Skip to main content

Discovering and Mitigating Social Data Bias


Abstract Exabytes of data are created online every day. This deluge of data is no more apparent than it is on social media. Naturally, finding ways to leverage this unprecedented source of human information is an active area of research. Social media platforms have become laboratories for conducting experiments about people at scales thought unimaginable only a few years ago.

Researchers and practitioners use social media to extract actionable patterns such as where aid should be distributed in a crisis. However, the validity of these patterns relies on having a representative dataset. As this dissertation shows, the data collected from social media is seldom representative of the activity of the site itself, and less so of human activity. This mea... (more)
Created Date 2017
Contributor Morstatter, Fred (Author) / Liu, Huan (Advisor) / Kambhampati, Subbarao (Committee member) / Maciejewski, Ross (Committee member) / Carley, Kathleen M (Committee member) / Arizona State University (Publisher)
Subject Artificial intelligence / Computer science / Engineering / bias / bot detection / cultural bias / data collection bias / malicious users / perceptual bias
Type Doctoral Dissertation
Extent 187 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Computer Science 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


  Full Text
6.5 MB application/pdf
Download Count: 1630

Description Dissertation/Thesis