Computational analyses identify addiction help-seeking behaviors on the social networking website Reddit: Insights into online social interactions and addiction support communities

Evaluating generative AI responses to real-world drug-related questions

Generative Artificial Intelligence (AI) systems such as OpenAI's ChatGPT, capable of an unprecedented ability to generate human-like text and converse in real time, hold potential for large-scale deployment in clinical settings such as substance use treatment. Treatment for substance use disorders (SUDs) is particularly high stakes, requiring evidence-based clinical treatment, mental health expertise, and peer support. Thus, promises of AI systems addressing deficient healthcare resources and structural bias are relevant within this domain, especially in an anonymous setting. This study explores the effectiveness of generative AI in answering real-world substance use and recovery questions. We collect questions from online recovery forums, use ChatGPT and Meta's LLaMA-2 for responses, and have SUD clinicians rate these AI responses. While clinicians rated the AI-generated responses as high quality, we discovered instances of dangerous disinformation, including disregard for suicidal ideation, incorrect emergency helplines, and endorsement of home detox. Moreover, the AI systems produced inconsistent advice depending on question phrasing. These findings indicate a risky mix of seemingly high-quality, accurate responses upon initial inspection that contain inaccurate and potentially deadly medical advice. Consequently, while generative AI shows promise, its real-world application in sensitive healthcare domains necessitates further safeguards and clinical validation.

Digital Epidemiology of Prescription Drug References on X (Formerly Twitter): Neural Network Topic Modeling and Sentiment Analysis

BACKGROUND

Data from the social media platform X (formerly Twitter) can provide insights into the types of language that are used when discussing drug use. In past research using latent Dirichlet allocation (LDA), we found that tweets containing "street names" of prescription drugs were difficult to classify due to the similarity to other colloquialisms and lack of clarity over how the terms were used. Conversely, "brand name" references were more amenable to machine-driven categorization.

OBJECTIVE

This study sought to use next-generation techniques (beyond LDA) from natural language processing to reprocess X data and automatically cluster groups of tweets into topics to differentiate between street- and brand-name data sets. We also aimed to analyze the differences in emotional valence between the 2 data sets to study the relationship between engagement on social media and sentiment.

METHODS

We used the Twitter application programming interface to collect tweets that contained the street and brand name of a prescription drug within the tweet. Using BERTopic in combination with Uniform Manifold Approximation and Projection and k-means, we generated topics for the street-name corpus (n=170,618) and brand-name corpus (n=245,145). Valence Aware Dictionary and Sentiment Reasoner (VADER) scores were used to classify whether tweets within the topics had positive, negative, or neutral sentiments. Two different logistic regression classifiers were used to predict the sentiment label within each corpus. The first model used a tweet's engagement metrics and topic ID to predict the label, while the second model used those features in addition to the top 5000 tweets with the largest term-frequency-inverse document frequency score.

RESULTS

Using BERTopic, we identified 40 topics for the street-name data set and 5 topics for the brand-name data set, which we generalized into 8 and 5 topics of discussion, respectively. Four of the general themes of discussion in the brand-name corpus referenced drug use, while 2 themes of discussion in the street-name corpus referenced drug use. From the VADER scores, we found that both corpora were inclined toward positive sentiment. Adding the vectorized tweet text increased the accuracy of our models by around 40% compared with the models that did not incorporate the tweet text in both corpora.

CONCLUSIONS

BERTopic was able to classify tweets well. As with LDA, the discussion using brand names was more similar between tweets than the discussion using street names. VADER scores could only be logically applied to the brand-name corpus because of the high prevalence of non-drug-related topics in the street-name data. Brand-name tweets either discussed drugs positively or negatively, with few posts having a neutral emotionality. From our machine learning models, engagement alone was not enough to predict the sentiment label; the added context from the tweets was needed to understand the emotionality of a tweet.

