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

ABSTRACT

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 ps < .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.

KEYWORDS:

• Substance use disorder treatment
• dropout
• social media language
• machine learning
• automated text analysis

Acknowledgement

The corresponding author, Dr. Brenda Curtis, had full access to all the data in the study and took responsibility for the integrity of the data and the accuracy of the data analysis.

Author contributions

Tingting Liu: Methodology; Data analysis; Visualization; Literature review; Manuscript draft preparation, editing, and reviewing; Salvatore Giorgi: Conceptualization; Methodology; Data analysis; Manuscript editing and reviewing; Kenna Yadeta: Literature review; Manuscript draft preparation and editing; H. Andrew Schwartz: Conceptualization; Project administration; Methodology; Manuscript editing and reviewing; Lyle H. Ungar: Conceptualization; Project administration; Methodology; Supervision; Manuscript editing and reviewing; Brenda Curtis: Conceptualization; Funding acquisition; Project administration; Methodology; Supervision; Manuscript editing and reviewing.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was funded by the Intramural Research Program of the National Institutes of Health (NIH), National Institute on Drug Abuse (NIDA), NIH-NIDA R01 DA039457 to Dr. Curtis at NIDA, and in part by NIH-National Institute on Alcohol Abuse and Alcoholism (NIAAA) R01 AA028032-01 to Dr. Schwartz at Stony Brook University and Dr. Ungar at University of Pennsylvania. The authors report no financial relationships with commercial interests.