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. In this work is presented a survey of existing research, analyzing definitions from psychology, sociology, and philosophy. A semi-automatic literature review was performed by using Semantic Scholar. We retrieved and filtered over 6,000 papers (in the year range 2000-2025), identifying key trends, methodologies, challenges and future directions. The findings emphasize stereotype detection as a potential early-monitoring tool to prevent bias escalation and the rise of hate speech. Conclusions highlight the need for a broader, multilingual, and intersectional approach in NLP studies.
View on arXiv@article{cignarella2025_2505.17642,
title={ Stereotype Detection in Natural Language Processing },
author={ Alessandra Teresa Cignarella and Anastasia Giachanou and Els Lefever },
journal={arXiv preprint arXiv:2505.17642},
year={ 2025 }
}