A Survey on Stereotype Detection in Natural Language Processing
This survey addresses the emerging field of stereotype detection in NLP, which has societal implications for preventing discrimination and violence, but it is incremental as it reviews existing work rather than introducing new methods.
The paper surveyed existing research on stereotype detection in NLP, analyzing definitions from multiple disciplines and identifying key trends, methodologies, and challenges, with findings emphasizing its potential as an early-monitoring tool to prevent bias escalation and hate speech.
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.