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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2020
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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 |
Sumario: | 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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