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Mental Health–Related Behaviors and Discussions Among Young Adults: Analysis and Classification

BACKGROUND: There have been recurring reports of web-based harassment and abuse among adolescents and young adults through anonymous social networks. OBJECTIVE: This study aimed to explore discussions on the popular anonymous social network Yik Yak related to social and mental health messaging behav...

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Autores principales: Rivas, Ryan, Shahbazi, Moloud, Garett, Renee, Hristidis, Vagelis, Young, Sean
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293060/
https://www.ncbi.nlm.nih.gov/pubmed/32469317
http://dx.doi.org/10.2196/17224
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author Rivas, Ryan
Shahbazi, Moloud
Garett, Renee
Hristidis, Vagelis
Young, Sean
author_facet Rivas, Ryan
Shahbazi, Moloud
Garett, Renee
Hristidis, Vagelis
Young, Sean
author_sort Rivas, Ryan
collection PubMed
description BACKGROUND: There have been recurring reports of web-based harassment and abuse among adolescents and young adults through anonymous social networks. OBJECTIVE: This study aimed to explore discussions on the popular anonymous social network Yik Yak related to social and mental health messaging behaviors among college students, including cyberbullying, to provide insights into mental health behaviors on college campuses. METHODS: From April 6, 2016, to May 7, 2016, we collected anonymous conversations posted on Yik Yak at 19 universities in 4 different states and performed statistical analyses and text classification experiments on a subset of these messages. RESULTS: We found that prosocial messages were 5.23 times more prevalent than bullying messages. The frequency of cyberbullying messages was positively associated with messages seeking emotional help. We found significant geographic variation in the frequency of messages offering supportive vs bullying messages. Across campuses, bullying and political discussions were positively associated. We also achieved a balanced accuracy of over 0.75 for most messaging behaviors and topics with a support vector machine classifier. CONCLUSIONS: Our results show that messages containing data about students’ mental health–related attitudes and behaviors are prevalent on anonymous social networks, suggesting that these data can be mined for real-time analysis. This information can be used in education and health care services to better engage with students, provide insight into conversations that lead to cyberbullying, and reach out to students who need support.
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spelling pubmed-72930602020-06-19 Mental Health–Related Behaviors and Discussions Among Young Adults: Analysis and Classification Rivas, Ryan Shahbazi, Moloud Garett, Renee Hristidis, Vagelis Young, Sean J Med Internet Res Original Paper BACKGROUND: There have been recurring reports of web-based harassment and abuse among adolescents and young adults through anonymous social networks. OBJECTIVE: This study aimed to explore discussions on the popular anonymous social network Yik Yak related to social and mental health messaging behaviors among college students, including cyberbullying, to provide insights into mental health behaviors on college campuses. METHODS: From April 6, 2016, to May 7, 2016, we collected anonymous conversations posted on Yik Yak at 19 universities in 4 different states and performed statistical analyses and text classification experiments on a subset of these messages. RESULTS: We found that prosocial messages were 5.23 times more prevalent than bullying messages. The frequency of cyberbullying messages was positively associated with messages seeking emotional help. We found significant geographic variation in the frequency of messages offering supportive vs bullying messages. Across campuses, bullying and political discussions were positively associated. We also achieved a balanced accuracy of over 0.75 for most messaging behaviors and topics with a support vector machine classifier. CONCLUSIONS: Our results show that messages containing data about students’ mental health–related attitudes and behaviors are prevalent on anonymous social networks, suggesting that these data can be mined for real-time analysis. This information can be used in education and health care services to better engage with students, provide insight into conversations that lead to cyberbullying, and reach out to students who need support. JMIR Publications 2020-05-29 /pmc/articles/PMC7293060/ /pubmed/32469317 http://dx.doi.org/10.2196/17224 Text en ©Ryan Rivas, Moloud Shahbazi, Renee Garett, Vagelis Hristidis, Sean Young. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 29.05.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Rivas, Ryan
Shahbazi, Moloud
Garett, Renee
Hristidis, Vagelis
Young, Sean
Mental Health–Related Behaviors and Discussions Among Young Adults: Analysis and Classification
title Mental Health–Related Behaviors and Discussions Among Young Adults: Analysis and Classification
title_full Mental Health–Related Behaviors and Discussions Among Young Adults: Analysis and Classification
title_fullStr Mental Health–Related Behaviors and Discussions Among Young Adults: Analysis and Classification
title_full_unstemmed Mental Health–Related Behaviors and Discussions Among Young Adults: Analysis and Classification
title_short Mental Health–Related Behaviors and Discussions Among Young Adults: Analysis and Classification
title_sort mental health–related behaviors and discussions among young adults: analysis and classification
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7293060/
https://www.ncbi.nlm.nih.gov/pubmed/32469317
http://dx.doi.org/10.2196/17224
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