Depression often goes undiagnosed because of reluctance by patients to seek professional help. Individuals may seek health care information about depression online in the form of depression inventories for self-reporting, which reflect some of the symptoms listed in the manuals used by mental health practitioners to diagnose patients. Alternatively, patients may turn to online mental health communities (OMHCs) for social support, to seek advice and to engage with similar others. Four topic modeling approaches and a subsequent thematic analysis were employed on 11,975 posts from the r/depression subreddit to develop themes that represent the experiences of people with depression. The results of the topic models were used to identify twenty-three sub-themes that were categorized into four greater themes: (1) depression attributions, (2) manifestations of depression, (3) mechanisms for coping with depression, and (4) expressions of depression. The twenty-three sub-themes encompass the nuanced and diverse ways in which people communicate about depression. The use of a computational topic modeling approach and a qualitative thematic analysis approach, when applied to unstructured text data from an OMHC, led to finding symptoms from the text that mirror the symptoms outlined in established depression diagnostic tools. The criticisms of revisions made to depression inventories and standardized diagnostic tools can be addressed by fine-tuning the diagnostic criteria to consider the aspects of depression expressed by individuals experiencing depression.
Kurt Marais (2026) studied this question.