Current applications and future directions in natural language processing for news media and mental health

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.