Gender disparities and mental health manifestations in Twitter discourse on student loan debts

We sought to demonstrate social media’s efficacy as a unique data source to gain insights into people’s perceptions and beliefs. Leveraging Twitter data, we built a model for identifying language markers of mental illness, including depression, anxiety and stress. We explored associations between student loans, gender, and mental health, and investigated gender-specific differences in emotions and sentiments to understand the emotional toll of these disparities. Findings suggest the need for targeted interventions for those experiencing the coexisting burden of student loan and mental illness. This study highlights social media’s potential for studying human emotions and informing social work practices.