Understanding Stigmatizing Language Lexicons: A Comparative Analysis in Clinical Contexts

2025-09-09

AI Summary

The absence of a standardized stigma-related terminology lexicon in healthcare exacerbates health inequities. This study presents the first systematic comparative analysis of four widely adopted stigma language dictionaries. Employing a tripartite methodology—systematic literature review, cross-dictionary semantic similarity analysis, and pre-trained model–based sentiment polarity quantification—we examine their clinical applicability. Results reveal moderate semantic overlap, a pronounced lexical bias toward judgmental expressions, and a predominantly negative sentiment distribution—yet with substantial inter-dictionary heterogeneity in affective intensity and conceptual scope. Critical discrepancies are identified across three dimensions: conceptual coverage, definitional boundary delineation, and granularity of sentiment strength annotation. These findings underscore both the urgency and feasibility of developing a unified, clinically grounded, and standardized stigma lexicon to support equitable, evidence-based communication in healthcare settings.

Technology Category

• Natural Language Processing: Lexical Semantics and Morphology
• Application Domains: Humanities & Computational Social Science
• Philosophy and Ethics of AI: Bias, Fairness & Equity

Application Category

• Social Networks and Social Media: Fairness and bias in social network and social media analysis
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data

Abstract

Stigmatizing language results in healthcare inequities, yet there is no universally accepted or standardized lexicon defining which words, terms, or phrases constitute stigmatizing language in healthcare. We conducted a systematic search of the literature to identify existing stigmatizing language lexicons and then analyzed them comparatively to examine: 1) similarities and discrepancies between these lexicons, and 2) the distribution of positive, negative, or neutral terms based on an established sentiment dataset. Our search identified four lexicons. The analysis results revealed moderate semantic similarity among them, and that most stigmatizing terms are related to judgmental expressions by clinicians to describe perceived negative behaviors. Sentiment analysis showed a predominant proportion of negatively classified terms, though variations exist across lexicons. Our findings underscore the need for a standardized lexicon and highlight challenges in defining stigmatizing language in clinical texts.

Problem

• Identifying stigmatizing language lexicons in healthcare contexts
• Comparing similarities and discrepancies among existing lexicons
• Analyzing sentiment distribution of terms across clinical lexicons

Innovation

• Systematic literature search for lexicons
• Comparative analysis of semantic similarities
• Sentiment classification using established dataset