Understanding human behavior is a significant and crucial step in solving many business challenges. Our uses of digital technologies like social media platforms, emails, cell phones, or internet searches leave massive amounts of behavioral traces. However, Big Data is not about the data. Data is now easy and cheap to obtain, but the analytics that turns these digital behavioral data into actionable insights are complex and become more sophisticated every day.

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Modeling digital behavioral data with new computational methods contribute to a better understanding of many aspects of human behaviors. For instance, we model interpersonal influences and diffusion of information in social networks. In addition, we apply text mining and natural language processing techniques of text and voice data to model personal preferences, emotions, and behavioral contents. We also work on detecting misinformation and misbehavior in social networks using deep learning methods and model spatiotemporal patterns with multi-dimensional data. Although these new methods provide crucial information on human behaviors, in the computational behavior science lab, we work at the intersection between computational and behavioral science, associating computational methods with an experimental approach to translate knowledge into tangible actions.