A Survey on Stereotype Detection in Natural Language Processing

TL;DR

This survey reviews NLP research on stereotype detection, highlighting its societal importance, current methodologies, challenges, and future directions, emphasizing the need for more inclusive and multilingual approaches.

Contribution

It provides a comprehensive overview of stereotype detection in NLP, analyzing definitions, trends, and challenges from interdisciplinary perspectives, and identifies gaps for future research.

Findings

Stereotype detection can serve as an early-monitoring tool to prevent bias escalation.

Current research emphasizes the need for broader, multilingual, and intersectional approaches.

Key challenges include dataset diversity and defining stereotypes across cultures.

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. 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.