Predicting Student Dropout Through Text and Media Content Analysis of VKontakte Profiles

This paper presents a novel approach to predicting student dropout by analyzing publicly available data from VKontakte social network profiles. Unlike traditional methods that primarily rely on academic and institutional data, our method leverages publicly available content, including photos, videos, music preferences, and textual posts. Image and video content were analyzed using scene recognition models (Places365) and contextual alignment models (CLIP), while text data were processed using the BERTopic model for topic modeling and a pre-trained model for emotion analysis. Music tracks were categorized by genre. Additionally, we developed and implemented the Weighted Neighborhood Feature Aggregation method, which incorporates weighted features from a student’s friends to make classification models graph-aware, leveraging information from the friendship graph to enrich feature representation. Using this data, a comprehensive digital student profile was constructed and utilized to predict dropout risk through machine learning techniques. The CatBoost model achieved the highest prediction accuracy, with a ROC-AUC of 0.802 and a balanced accuracy of 0.749. Key predictors included indirect indicators of academic and extracurricular engagement, the emotional tone of posts, and leisure activity preferences. The findings demonstrate that social media data can effectively aid in the early identification of students at risk of dropping out, allowing for timely interventions. While this approach depends on publicly available VK data and may be limited by privacy settings, it still provides useful insights. This approach also holds promise for applications such as student loan risk assessment.