Exploring media portrayals of people with mental disorders using NLP

Exploring media portrayals of people with mental disorders using NLP

Media plays an important role in creating an impact in society. Several studies show that news media and entertainment channels, at times may create overwhelming images of the mental illness that emphasize criminality and dangerousness. The consequences of such negative impact may impact the audienc...

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Bibliographic Details
Main Authors: GOTTIPATI, Swapna, CHONG, Mark, LIM, Andrew Wei Kiat, KAWIDIREDJO, Benny Haryanto
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2021
Subjects:
Machine Learning
Media Portrayal
Mental Illness
NLP
Sentiment Analysis
Stigmatization
MITB student
Data Science
Health Communication
Mental and Social Health
Numerical Analysis and Scientific Computing
Online Access:https://ink.library.smu.edu.sg/sis_research/5977
https://ink.library.smu.edu.sg/context/sis_research/article/6980/viewcontent/Gottipati__S.__Chong__M.__Kiat__A._L._W.____Kawidiredjo__B._H.__2021_._Exploring_Media_Portrayals_of_People_with_Mental_Disorders_using_NLP..pdf
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Institution: Singapore Management University
Language: English
Description
Summary:Media plays an important role in creating an impact in society. Several studies show that news media and entertainment channels, at times may create overwhelming images of the mental illness that emphasize criminality and dangerousness. The consequences of such negative impact may impact the audience with stigma and on the other hand, they impair the self-esteem and help-seeking behavior of the people with mental disorders. This is the first study to examine the Singapore media’s portrayal of persons with mental disorders (MDs) using text analytics and natural language processing. To date, most studies on media portrayal of people with MDs have been conducted in developed Western countries. This study found that media articles on MDs in Singapore were largely negative in sentiment; even quotes from experts contain aspects of stigma. In addition, crime-related articles on MDs accounted for a significant portion of the corpus. Our model is also extended to detect positive health articles that discuss recovery and motivation. We further developed a stigma classifier based on the machine learning algorithms and text mining techniques. The classifier based on the XGBoosts performed best with an F1-score around 76%.

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