High Interest in the Use of mHealth Platform for HIV Prevention among Men Who Have Sex with Men in Nepal

Mobile technology growth in Nepal offers promising opportunities for using mobile health (mHealth) interventions to facilitate HIV prevention efforts. However, little is known about access and utilization of communication technology and their willingness to use mHealth for HIV prevention services in Nepal. We conducted a cross-sectional respondent-driven sampling survey of 250 MSM in Kathmandu Valley of Nepal from October to December 2022. We collected information on participant characteristics, HIV risk-related behaviors, ownership, or access to and frequency of use of communication technology (phones, tablets, laptops, and computers), and willingness to use mHealth to access HIV prevention services. Descriptive, bivariate, and multivariate linear regression analyses were performed. Almost all participants had smartphones with the internet (231/250, 92.4%) and accessed the internet daily (219/250, 87.6%) on the smartphone (236/250, 94.4%). The median score for willingness to use mHealth for HIV prevention was 10 (IQR: 3 to 17). Willingness to use mHealth was higher among those participants with a high school or above education (β = 0.223, p = < 0.001), had experienced violence (β = 0.231, p = 0.006), and had moderate to severe depressive symptoms (β = 0.223, p = < 0.001). However, monthly income above NPR 20,000 (USD 150) (β= -0.153, p = 0.008), disclosure of their sexual orientation to anyone (β= -0.159, p = < 0.007), and worry about being negatively judged by health care workers (β= -0.136, p = 0.023) were less willing to use mHealth strategies. The findings from this study suggest that there is a high willingness for utilizing mHealth interventions for HIV prevention in MSM population who are at higher risk of HIV acquisition.

Social Media Discourse Related to Caregiving for Older Adults Living With Alzheimer Disease and Related Dementias: Computational and Qualitative Study

BACKGROUND

In the United States, caregivers of people living with Alzheimer disease and Alzheimer disease-related dementias (AD/ADRD) provide >16 billion hours of unpaid care annually. These caregivers experience high levels of stress and burden related to the challenges associated with providing care. Social media is an emerging space for individuals to seek various forms of support.

OBJECTIVE

We aimed to explore the primary topics of conversation on the social media site Reddit related to AD/ADRD. We then aimed to explore these topics in depth, specifically examining elements of social support and behavioral symptomology discussed by users.

METHODS

We first generated an unsupervised topic model from 6563 posts made to 2 dementia-specific subreddit forums (r/Alzheimers and r/dementia). Then, we conducted a manual qualitative content analysis of a random subset of these data to further explore salient themes in the corpus.

RESULTS

The topic model with the highest overall coherence score (0.38) included 10 topics, including caregiver burden, anxiety, support-seeking, and AD/ADRD behavioral symptomology. Qualitative analyses provided added context, wherein users sought emotional and informational support for many aspects of the care experience, including assistance in making key care-related decisions. Users expressed challenging and complex emotions on Reddit, which may be taboo to express in person.

CONCLUSIONS

Reddit users seek many different forms of support, including emotional and specific informational support, from others on the internet. Users expressed a variety of concerns, challenges, and behavioral symptoms to manage as part of the care experience. The unique (ie, anonymous and moderated) nature of the forum allowed for a safe space to express emotions free from documented caregiver stigma. Additional support structures are needed to assist caregivers of people living with AD/ADRD.

First-hand accounts of structural stigma toward people who use opioids on Reddit

People who use opioids face multilevel stigma that negatively affects their health and well-being and drives opioid-related overdose. Little research has focused on lived experience of the structural levels of stigma toward opioid use. This study identified and qualitatively analyzed Reddit content about structural stigma toward opioid use. Iterative, human-in-the-loop natural language processing methods were used to identify relevant posts and comments from an opioid-related subforum. Ultimately, 273 posts and comments were qualitatively analyzed via directed content analysis guided by a prominent conceptualization of stigma. Redditors described how structures-including governmental programs and policies, the pharmaceutical industry, and healthcare systems-stigmatize people who use opioids. Structures were reported to stigmatize through labeling (i.e., particularly in medical settings), perpetuating negative stereotypes, separating people who use opioids into those who use opioids "legitimately" versus "illegitimately," and engendering status loss and discrimination (e.g., denial of healthcare, loss of employment). Redditors also posted robust formulations of structural stigma, mostly describing how it manifests in the criminalization of substance use, is often driven by profit motive, and leads to the pervasiveness of fentanyl in the drug supply and the current state of the overdose crisis. Some posts and comments highlighted interpersonal and structural resources (e.g., other people who use opioids, harm reduction programs, telemedicine) leveraged to navigate structural stigma and its effects. These findings reveal key ways by which structural stigma can pervade the lives of people who use opioids and show the value of social media data for investigating complex social processes. Particularly, this study's findings related to structural separation may help encourage efforts to promote solidarity among people who use opioids. Attending to first-hand accounts of structural stigma can help interventions aiming to reduce opioid-related stigma be more responsive to these stigmatizing structural forces and their felt effects.

