Keywords:  Data Economy, Infrastructure,

2017

Tools and Methods for Inferring Demographic Bias in Social Media datasets

Samuel Maurer, PhD Student, College for Environmental Design, UC Berkeley

Social media posts from smartphones are an increasingly useful data source for researchers and policymakers. For example, place-based posts can help city planners assess how infrastructure or public space is being used, and help identify the needs of different communities. But it is important to know who is represented in these data streams and who may be missing. This project will develop practical tools and methods for inferring demographic biases, using rule-based algorithms to determine the neighborhoods where frequent posters live, and then compare the demographic characteristics of these places with the population at large, thereby helping identify biases in characteristics like race, income, or education.