NSSI often portends risk for suicidal attempts; however, the mechanism explaining the association between SI and NSSI remains unclear. Social media and the recent developments in machine learning enable research on voluntary, anonymous disclosure of SI and NSSI. No prior investigation, however, attempted to assess the etiology underlying SI and NSSI with natural language processing. The present investigation uses structural topic modeling with subreddit (r/SuicideWatch: n = 8311 and r/selfharm: n = 9443) as prevalence and content covariates. We found that topics were non-significantly differentiated in either subreddit (transgender identity experience, b = 0.0008, and imminent suicide plan with time and method, b = 0.0008), but several topics were significantly differentiated between r/SuicideWatch (e.g., despair and ambivalence about death, b = 0.06, and hopelessness in experienc?ing pain, b = 0.05) and r/selfharm (e.g., hiding the wound and scars, b = -0.04, and celebration of recovery, b = -0.04). Topic content in both subreddits aligned with existing theory surrounding SI and NSSI (e.g., Third Variable Theory and Idea-to-action framework), which suggests that natural language processing is a valid and powerful approach to investigate etiology of SI and NSSI.