Analyzing Reddit Forums Specific to Abortion That Yield Diverse Dialogues Pertaining to Medical Information Seeking and Personal Worldviews: Data Mining and Natural Language Processing Comparative Study

BACKGROUND

Attitudes toward abortion have historically been characterized via dichotomized labels, yet research suggests that these labels do not appropriately encapsulate beliefs on abortion. Rather, contexts, circumstances, and lived experiences often shape views on abortion into more nuanced and complex perspectives. Qualitative data have also been shown to underpin belief systems regarding abortion. Social media, as a form of qualitative data, could reveal how attitudes toward abortion are communicated publicly in web-based spaces. Furthermore, in some cases, social media can also be leveraged to seek health information.

OBJECTIVE

This study applies natural language processing and social media mining to analyze Reddit (Reddit, Inc) forums specific to abortion, including r/Abortion (the largest subreddit about abortion) and r/AbortionDebate (a subreddit designed to discuss and debate worldviews on abortion). Our analytical pipeline intends to identify potential themes within the data and the affect from each post.

METHODS

We applied a neural network-based topic modeling pipeline (BERTopic) to uncover themes in the r/Abortion (n=2151) and r/AbortionDebate (n=2815) subreddits. After deriving the optimal number of topics per subreddit using an iterative coherence score calculation, we performed a sentiment analysis using the Valence Aware Dictionary and Sentiment Reasoner to assess positive, neutral, and negative affect and an emotion analysis using the Text2Emotion lexicon to identify potential emotionality per post. Differences in affect and emotion by subreddit were compared.

RESULTS

The iterative coherence score calculation revealed 10 topics for both r/Abortion (coherence=0.42) and r/AbortionDebate (coherence=0.35). Topics in the r/Abortion subreddit primarily centered on information sharing or offering a source of social support; in contrast, topics in the r/AbortionDebate subreddit centered on contextualizing shifting or evolving views on abortion across various ethical, moral, and legal domains. The average compound Valence Aware Dictionary and Sentiment Reasoner scores for the r/Abortion and r/AbortionDebate subreddits were 0.01 (SD 0.44) and -0.06 (SD 0.41), respectively. Emotionality scores were consistent across the r/Abortion and r/AbortionDebate subreddits; however, r/Abortion had a marginally higher average fear score of 0.36 (SD 0.39).

CONCLUSIONS

Our findings suggest that people posting on abortion forums on Reddit are willing to share their beliefs, which manifested in diverse ways, such as sharing abortion stories including how their worldview changed, which critiques the value of dichotomized abortion identity labels, and information seeking. Notably, the style of discourse varied significantly by subreddit. r/Abortion was principally leveraged as an information and outreach source; r/AbortionDebate largely centered on debating across various legal, ethical, and moral abortion domains. Collectively, our findings suggest that abortion remains an opaque yet politically charged issue for people and that social media can be leveraged to understand views and circumstances surrounding abortion.

Online Perspectives of Workers Navigating Workers' Compensation Systems: A Content Analysis of the Reddit Social Media Platform

BACKGROUND

Social media platforms are increasingly used by the general public as a source of information on health-related and legal concerns, among other topics. Reddit.com, one of the top 10 most visited websites in the United States, is a popular social media platform that allows users to anonymously discuss various topics, including workers' compensation (WC). Understanding the candid concerns of workers who are navigating WC systems will allow for the development of more effective educational resources that are tailored to the needs of this population.  Methods: In January-March 2023, a cross-sectional review of anonymous public posts submitted to the r/WorkersComp section of the Reddit social media website between December 2021 and December 2022 was performed. Post content was extracted from a systematic random sample and coded into themes/sub-themes and emotional tones by two independent reviewers. A third reviewer resolved any discrepancies in coding in order to reach consensus prior to data analysis. The data were analyzed using Microsoft Excel 2019 (Microsoft Corporation, Redmond, WA, USA).

RESULTS

Content from 200 original posts submitted to r/WorkersComp was reviewed and analyzed. Nearly 94.0% of posts (n =187) specified a state of residence, with posters most frequently residing within the United States in California (32.0%), New York (7.0%), Pennsylvania (5.0%), and Florida (5.0%). The most common primary theme was "medical" (27.0%, n = 54), with questions and comments related to provider complaints, medical care access, referral denials, maximum medical improvement, and independent medical examinations being the most frequent within this category. The second most common primary theme was "legal" (26.5%, n = 53), with questions and comments related to lawyer retainment and settlements being the most frequent within this category. The third most common primary theme was "general" (18.5%, n = 37), with questions and comments related to the general claims process, eligibility for WC, claim denial, and communication issues with claims adjusters being the most frequent within this category. The fourth most common primary theme was "employer" (14.0%, n = 28), with questions and comments related to employer retaliation, job security, and work restrictions being most frequent within this category. Only 37.0% of posts (n = 74) expressed a clear emotional tone, with frustration (13.5%, n = 10), fear (13.5%, n = 10), and confusion (13.5%, n = 10) being the most frequent tones observed in this sample of posts.

