Data Science for Social and Behavioral Analytics(DSSBA)

In this era where mobile devices and online services occupy a major role in daily lives, more and more data are recorded about the behavior of humans and their social interactions. For instance, data are collected about interactions between social network users, and between learners using e-learning systems, about the shopping behavior of customers, and about medical pathways in hospitals.
To make sense of behavioral and social data, Behavior Analytics (BA) has emerged as a key research area in data science. The aim is to analyze data to gain a better understanding of behavior, which can support taking better decisions, but also to design machine learning models to offer tailored services to users such as personalized recommendation.  Analyzing behavioral and social data raises several challenges such as (1) designing appropriate, scalable and efficient algorithms and models for analyzing behavioral data, (2) preserving the privacy of users for behavioral analytics, (3) analyzing behavior by taking into account the cognitive and social dimensions, (4) designing intelligent systems and services that are powered by behavioral and social data models, and (5) addressing the privacy and security issues in algorithms, models, and tools for social and behavioral analytics.