Visible to the public Biblio

Filters: Author is Haimson, Oliver L.  [Clear All Filters]
2017-06-27
Haimson, Oliver L., Brubaker, Jed R., Dombrowski, Lynn, Hayes, Gillian R..  2016.  Digital Footprints and Changing Networks During Online Identity Transitions. Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. :2895–2907.

Digital artifacts on social media can challenge individuals during identity transitions, particularly those who prefer to delete, separate from, or hide data that are representative of a past identity. This work investigates concerns and practices reported by transgender people who transitioned while active on Facebook. We analyze open-ended survey responses from 283 participants, highlighting types of data considered problematic when separating oneself from a past identity, and challenges and strategies people engage in when managing personal data in a networked environment. We find that people shape their digital footprints in two ways: by editing the self-presentational data that is representative of a prior identity, and by managing the configuration of people who have access to that self-presentation. We outline the challenging interplay between shifting identities, social networks, and the data that suture them together. We apply these results to a discussion of the complexities of managing and forgetting the digital past.

2017-03-07
Madaio, Michael, Chen, Shang-Tse, Haimson, Oliver L., Zhang, Wenwen, Cheng, Xiang, Hinds-Aldrich, Matthew, Chau, Duen Horng, Dilkina, Bistra.  2016.  Firebird: Predicting Fire Risk and Prioritizing Fire Inspections in Atlanta. Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. :185–194.

The Atlanta Fire Rescue Department (AFRD), like many municipal fire departments, actively works to reduce fire risk by inspecting commercial properties for potential hazards and fire code violations. However, AFRD's fire inspection practices relied on tradition and intuition, with no existing data-driven process for prioritizing fire inspections or identifying new properties requiring inspection. In collaboration with AFRD, we developed the Firebird framework to help municipal fire departments identify and prioritize commercial property fire inspections, using machine learning, geocoding, and information visualization. Firebird computes fire risk scores for over 5,000 buildings in the city, with true positive rates of up to 71% in predicting fires. It has identified 6,096 new potential commercial properties to inspect, based on AFRD's criteria for inspection. Furthermore, through an interactive map, Firebird integrates and visualizes fire incidents, property information and risk scores to help AFRD make informed decisions about fire inspections. Firebird has already begun to make positive impact at both local and national levels. It is improving AFRD's inspection processes and Atlanta residents' safety, and was highlighted by National Fire Protection Association (NFPA) as a best practice for using data to inform fire inspections.