activities on the platform (i.e. activity of video uploaders). We perform a large-scale empirical study by leveraging longitudinal video upload data from a major online video platform, demonstrating (i) heterogeneity of video types (e.g. presence of popular vs. niche genres), and (ii) inherent seasonality effects associated with video uploads. Through our analyses, we uncover a set of informative genre-clusters and estimate a self-exciting Hawkes point-process model on each of these clusters, to fully specify and estimate the video upload process. Additionally, we disentangle potential factors that govern user engagement and determine the video upload rates, which help supplement our analysis with additional explanatory power. Our results emphasize that using a parsimonious and relatively simple point-process model, we were able to obtain a high model fit, as well as perform prediction of video upload volumes with a higher accuracy than a number of competing models. The findings from this study can benefit platform owners in better understanding how their supply-side users engage with their site over time. We also offer a robust method for performing media upload prediction that is likely to be generalizable across media platforms which demonstrate similar temporal and genre-level heterogeneity.

Investigating the Effects of Self-Presentation at Online Social Network Sites and Brand Pages on Offline Purchase Behavior

The emergence and rapid growth of social media platforms, and particularly social-media based brand communities have spurred significant popular interest in recent times. However, despite the growing economic importance of brand presence on social media, little is understood about whether and how user engagement with these brand communities benefits product sales in brick-and-mortar stores - the online-to-offline (O2O) conversion problem. In this study, we combine two large real-world datasets from a popular social network site (SNS) and the loyalty card dataset from a brick-and-mortar Asian fashion retailer to study the offline purchase behavior of the SNS brand page members. We present evidence that individuals upon joining the brand page reduce their offline purchase expenditure on average. However, using a combination of text mining and statistical approaches, we show that this reduction is significantly attenuated for some individuals who self-present more than others on the SNS. The findings from our study not only illuminate our understanding of the offline economic impacts of online self-presentation, but also present newer ways of performing behavioral targeting of online users.

Project communicate: empowering children with autism and their caregivers in india

The work illustrated in this paper seeks to establish the case around children suffering from autism in developing countries. This paper proposes the design and development of a ubiquitous computing framework (codenamed Project Communicate) to provide a playful HCI model in order to help them learn and communicate effectively. The key merit of this study is in the illustration of how economical off-the-shelf technologies can be effectively integrated to achieve two key use cases in the area of autism interventions, namely Communication and Pedagogy. The proposed framework congregates some of the interventions designed and implemented by the authors in each of these test cases with a special focus on how mobile phone usage could be operationalized and leveraged in the context of a developing country like India.