Using computational methods to study social structure and behavior at scale requires researchers to make a plethora of decisions, including how to sample and preprocess data, implement algorithms, and validate results. Jana Diesner presents findings and lessons learned from her group’s work on assessing the impact of some of these choices, especially related to data provenance and selecting variables and metrics, on understanding social systems and validating social science theories in contemporary settings.
Julia Diesner highlights sources of biases and strategies for mitigating biased insights. Bringing this work into application contexts, she discusses how we leveraged computational
social science approaches to study the impact of information, science, and funding on society, and highlight some of our research in crisis informatics.
Info & contact: jennifer.dusdal@uni.lu
Registration: scisci@uni.lu