  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 harm of these texts and explore the effectiveness of
chain-of-thought prompting to teach large language models (LLMs) to leverage
author identity context. We reveal a tendency for these models to inaccurately
flag texts authored by gender-queer individuals as harmful. Strikingly, across
all LLMs the performance is poorest for texts that show signs of being written
by individuals targeted by the featured slur (F1 <= 0.24). We highlight an
urgent need for fairness and inclusivity in content moderation systems. By
uncovering these biases, this work aims to inform the development of more
equitable content moderation practices and contribute to the creation of
inclusive online spaces for all users.
