PNNL

Abstract

Student resilience and emotional well-being are essential for both academic and social development. Earlier studies on tracking students' happiness in academia showed that many of them struggle with mental health issues. For example, a 2015 study at the University of California Berkeley found that 47% of graduate students suffer from depression, following a 2005 study that showed 10% had considered suicide. This is the first large-scale study that uses signals from social media to evaluate students' emotional well-being in academia. This work presents fine-grained emotion and opinion analysis of 79,329 tweets produced by students from 44 universities. The goal of this study is to qualitatively evaluate and compare emotions and sentiments emanating from students' communications across different academic discourse types and across universities in the U.S. We first build novel predictive models to categorize academic discourse types generated by students into personal, social, and general categories. We then apply emotion and sentiment classification models to annotate each tweet with six Ekman's emotions -- joy, fear, sadness, disgust, anger, and surprise and three opinion types -- positive, negative, and neutral. We found that emotions and opinions expressed by students vary across discourse types and universities, and correlate with survey-based data on student satisfaction, happiness and stress. Moreover, our results provide novel insights on how students use social media to share academic information, emotions, and opinions that would pertain to students academic performance and emotional well-being.