Social networks – both online and offline – form an integral part of our modern world. Examples of every-day connections established among people are given by online platforms such as online social networks and social media (Facebook, Twitter, etc.), games and virtual worlds (including Augmented Reality and Virtual Reality game worlds), as well as physical interactions in the offline world, which can be inferred from wearable sensors, mobile phone positioning systems, etc. As a result, a massive amount of data is generated on a daily basis, providing access to a variety of information on individuals and their interactions in social settings. Moreover, the dynamics on these socio-technical systems often vary over time, thus allowing researchers to analyze and extract characteristic patterns of human behaviors and their temporal evolution. This data can be exploited to spread contents and provide recommendations, to set up personalized advertising campaigns, and to build professional relations and networks. While this rich and massive-scale data yields an unprecedented potential for the study of societal behaviors, mining and modeling such data poses great methodological and technical challenges.

The aim of this workshop is to provide a forum for presentations and discussions on the advances, challenges and future directions in the mining and modeling of human behaviors and performance in social systems. We will seek original works and contributions at the intersection of data mining, statistical inference, machine learning and network science, with an emphasis on works that are both data-driven and methodologically novel. We would like to expose researchers and practitioners to state of the art approaches to the mining and modeling of human behavior and social systems, and create an interactive environment for discussions on the future directions of this field.

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