A Computational Content Analysis on Representation in Mental Health News Media with Global Perspectives

Abstract

Media representations can provide important symbolic resources in the construction of perception and agendas in diverse domains particularly concerning public health and social awareness. In view of this standpoint, dominant media frames are said to be powerful in defining social problems and shaping public discourses. The majority of the relevant literature demonstrates that representations of people with mental disorder are often negative, individuals being depicted in a passive role. A grammatical analysis of language use in press reveals different perspectives regarding the assignment of a more or less prominent role. The use of active and passive verb voice is one constituent of such analysis. Framing process constitutes another mode of analysis of news media content, which is stated to be dynamic in the concept of communication. Accordingly, this study aims to provide insights into the representation of individuals that experience mental health problems. For this purpose, newspaper stories are analyzed by examining the volume and content of a sample of selected news stories on mental illness in Türkiye for a time period of six years. Six national daily newspapers make up the sample of the study which provides a content analysis on the news media dataset. The analysis results demonstrate statistical findings on the depiction of people with mental disorder. Based on the findings and considering the sensitivity of the issue, it can be noted that more emphasis can be placed on humanizing mental illness by indicating that mental disorder is real, common and possible to be treated. In addition, coverage of other success stories with positive frames can be an alternative way to balance the multitude of news stories that link mental illness with violence or other negative elements. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.