Human-Centered data science for Crisis Informatics

When: Monday, April 3, 2017
Where: PGH 232
Time: 11:00 AM – Noon

Speaker: Marina Kogan, University of Colorado Boulder

Host: Dr. Ioannis Pavlidis

Disasters arising from natural hazards are associated with the disruption of existing social structures, but they also result in the creation of new social ties by those affected as they problem-solve alone and together. With social media now being a site for some of this interaction, there is much to learn about the nature of those changing social structures, including how and why they shift. However, the study of this social arena is challenging, because the high-tempo, high-volume convergent nature of crisis events produces vast amounts of social media data, necessitating the use of the data science methods. On the other hand, to glean meaningful insight from the crisis-related social media activity, it is necessary to use methods that account for the complex social context of the user activity, including qualitative analysis.

In this talk I will show how Human-Centered Data Science provides methodological approaches that both harness the power of computation methods and account for the highly situated nature of social media activity in disaster. Utilizing these methodological approaches, I will show how disaster-related coordination and distributed problem solving take shape on two social media platforms: Twitter and OpenStreetMap.

Bio:

Marina Kogan is a PhD candidate in Computer Science at the University of Colorado Boulder. Her research examines how people coordinate and problem-solve in crisis via social media interaction. Kogan received a Bachelor’s degree in Computer Science and another in Sociology at the City University of New York, followed by an MA in Sociology at the University of Illinois at Urbana-Champaign. With background in both computer and social science, Kogan crosses disciplinary boundaries in her work in human-centered data science, where she applies and develops methods that both harness the power of computational techniques and account for highly situated nature of the social activity in disruption events.