Intersectional Identities and Machine Learning: Illuminating Language Biases in Twitter Algorithms
Files
Date
2022-01-04
Authors
Contributor
Advisor
Department
Instructor
Depositor
Speaker
Researcher
Consultant
Interviewer
Narrator
Transcriber
Annotator
Journal Title
Journal ISSN
Volume Title
Publisher
Volume
Number/Issue
Starting Page
Ending Page
Alternative Title
Abstract
Intersectional analysis of social media data is rare. Social media data is ripe for identity and intersectionality analysis with wide accessibility and easy to parse text data yet provides a host of its own methodological challenges regarding the identification of identities. We aggregate Twitter data that was annotated by crowdsourcing for tags of “abusive,” “hateful,” or “spam” language. Using natural language prediction models, we predict the tweeter’s race and gender and investigate whether these tags for abuse, hate, and spam have a meaningful relationship with the gendered and racialized language predictions. Are certain gender and race groups more likely to be predicted if a tweet is labeled as abusive, hateful, or spam? The findings suggest that certain racial and intersectional groups are more likely to be associated with non-normal language identification. Language consistent with white identity is most likely to be considered within the norm and non-white racial groups are more often linked to hateful, abusive, or spam language.
Description
Keywords
Culture, Identity, and Inclusion, bias, hate speech, intersectionality, machine learning, social media
Citation
Extent
10 pages
Format
Geographic Location
Time Period
Related To
Proceedings of the 55th Hawaii International Conference on System Sciences
Related To (URI)
Table of Contents
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
Local Contexts
Collections
Email libraryada-l@lists.hawaii.edu if you need this content in ADA-compliant format.