The proliferation of cultural data has given data-driven approaches a significant edge in modeling various cultural phenomena. This workshop focuses on such approaches that make use of mathematical tools in machine learning, data mining, network science, and computational social science. We are particularly interested in presenting methods, both normative and descriptive, that offer a gestalt or structure-first approach to culture analysis and that provide a multi-layered summarization of these phenomena suitable for exploration at multiple scales. These models are applied to various datasets such as social and information networks, social media, narrative and story detection in texts, group dynamics or behavior, and collaboration and competition leading to emergent behavior.
This workshop will include a poster session; a request for posters will be sent to registered participants in advance of the workshop.
Edoardo Airoldi (Harvard University)
Tina Eliassi-Rad, Chair (Northeastern University)
Eitan Hersh (Yale University)
Jure Leskovec (Stanford University)
Johan Ugander (Stanford University)