Comparing Naturalistic Mental Health Expressions on Student Loan Debts Using Reddit and Twitter.

Purpose: The primary objective of this study was to identify patterns in users' naturalistic expressions on student loans on two social media platforms. The secondary objective was to examine how these patterns, sentiments, and emotions associated with student loans differ in user posts indicating mental illness.

Material And Method: Data for this study were collected from Reddit and Twitter (2009-2020,  = 85,664) using certain key terms of student loans along with first-person pronouns as a triangulating measure of posts by individuals.

Drug Abuse Ontology to Harness Web-Based Data for Substance Use Epidemiology Research: Ontology Development Study.

Background: Web-based resources and social media platforms play an increasingly important role in health-related knowledge and experience sharing. There is a growing interest in the use of these novel data sources for epidemiological surveillance of substance use behaviors and trends.

Objective: The key aims were to describe the development and application of the drug abuse ontology (DAO) as a framework for analyzing web-based and social media data to inform public health and substance use research in the following areas: determining user knowledge, attitudes, and behaviors related to nonmedical use of buprenorphine and illicitly manufactured opioids through the analysis of web forum data Prescription Drug Abuse Online Surveillance; analyzing patterns and trends of cannabis product use in the context of evolving cannabis legalization policies in the United States through analysis of Twitter and web forum data (eDrugTrends); assessing trends in the availability of novel synthetic opioids through the analysis of cryptomarket data (eDarkTrends); and analyzing COVID-19 pandemic trends in social media data related to 13 states in the United States as per Mental Health America reports.

Knowledge-Driven Drug-Use NamedEntity Recognition with Distant Supervision.

As Named Entity Recognition (NER) has been essential in identifying critical elements of unstructured content, generic NER tools remain limited in recognizing entities specific to a domain, such as drug use and public health. For such high-impact areas, accurately capturing relevant entities at a more granular level is critical, as this information influences real-world processes. On the other hand, training NER models for a specific domain without handcrafted features requires an extensive amount of labeled data, which is expensive in human effort and time.

Knowledge-Infused Abstractive Summarization of Clinical Diagnostic Interviews: Framework Development Study.

Background: In clinical diagnostic interviews, mental health professionals (MHPs) implement a care practice that involves asking open questions (eg, "What do you want from your life?" "What have you tried before to bring change in your life?") while listening empathetically to patients. During these interviews, MHPs attempted to build a trusting human-centered relationship while collecting data necessary for professional medical and psychiatric care. Often, because of the social stigma of mental health disorders, patient discomfort in discussing their presenting problem may add additional complexities and nuances to the language they use, that is, hidden signals among noisy content.