Alessandra Teresa Cignarella, Anastasia Giachanou, Els Lefever
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.
| Subjects: | Computation and Language (cs.CL); Computers and Society (cs.CY) |
| Cite as: | arXiv:2505.17642 [cs.CL] |
| (or arXiv:2505.17642v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2505.17642Focus to learn more |
Submission history
From: Alessandra Teresa Cignarella [view email]
[v1] Fri, 23 May 2025 09:03:56 UTC (410 KB)
Here is an evaluation of the provided abstract on stereotype detection in NLP:
Strengths:
- Clear Societal Importance:
The abstract effectively emphasizes the societal risks of stereotypes, linking them to discrimination and violence, which underscores the relevance of this NLP research domain. - Research Gap Identification:
It distinguishes stereotype detection from the more mature areas of gender bias and hate speech detection, establishing the novelty and emerging nature of the field. - Multidisciplinary Approach:
Mentioning the analysis of stereotype definitions from psychology, sociology, and philosophy reflects a solid interdisciplinary foundation, which is important for understanding such a complex social phenomenon. - Large-Scale Literature Review:
The use of a semi-automatic review on over 6,000 papers spanning 25 years shows methodological rigor and breadth, likely yielding a comprehensive overview of the landscape. - Key Findings and Future Directions:
The abstract highlights stereotype detection as an early-monitoring tool that can prevent escalation of bias to hate speech and calls for broader multilingual, intersectional approaches in future NLP research, indicating forward-looking insights.
Areas for Improvement:
- Language Precision and Flow:
The sentence “In this work is presented a survey of existing research…” is grammatically awkward; rephrasing to “This work presents a survey…” would improve readability. Similarly, some sentences could be streamlined for clarity. - Details on Review Process:
The term “semi-automatic literature review” is used but without explanation. Briefly clarifying what this entails would help readers understand the review methodology. - Specific Outcome Details:
The abstract could be strengthened by including brief mention of major trends, dominant methodologies, or key challenges identified in the survey to give a more substantive preview. - Scope of Survey:
Although the abstract mentions 6,000 papers, it does not clarify how many were thoroughly analyzed or on what criteria papers were included/excluded. Clarifying this would improve transparency.
Overall Impression:
This abstract provides a concise, well-motivated overview of a survey in the emerging area of stereotype detection in NLP, highlighting its social significance and grounding it in multidisciplinary theory. The research scope appears robust given the large dataset analyzed. Improved clarity in language and a few more specifics on methodologies and key survey outcomes would enhance its effectiveness and appeal.
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