While depressive linguistic expressions have been extensively studied in traditional clinical contexts, there has been comparatively little attention devoted to modeling how both depressed and non-depressed individuals express their symptoms on social networking sites as a holistic thematic process. This study addresses this gap by examining how depression is expressed linguistically on social networking sites using various topic modeling techniques, including an innovative methodology based on LLMs. We use datasets in the Brazilian Portuguese language gathered from Instagram, Reddit, and X. Our evaluation reveals that while common themes related to depression emerge across different social networking sites, each platform's unique characteristics influence the thematic content. Reddit discussions focus on symptomatology, Instagram on travel and positive emotions, and Twitter on everyday life and media. The LLM-based approach produced more interpretable topics with a higher embedding-based coherence metric, whereas traditional methods often resulted in noisy and less internally coherent topics. This research contributes to a deeper understanding of the holistic online expressions of depression and highlights the potential of advanced topic modeling techniques to reveal subtle aspects of mental health discussions online.
Mann et al. (Wed,) studied this question.
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