Conference Paper

#59 

@inproceedings{Hoque2022, title={DramatVis Personae: Visual Text Analytics for identifying Social Biases in Creative Writing}, author={Md Naimul Hoque and Bhavya Ghai and Niklas Elmqvist}, url={https://users.umiacs.umd.edu/~elm/projects/dvp/dvp.pdf}, year={2022}, date={2022-05-10}, journal={Proceedings of the ACM Conference on Designing Interactive Systems}, booktitle={Proceedings of the ACM Conference on Designing Interactive Systems}, publisher={ACM}, address={New York, NY, USA}, abstract={Implicit biases and stereotypes are often pervasive in different 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 different intersectional social identities are represented in the story. To evaluate DVP, we first 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 identification tasks. We then conducted a follow-up user study with 11 writers and found that participants could answer questions related to bias detection more efficiently using DVP in comparison to a simple text editor. }, keywords={}, }