The second school of Social ComQuant’s summer school series focuses on methods for analyzing and modeling textual data (e.g. text mining, text classification, information extraction, sentiment analysis, latent semantic models, NLP, event extraction).

The main topic of the 1st Social ComQuant Summer School will be the analysis of behavioral digital trace data. Digital traces such as social media posts, search engine queries, mobile phone records, and co-purchases are increasingly allowing social and computational scientists the analysis of human behavior at high spatial and temporal resolution. The COVID-19 pandemic will provide the context of the summer school. Public health advice, stay-at-home orders, and self-imposed limitations have dramatically changed human behaviors on a global scale. For the first time in an epidemic, changes in human behavior have been monitored through digital platforms in real-time. Digital trace data has provided epidemiologists, public health officials, and social scientists with new tools to study the impact of interventions against the virus and how society is changing during the course of the pandemic.