Conference proceeding
DramatVis Personae: Visual Text Analytics for Identifying Social Biases in CreativeWriting
Designing Interactive Systems Conference, pp.1260-1276
01/01/2022
DOI: 10.1145/3532106.3533526
Abstract
Implicit biases and stereotypes are often pervasive in diferent forms of creative writing such as novels, screenplays, and children's books. To understand the kind of biases writers are concerned about and how they mitigate those in their writing, we conducted formative interviews with nine writers. The interviews suggested that despite a writer's best interest, tracking and managing implicit biases such as a lack of agency, supporting or submissive roles, or harmful language for characters representing marginalized groups is challenging as the story becomes longer and complicated. Based on the interviews, we developed DramatVis Personae (DVP), a visual analytics tool that allows writers to assign social identities to characters, and evaluate how characters and diferent intersectional social identities are represented in the story. To evaluate DVP, we frst conducted think-aloud sessions with three writers and found that DVP is easy-to-use, naturally integrates into the writing process, and could potentially help writers in several critical bias identifcation tasks. We then conducted a follow-up user study with 11 writers and found that participants could answer questions related to bias detection more efciently using DVP in comparison to a simple text editor.
Details
- Title: Subtitle
- DramatVis Personae: Visual Text Analytics for Identifying Social Biases in CreativeWriting
- Creators
- Md Naimul Hoque - University of Maryland, College ParkBhavya Ghai - Stony Brook UniversityNiklas Elmqvist - University of Maryland, College Park
- Resource Type
- Conference proceeding
- Publication Details
- Designing Interactive Systems Conference, pp.1260-1276
- DOI
- 10.1145/3532106.3533526
- Publisher
- Assoc Computing Machinery
- Number of pages
- 17
- Grant note
- Doctoral Student Research Award from College of Information Studies, University of Maryland, College Park
- Language
- English
- Date published
- 01/01/2022
- Academic Unit
- Computer Science
- Record Identifier
- 9984787258902771
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