Linguistic predictors from Facebook postings of substance use disorder treatment retention versus discontinuation.

Background: Early indicators of who will remain in - or leave - treatment for substance use disorder (SUD) can drive targeted interventions to support long-term recovery. Objectives: To conduct a comprehensive study of linguistic markers of SUD treatment outcomes, the current study integrated features produced by machine learning models known to have social-psychology relevance. Methods: We extracted and analyzed linguistic features from participants' Facebook posts ( N = 206, 39.32% female; 55,415 postings) over the two years before they entered a SUD treatment program. Exploratory features produced by both Linguistic Inquiry and Word Count (LIWC) and Latent Dirichlet Allocation (LDA) topic modeling and the features from theoretical domains of religiosity, affect, and temporal orientation via established AI-based linguistic models were utilized. Results: Patients who stayed in the SUD treatment for over 90 days used more words associated with religion, positive emotions, family, affiliations, and the present, and used more first-person singular pronouns (Cohen's d values: [-0.39, -0.57]). Patients who discontinued their treatment before 90 days discussed more diverse topics, focused on the past, and used more articles (Cohen's d values: [0.44, 0.57]). All p s < .05 with Benjamini-Hochberg False Discovery Rate correction. Conclusions: We confirmed the literature on protective and risk social-psychological factors linking to SUD treatment in language analysis, showing that Facebook language before treatment entry could be used to identify the markers of SUD treatment outcomes. This reflects the importance of taking these linguistic features and markers into consideration when designing and recommending SUD treatment plans.