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Social media discussions predict mental health consultations on college campuses

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Social media discussions predict mental health consultations on college campuses. / Saha, Koustuv; Yousuf, Asra; Boyd, Ryan L et al.
In: Scientific Reports, Vol. 12, No. 123, 31.01.2022, p. 1-11.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Saha, K, Yousuf, A, Boyd, RL, Pennebaker, JW & De Choudhury, M 2022, 'Social media discussions predict mental health consultations on college campuses', Scientific Reports, vol. 12, no. 123, pp. 1-11. https://doi.org/10.1038/s41598-021-03423-4

APA

Saha, K., Yousuf, A., Boyd, R. L., Pennebaker, J. W., & De Choudhury, M. (2022). Social media discussions predict mental health consultations on college campuses. Scientific Reports, 12(123), 1-11. https://doi.org/10.1038/s41598-021-03423-4

Vancouver

Saha K, Yousuf A, Boyd RL, Pennebaker JW, De Choudhury M. Social media discussions predict mental health consultations on college campuses. Scientific Reports. 2022 Jan 31;12(123):1-11. Epub 2022 Jan 7. doi: 10.1038/s41598-021-03423-4

Author

Saha, Koustuv ; Yousuf, Asra ; Boyd, Ryan L et al. / Social media discussions predict mental health consultations on college campuses. In: Scientific Reports. 2022 ; Vol. 12, No. 123. pp. 1-11.

Bibtex

@article{5b4e2d7623e74124806e795806a17d21,
title = "Social media discussions predict mental health consultations on college campuses",
abstract = "The mental health of college students is a growing concern, and gauging the mental health needs of college students is difficult to assess in real-time and in scale. While social media has shown potential as a viable “passive sensor” of mental health, the construct validity and in-practice reliability of such computational assessments remain largely unexplored. Towards this goal, we study how assessing the mental health of college students using social media data correspond with ground-truth data of on-campus mental health consultations. For a large U.S. public university, we obtained ground-truth data of on-campus mental health consultations between 2011–2016, and collected 66,000 posts from the university{\textquoteright}s Reddit community. We adopted machine learning and natural language methodologies to measure symptomatic mental health expressions of depression, anxiety, stress, suicidal ideation, and psychosis on the social media data. Seasonal auto-regressive integrated moving average (SARIMA) models of forecasting on-campus mental health consultations showed that incorporating social media data led to predictions with r=0.86 and SMAPE=13.30, outperforming models without social media data by 41%. Our language analyses revealed that social media discussions during high mental health consultations months consisted of discussions on academics and career, whereas months of low mental health consultations saliently show expressions of positive affect, collective identity, and socialization. This study reveals that social media data can improve our understanding of college students{\textquoteright} mental health, particularly their mental health treatment needs.",
keywords = "social media, language, college students, mental health, counseling services, Reddit",
author = "Koustuv Saha and Asra Yousuf and Boyd, {Ryan L} and Pennebaker, {James W.} and {De Choudhury}, Munmun",
year = "2022",
month = jan,
day = "31",
doi = "10.1038/s41598-021-03423-4",
language = "English",
volume = "12",
pages = "1--11",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "123",

}

RIS

TY - JOUR

T1 - Social media discussions predict mental health consultations on college campuses

AU - Saha, Koustuv

AU - Yousuf, Asra

AU - Boyd, Ryan L

AU - Pennebaker, James W.

AU - De Choudhury, Munmun

PY - 2022/1/31

Y1 - 2022/1/31

N2 - The mental health of college students is a growing concern, and gauging the mental health needs of college students is difficult to assess in real-time and in scale. While social media has shown potential as a viable “passive sensor” of mental health, the construct validity and in-practice reliability of such computational assessments remain largely unexplored. Towards this goal, we study how assessing the mental health of college students using social media data correspond with ground-truth data of on-campus mental health consultations. For a large U.S. public university, we obtained ground-truth data of on-campus mental health consultations between 2011–2016, and collected 66,000 posts from the university’s Reddit community. We adopted machine learning and natural language methodologies to measure symptomatic mental health expressions of depression, anxiety, stress, suicidal ideation, and psychosis on the social media data. Seasonal auto-regressive integrated moving average (SARIMA) models of forecasting on-campus mental health consultations showed that incorporating social media data led to predictions with r=0.86 and SMAPE=13.30, outperforming models without social media data by 41%. Our language analyses revealed that social media discussions during high mental health consultations months consisted of discussions on academics and career, whereas months of low mental health consultations saliently show expressions of positive affect, collective identity, and socialization. This study reveals that social media data can improve our understanding of college students’ mental health, particularly their mental health treatment needs.

AB - The mental health of college students is a growing concern, and gauging the mental health needs of college students is difficult to assess in real-time and in scale. While social media has shown potential as a viable “passive sensor” of mental health, the construct validity and in-practice reliability of such computational assessments remain largely unexplored. Towards this goal, we study how assessing the mental health of college students using social media data correspond with ground-truth data of on-campus mental health consultations. For a large U.S. public university, we obtained ground-truth data of on-campus mental health consultations between 2011–2016, and collected 66,000 posts from the university’s Reddit community. We adopted machine learning and natural language methodologies to measure symptomatic mental health expressions of depression, anxiety, stress, suicidal ideation, and psychosis on the social media data. Seasonal auto-regressive integrated moving average (SARIMA) models of forecasting on-campus mental health consultations showed that incorporating social media data led to predictions with r=0.86 and SMAPE=13.30, outperforming models without social media data by 41%. Our language analyses revealed that social media discussions during high mental health consultations months consisted of discussions on academics and career, whereas months of low mental health consultations saliently show expressions of positive affect, collective identity, and socialization. This study reveals that social media data can improve our understanding of college students’ mental health, particularly their mental health treatment needs.

KW - social media

KW - language

KW - college students

KW - mental health

KW - counseling services

KW - Reddit

U2 - 10.1038/s41598-021-03423-4

DO - 10.1038/s41598-021-03423-4

M3 - Journal article

VL - 12

SP - 1

EP - 11

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 123

ER -