Socioeconomic status (SES) is a metric used to compare a person’s social standing based on their income, level of education, and occupation. Students from low SES backgrounds are those whose parents have low income and have limited access to the resources and opportunities they need to aid their success. Researchers have studied many issues and solutions for students with low SES, and there is a lot of research going on in many fields, especially in the social sciences. Computer science, however, has not yet as a field turned its considerable potential to addressing these inequalities. Utilizing Natural Language Processing (NLP) methods and technology, our work aims to address these disparities and ways to bridge the gap. We built a simple string matching algorithm including Latent Dirichlet Allocation (LDA) topic model and Open Information Extraction (open IE) to generate relational triples that are connected to the context of the students’ challenges, and the strategies they follow to overcome them. We manually collected 16 narratives about the experiences of low SES students in higher education from a publicly accessible internet forum (Reddit) and tested our model on them. We demonstrate that our strategy is effective (from 37.50% to 80%) in gathering contextual data about low SES students, in particular, about their difficulties while in a higher educational institution and how they improve their situation. A detailed error analysis suggests that increase of data, improvement of the LDA model, and quality of triples can help get better results from our model. For the advantage of other researchers, we make our code available.