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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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:
NLP
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
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spelling sg-smu-ink.sis_research-69802021-06-07T06:30:06Z Exploring media portrayals of people with mental disorders using NLP GOTTIPATI, Swapna CHONG, Mark LIM, Andrew Wei Kiat KAWIDIREDJO, Benny Haryanto 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%. 2021-02-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/5977 info:doi/10.5220/0010380007080715 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 http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University 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
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic 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
spellingShingle 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
GOTTIPATI, Swapna
CHONG, Mark
LIM, Andrew Wei Kiat
KAWIDIREDJO, Benny Haryanto
Exploring media portrayals of people with mental disorders using NLP
description 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%.
format text
author GOTTIPATI, Swapna
CHONG, Mark
LIM, Andrew Wei Kiat
KAWIDIREDJO, Benny Haryanto
author_facet GOTTIPATI, Swapna
CHONG, Mark
LIM, Andrew Wei Kiat
KAWIDIREDJO, Benny Haryanto
author_sort GOTTIPATI, Swapna
title Exploring media portrayals of people with mental disorders using NLP
title_short Exploring media portrayals of people with mental disorders using NLP
title_full Exploring media portrayals of people with mental disorders using NLP
title_fullStr Exploring media portrayals of people with mental disorders using NLP
title_full_unstemmed Exploring media portrayals of people with mental disorders using NLP
title_sort exploring media portrayals of people with mental disorders using nlp
publisher Institutional Knowledge at Singapore Management University
publishDate 2021
url 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
_version_ 1770575712958283776