Abstract

Trigger Warning: Profane Language, Slurs Content moderation on social media platforms shapes the dynamics of online discourse, influencing whose voices are amplified and whose are suppressed. Recent studies have raised concerns about the fairness of content moderation practices, particularly for aggressively flagging posts from transgender and non-binary individuals as toxic. In this study, we investigate the presence of bias in harmful speech classification of gender-queer dialect online, focusing specifically on the treatment of reclaimed slurs. We introduce a novel dataset, QueerReclaimLex, based on 109 curated templates exemplifying non-derogatory uses of LGBTQ+ slurs. Dataset instances are scored by gender-queer annotators for potential harm depending on additional context about speaker identity. We systematically evaluate the performance of five off-the-shelf language models in assessing the …

Metadata

publication

Proceedings of the 4th ACM Conference on Equity and Access in Algorithms …, 2024

year

2024

publication date

2024/10/29

authors

Rebecca Dorn, Lee Kezar, Fred Morstatter, Kristina Lerman

link

https://dl.acm.org/doi/abs/10.1145/3689904.3694704

resource_link

https://dl.acm.org/doi/pdf/10.1145/3689904.3694704

book

Proceedings of the 4th ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization

pages

1-12