Analysis of Mental Health During the Covid-19 Pandemic in Indonesia using Twitter Data

Fatimah, Nurriasih; Budi, Indra; Santoso, Aris Budi; Putra, Prabu Kresna

doi:10.1109/icaicta53211.2021.9640265

“…Zhang et al [35] developed a pipeline to monitor the trends of depressive users and analyzed depression levels. Fatimah et al [39] used tweets posted by Tweeters from Indonesia to detect anxiety and other psychological issues. Some works have focused on the detection of specific psychological illnesses from posted tweets such as depression detection [40], [41], and detection of post-traumatic stress disorder [42].…”

Section: Twitter Data In Psychological Health Researchmentioning

confidence: 99%

Psychological Health and Drugs: Data-Driven Discovery of Causes, Treatments, Effects, and Abuses

Alswedani¹,

Mehmood²,

Katib³

et al. 2023

Preprint

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Mental health issues can have significant impacts on individuals and communities and hence on social sustainability. There are several challenges facing mental health treatment, however, more important is to remove the root causes of mental illnesses because doing so can help prevent mental health problems from occurring or recurring. This requires a holistic approach to understanding mental health issues that are missing from the existing research. Mental health should be understood in the context of social and environmental factors. More research and awareness are needed, as well as interventions to address root causes. The effectiveness and risks of medications should also be studied. This paper proposes a big data and machine learning-based approach for the automatic discovery of parameters related to mental health from Twitter data. The parameters are discovered from three different perspectives, Drugs & Treatments, Causes & Effects, and Drug Abuse. We used Twitter to gather 1,048,575 tweets in Arabic about psychological health in Saudi Arabia. We built a big data machine learning software tool for this work. A total of 52 parameters were discovered for all three perspectives. We defined 6 macro-parameters (Diseases & Disorders, Individual Factors, Social & Economic Factors, Treatment Options, Treatment Limitations, and Drug Abuse) to aggregate related parameters. We provide a comprehensive account of mental health, causes, medicines and treatments, mental health and drug effects, and drug abuse, as seen on Twitter, discussed by the public and health professionals. Moreover, we identify their associations with different drugs. The work will open new directions for social media-based identification of drug use and abuse for mental health, as well as other micro and macro factors related to mental health. The methodology can be extended to other diseases and provides a potential for discovering evidence for forensics toxicology from social and digital media.

show abstract

“…Zhang et al [36] developed a pipeline to monitor the trends of depressive users and analyzed depression levels. Fatimah et al [40] used tweets posted by Tweeters from Indonesia to detect anxiety and other psychological issues. Some works have focused on the detection of specific psychological illnesses from posted tweets such as depression detection [41], [42], and detection of post-traumatic stress disorder [43].…”

Section: Twitter Data In Psychological Health Researchmentioning

confidence: 99%

Psychological Health and Drugs: Data-Driven Discovery of Causes, Treatments, Effects, and Abuses

Alswedani¹,

Mehmood²,

Katib³

et al. 2023

Preprint

View full text Add to dashboard Cite

Mental health issues can have significant impacts on individuals and communities and hence on social sustainability. There are several challenges facing mental health treatment, however, more important is to remove the root causes of mental illnesses because doing so can help prevent mental health problems from occurring or recurring. This requires a holistic approach to understanding mental health issues that are missing from the existing research. Mental health should be understood in the context of social and environmental factors. More research and awareness are needed, as well as interventions to address root causes. The effectiveness and risks of medications should also be studied. This paper proposes a big data and machine learning-based approach for the automatic discovery of parameters related to mental health from Twitter data. The parameters are discovered from three different perspectives, Drugs & Treatments, Causes & Effects, and Drug Abuse. We used Twitter to gather 1,048,575 tweets in Arabic about psychological health in Saudi Arabia. We built a big data machine learning software tool for this work. A total of 52 parameters were discovered for all three perspectives. We defined 6 macro-parameters (Diseases & Disorders, Individual Factors, Social & Economic Factors, Treatment Options, Treatment Limitations, and Drug Abuse) to aggregate related parameters. We provide a comprehensive account of mental health, causes, medicines and treatments, mental health and drug effects, and drug abuse, as seen on Twitter, discussed by the public and health professionals. Moreover, we identify their associations with different drugs. The work will open new directions for social media-based identification of drug use and abuse for mental health, as well as other micro and macro factors related to mental health. The methodology can be extended to other diseases and provides a potential for discovering evidence for forensics toxicology from social and digital media.

show abstract

“…Zhang et al [ 38 ] developed a pipeline to monitor the trends of depressive users and analyzed depression levels. Fatimah et al [ 43 ] used tweets posted by Tweeters from Indonesia to detect anxiety and other psychological issues. Some works have focused on the detection of specific psychological illnesses from posted tweets such as depression detection [ 44 , 45 ] and detection of post-traumatic stress disorder [ 46 ].…”

Section: State-of-the-artmentioning

confidence: 99%

Psychological Health and Drugs: Data-Driven Discovery of Causes, Treatments, Effects, and Abuses

Alswedani

¹

,

Mehmood

²

,

Katib

³

et al. 2023

Toxics

View full text Add to dashboard Cite

Mental health issues can have significant impacts on individuals and communities and hence on social sustainability. There are several challenges facing mental health treatment; however, more important is to remove the root causes of mental illnesses because doing so can help prevent mental health problems from occurring or recurring. This requires a holistic approach to understanding mental health issues that are missing from the existing research. Mental health should be understood in the context of social and environmental factors. More research and awareness are needed, as well as interventions to address root causes. The effectiveness and risks of medications should also be studied. This paper proposes a big data and machine learning-based approach for the automatic discovery of parameters related to mental health from Twitter data. The parameters are discovered from three different perspectives: Drugs and Treatments, Causes and Effects, and Drug Abuse. We used Twitter to gather 1,048,575 tweets in Arabic about psychological health in Saudi Arabia. We built a big data machine learning software tool for this work. A total of 52 parameters were discovered for all three perspectives. We defined six macro-parameters (Diseases and Disorders, Individual Factors, Social and Economic Factors, Treatment Options, Treatment Limitations, and Drug Abuse) to aggregate related parameters. We provide a comprehensive account of mental health, causes, medicines and treatments, mental health and drug effects, and drug abuse, as seen on Twitter, discussed by the public and health professionals. Moreover, we identify their associations with different drugs. The work will open new directions for a social media-based identification of drug use and abuse for mental health, as well as other micro and macro factors related to mental health. The methodology can be extended to other diseases and provides a potential for discovering evidence for forensics toxicology from social and digital media.

show abstract

Analysis of Mental Health During the Covid-19 Pandemic in Indonesia using Twitter Data

Cited by 4 publications

References 19 publications

Psychological Health and Drugs: Data-Driven Discovery of Causes, Treatments, Effects, and Abuses

Psychological Health and Drugs: Data-Driven Discovery of Causes, Treatments, Effects, and Abuses

Psychological Health and Drugs: Data-Driven Discovery of Causes, Treatments, Effects, and Abuses

Psychological Health and Drugs: Data-Driven Discovery of Causes, Treatments, Effects, and Abuses

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