Hitchhiker’s Guide to Temporal Analysis: Modeling, Causality, and Visualization for User Interaction Data

The exponential growth of time-stamped user interaction data offers immense opportunities, but also poses a key challenge: how can we move beyond descriptive analytics to uncover meaningful temporal patterns and causal relationships in user behavior? Our half-day, hands-on tutorial, “Hitchhiker’s Guide to Temporal Analysis: Modeling, Causality, and Visualization for User Interaction Data” provides a comprehensive toolkit that bridges conceptual foundations with practical skills in modeling, causality, and visualization. This tutorial is designed for researchers, graduate students, and industry practitioners with basic Python knowledge who aim to leverage user logs effectively. Attendees will learn to select and apply a range of temporal modeling techniques, including methods for causality analysis, to identify event influences and build predictive models. These methods will be complemented by best practices in temporal visualization, where participants explore visual encodings and interaction techniques through real-world case studies in user data visualization. By the end of the tutorial, participants will have developed practical skills to analyze and interpret complex time-oriented datasets. They will also gain access to a public GitHub repository containing reusable code, datasets, and visualization templates to support their future research.