Studying Social Phenomena with Digital Traces
Interactions and activities of people recorded as digital traces via digital devices or online environments offer increasingly comprehensive pictures of both individual and group-level behaviour. These traces may even allow us to describe and explain human biases like perception biases, gender bias or sexist attitudes. However, several methodological challenges arise when working with non-reactive digital trace data. In this talk, Professor Claudia Wagner will focus on some of the issues that become apparent when we aim to measure and explain social constructs like sexist attitudes based on the digital traces that humans leave online. She will present work on assessing the measurement bias of Natural Language Processing (NLP) methods for detecting sexism. In addition, Professor Wagner will show how homophily and groups size differences impact the visibility of minorities in rankings and to what extent perception biases of humans can be explained by the structure of the social network in which they are embedded.
This keynote lecture is part of the workshop Women in Computational Social Sciences. The workshop aims at fostering collaboration between female computer scientists and social scientists, making use of each other’s value domain knowledge and developing a form of multilingualism among scholars.