arXiv:2606.01298v1 Announce Type: new Abstract: The spread of hate speech has become increasingly harmful in modern digital environments, particularly on social networking platforms. While recent advances have shown promising results in automatic hate speech detection, a key challenge remains: distinguishing genuine hate speech from reclaimed language. Accurate labeling is difficult due to the nuanced and context-dependent nature of reclaimed expressions. In this paper, we present a simple and interpretable approach for distinguishing hate speech from reclaimed language, developed for the Mult
The proliferation of digital platforms and AI's increasing role in content moderation necessitates advanced techniques to discern nuanced language use. This paper presents a timely solution to a persistent societal and technological challenge.
Accurate hate speech detection directly impacts online safety, platform governance, and the ethical deployment of AI, with significant implications for social stability and the freedom of expression.
The proposed 'simple and interpretable approach' could improve AI's ability to differentiate genuine hate speech from reclaimed language, potentially refining content moderation policies and reducing erroneous censorship.
- · Social Media Platforms
- · NLP Researchers
- · Online Communities
- · AI Ethics Advocates
- · Hate Speech Propagators
- · Ineffective Content Moderation Systems
Improved accuracy in distinguishing hate speech from reclaimed language in automated content moderation systems.
Reduced false positives in content flagging, leading to fewer bans for marginalized groups using reclaimed language.
Enhanced trust in AI-powered moderation and potentially more equitable online discourse, reducing platform liability and improving user experience.
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