Health is wealth: a corpus-driven analysis of the portrayal of mental health in Malaysian English online newspapers

• Main Authors: Nor Fariza Mohd Nor, Novelia Bernice Jeffree, Hilwa Abdullah@Mohd Nor
• Format: Article
• Language: English
• Published: Penerbit Universiti Kebangsaan Malaysia 2021
• Online Access: http://journalarticle.ukm.my/17255/1/47167-156854-2-PB.pdf; http://journalarticle.ukm.my/17255/; https://ejournal.ukm.my/gema/issue/view/1397
• Institution: Universiti Kebangsaan Malaysia

In view of the increase mental health cases in Malaysia, it is timely to examine how Malaysian newspapers portray mental health issues. A corpus-driven approach was employed to identify the adjective collocates for the node word mental health, determine the semantic grouping of the collocates and investigate the portrayal of mental health issues in two newspapers. A total of 585 news articles, from January 2020 to December 2020 were collected from the New Straits Times and The Star Online. The Antconc 3.5.8 software was used to generate the collocates and a discourse analysis approach was employed to examine how mental health issues were portrayed. 22 adjective collocates were identified in the New Straits Times, while 34 adjective collocates were extracted from The Star Online. 13 similar adjective collocates were found and based on the 13 adjective collocates, four semantic group of adjectives (emotive, miscellaneous, size and evaluative) were identified. A discourse analysis on the portrayal of mental health issues in the two newspapers seemed positive because the reports concentrated on the initiatives taken by the government and NGOs to handle mental health situations in Malaysia, with the aim of educating the public and raising their awareness about mental health. The present study has contributed significantly to research on mental health in the Asian context, since the review of literature revealed that research on mental health were mostly from the West. Furthermore, a corpus-driven approach which is systematic and rigorous allows for the comparison of the language across large datasets.