Comparing Naturalistic Mental Health Expressions on Student Loan Debts Using Reddit and Twitter.
| Autor: | Sinha, Gaurav R., Larrison, Christopher R., Brooks, Ian, Kursuncu, Ugur |
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| Předmět: |
SUPPORT vector machines
STATISTICS SENTIMENT analysis DEBT SOCIAL media SOCIAL networks MENTAL health SCHOLARSHIPS MACHINE learning FEAR COGNITION PATIENTS' attitudes SURVEYS SOCIOECONOMIC factors SELF-disclosure EXPERIENCE PSYCHOSOCIAL factors STUDENTS CHI-squared test QUESTIONNAIRES ANGER PROGRAMMING languages STATISTICAL models EMOTIONS PUBLIC opinion MENTAL illness SADNESS ALGORITHMS PROBABILITY theory PSYCHOLOGICAL stress |
| Zdroj: | Journal of Evidence-Based Social Work (2640-8066); Sep/Oct2023, Vol. 20 Issue 5, p727-742, 16p |
| Abstrakt: | The primary objective of this study was to identify patterns in users' naturalistic expressions on student loans on two social media platforms. The secondary objective was to examine how these patterns, sentiments, and emotions associated with student loans differ in user posts indicating mental illness. Data for this study were collected from Reddit and Twitter (2009–2020, n = 85,664) using certain key terms of student loans along with first-person pronouns as a triangulating measure of posts by individuals. Unsupervised and supervised machine learning models were used to analyze the text data. Results suggested 50 topics in reddit finance and 40 each in reddit mental health communities and Twitter. Statistically significant associations were found between mental illness statuses and sentiments and emotions. Posts expressing mental illness showed more negative sentiments and were more likely to express sadness and fear. Patterns in social media discussions indicate both academic and non-academic consequences of having student debt, including users' desire to know more about their debts. Interventions should address the skill and information gaps between what is desired by the borrowers and what is offered to them in understanding and managing their debts. Cognitive burden created by student debts manifest itself on social media and can be used as an important marker to develop a nuanced understanding of people's expressions on a variety of socioeconomic issues. Higher volumes of negative sentiments and emotions of sadness, fear, and anger warrant immediate attention of policymakers and practitioners to reduce the cognitive burden of student debts. [ABSTRACT FROM AUTHOR] |
| Databáze: | Complementary Index |
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