ABSTRACT
Purpose
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.
Material and Method
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
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.
Discussion and Conclusion
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.
Disclosure statement
No potential conflict of interest was reported by the authors.
Institutional review board approval
The present study was determined exempt by the Institutional Review Board of the University of Illinois at Urbana-Champaign (#21035).