ArticleScientific reports2025
Current applications and future directions in natural language processing for news media and mental health.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- The Emerging Roles of AI in Self-Directed Stress Management: Systematic Review.Journal of medical Internet research · 2026Pooled it
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mental health discourse has gained prominence in public media, significantly influencing societal perceptions. This study explores the application of Natural Language Processing (NLP) techniques to analyze the representation of mental health in news media texts. The complex interplay between media representation and public understanding of mental health requires advanced analytical tools. NLP offers promising avenues for unpacking these narratives but faces challenges in capturing the nuances of mental health discourse. We employ a scoping review to examine several NLP applications, including sentiment analysis, topic modeling, and bias detection. Our study compares news media to social media, highlighting the unique linguistic challenges of formal journalistic language. The analysis reveals significant limitations of current NLP techniques when applied to mental health news coverage. We uncover significant biases and accuracy issues in sentiment analysis of mental health content across different media platforms. Our findings underscore the need for specialized NLP techniques in mental health news analysis. We propose ten recommendations for tailored NLP approaches that provide critical insights for researchers, policymakers, and media professionals. This work aims to improve mental health communication strategies and promote more nuanced, effective public discourse and media coverage.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.