A comparative and statistical analysis of depression classification in social media: assessing the impact of temporal boundaries and text representations

Depression is a prevalent mental health disorder that often goes undetected due to stigma and delayed clinical intervention. Social media offers a promising avenue for early detection by leveraging users’ posts, which reflect their emotional and psychological states. Most existing methods rely on datasets labeled via self-reporting, where users explicitly mention their diagnosis. This study explores whether segmenting user timelines around such self-reports–into pre- and post-report periods–can improve the classification of depressive versus control users. We evaluate multiple textual representations and assess the predictive value of different timeline segments. Experiments are conducted on several English and Spanish datasets, including one manually validated dataset that was initially labeled via self-reporting. Results indicate that temporal segmentation provides valuable insights and can enhance model performance. The proposed approach is also compared against state-of-the-art methods to assess its generalizability across languages and data conditions.