A Survey on Stereotype Detection in Natural Language Processing

Abstract

Stereotypes influence social perceptions and can escalate into discrimination and violence. While NLP research has extensively addressed gender bias and hate speech, stereotype detection remains an emerging field with significant societal implications. This work presents a survey of existing research, drawing on definitions from psychology, sociology, and philosophy. A semi-automatic literature review was conducted using Semantic Scholar, through which over 6,000 papers (published between 2000–2025) were retrieved and filtered. The analysis identifies key trends, methodologies, challenges, and future directions. The findings emphasize the potential of stereotype detection as an early-monitoring tool to prevent bias escalation and the rise of hate speech. The conclusions call for a broader, multilingual, and intersectional approach in NLP studies.