Students’ academic performance is influenced by various demographic factors, with socioeconomic class being a prominently researched and debated factor. Computer Science research traditionally prioritizes computationally definable problems, yet challenges such as the scarcity of high-quality labeled data and ethical concerns surrounding the mining of personal information can pose barriers to exploring topics like the impact of SES on students’ education. Overcoming these barriers may involve automating the collection and annotation of high-quality language data from diverse social groups through human collaboration. Therefore, our focus is on gathering unstructured narratives from Internet forums written by students with low socioeconomic status (SES) using machine learning models and human insights. We developed a hybrid data collection model that semi-automatically retrieved narratives from the Reddit website and created a dataset five times larger than the seed dataset. Additionally, we compared the performance of traditional ML models with recent large language models (LLMs) in classifying narratives written by low-SES students, and analyzed the collected data to extract valuable insights into the socioeconomic challenges these students encounter and the solutions they pursue.

Socioeconomic status (SES) reflects an individual’s standing in society, from a holistic set of factors including income, education level, and occupation. Identifying individuals in low-SES groups is crucial to ensuring they receive necessary support. However, many individuals may be hesitant to disclose their SES directly. This study introduces a federated learning-powered framework capable of verifying individuals’ SES levels through the analysis of their communications described in natural language. We propose to study language usage patterns among individuals from different SES groups using clustering and topic modeling techniques. An empirical study leveraging life narrative interviews demonstrates the effectiveness of our proposed approach.