CONCLUSIONS

Our findings indicate that there are workers who are navigating WC systems who use social media platforms such as Reddit to obtain information and advice on various aspects of WC, including medical issues, legal advice, and employer concerns. These findings may be used to address the information and education needs of workers who are navigating WC systems, which may help attenuate some of the frustrations surrounding the WC claims process.

Using Social Media to Examine Themes Surrounding Fentanyl Misuse and Risk Indicators

Background: Opioid misuse is a crisis in the United States, and synthetic opioids such as fentanyl pose risks for overdose and mortality. Individuals who misuse substances commonly seek information and support online due to stigma and legal concerns, and this online networking may provide insight for substance misuse prevention and treatment. We aimed to characterize topics in substance-misuse related discourse among members of an online fentanyl community. Method: We investigated posts on a fentanyl-specific forum on the platform Reddit to identify emergent substance misuse-related themes potentially indicative of heightened risk for overdose and other adverse health outcomes. We analyzed 27 posts and 338 comments with a qualitative codebook established using a subset of user posts via inductive and deductive methods. Posts and comments were independently reviewed by two coders with a third coder resolving discrepancies. The top 200 subreddits with the most activity by r/fentanyl members were also inductively analyzed to understand interests of r/fentanyl users. Results: Functional/quality of life impairments due to substance misuse (29%) was the most commonly occurring theme, followed by polysubstance use (27%) and tolerance/dependence/withdrawal (20%). Additional themes included drug identification with photos, substances cut with other drugs, injection drugs, and past overdoses. Media-focused subreddits and other drug focused communities were among the communities most often followed by r/fentanyl users. Conclusion: Themes closely align with DSM-V substance use disorder symptoms for fentanyl and other substances. High involvement in media-focused subreddits and other substance-misuse-related communities suggests digital platforms as acceptable for overdose prevention and recovery support interventions.

An analysis of cannabinoid hyperemesis syndrome Reddit posts and themes

INTRODUCTION

Reddit hosts a large active community of members dedicated to the discussion of cannabinoid hyperemesis syndrome. We sought to describe common themes discussed and the most frequently mentioned triggers and therapies for cannabinoid hyperemesis syndrome exacerbations in the Reddit online community.

METHODS

Data collected from six subreddits were filtered using natural language processing to curate posts referencing cannabinoid hyperemesis syndrome. Based on a manual review of posts, common themes were identified. A machine learning model was trained using the manually categorized data to automatically classify the themes for the rest of the posts so that their distributions could be quantified.

RESULTS

From August 2018 to November 2022, 2683 unique posts were collected. Thematic analysis resulted in five overall themes: cannabinoid hyperemesis syndrome-related science; symptom timing; cannabinoid hyperemesis syndrome treatment and prevention; cannabinoid hyperemesis syndrome diagnosis and education; and health impacts. Additionally, 447 trigger and 664 therapy-related posts were identified. The most commonly mentioned triggers for cannabinoid hyperemesis syndrome episodes included: food and drink (n = 62), cannabinoids (n = 45), mental health (e.g., stress, anxiety) (n = 27), and alcohol (n = 22). Most commonly mentioned cannabinoid hyperemesis syndrome therapies included: hot water/bathing (n = 62), hydration (n = 60), antiemetics (n = 42), food and drink (n = 38), gastrointestinal medications (n = 38), behavioral therapies (e.g., meditation, yoga) (n = 35), and capsaicin (n = 29).

DISCUSSION

Reddit posts for cannabinoid hyperemesis syndrome provide a valuable source of community discussion and individual reports of people experiencing cannabinoid hyperemesis syndrome. Mental health and alcohol were frequently reported triggers within the posts but are not often identified in the literature. While many of the therapies mentioned are well documented, behavioral responses such as meditation and yoga have not been explored by the scientific literature.

CONCLUSIONS

Knowledge shared via online social media platforms contains detailed information on self-reported cannabinoid hyperemesis syndrome disease and management experiences, which could serve as valuable data for the development of treatment strategies. Further longitudinal studies in patients with cannabinoid hyperemesis syndrome are needed to corroborate these findings.