Utilizing social media to explore overdose and HIV/HCV risk behaviors among current opioid misusers

Examining Symptoms of Stimulant Misuse and Community Support Among Members of a Recovery-Oriented Online Community

Misuse of prescription and non-prescription stimulants and related overdose deaths represent a growing public health crisis that warrants immediate intervention. We examined 100 posts and their respective comments from a public, recovery-oriented Reddit community in January 2021 to explore content related to DSM-V stimulant use disorder symptoms, access and barriers to recovery, and peer support. Using inductive and deductive methods, a codebook was developed with the following primary themes: 1) DSM-V Symptoms and Risk Factors, 2) Stigma/Shame, 3) Seeking Advice or Information, 4) Supportive or Unsupportive Comments. In 37% of posts community members reported taking high doses and engaging in prolonged misuse of stimulants. Nearly half of posts in the sample (46%) were seeking advice for recovery, but 42% noted fear of withdrawal symptoms or a loss of productivity (18%) as barriers to abstinence or a reduction in use. Concerns related to stigma, shame, hiding use from others (30%), and comorbid mental health conditions (34%) were also noted. Social media content analysis allows for insight into information about lived experiences of individuals struggling with substance use disorders. Future online interventions should address recovery barriers related to stigma and shame as well as fears associated with the physical and psychological impact of quitting stimulant misuse.

Utilization of Social Media for the Prevention and Control of HIV/AIDS: A Scoping Review

Social media has been used widely in public health for understanding health risks and intervening to improve health. However, the utilization of social media for HIV prevention and control interventions has not been clearly characterized. We conducted a scoping review on the utilization of social media for HIV prevention and control to identify gaps in the literature and highlight opportunities for future research and intervention. A comprehensive search of seven databases was performed: PubMed, Embase, CINAHL Complete (EBSCO), PsycInfo (EBSCO), Scopus, and WOS (Science Citation Index Expanded (SCI-EXPANDED). The Preferred Reporting Items for Systematic Review and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) was used as a framework. A total of 790 articles were screened at the title and abstract level, and 78 were screened at the full-text level. Twenty-three articles met the eligibility criteria for review. We found that Facebook was still the most common social media service used to reach target populations. Lesbian, gay, bisexual and transgender (LGBT) individuals were the primary groups and were the most common targets of social media-based HIV prevention interventions. Outcomes of social media-based interventions, such as increased HIV testing, social networking and social support, condom use attitudes, behavioral skills, and research participation, were reported. Most social media-based interventions have emphasized HIV prevention, with fewer interventions focused on improving linkage to care and adherence to treatment among people living with HIV. Future studies may benefit from using social media for interventions to improve HIV-related outcomes in high-risk populations. This review highlights the potential and challenges of social media approaches for HIV/AIDS prevention and control.

PrEP for people who use opioids: A NIDA clinical trials network survey study in Southern U.S. cities where HIV incidence is high

BACKGROUND

People who use opioids (PWUO) are at increased risk for HIV. Pre-exposure prophylaxis (PrEP) is effective but underutilized as HIV prevention among PWUO. This study examined predictors of willingness to take daily oral PrEP and long-acting injectable (LAI) PrEP among PWUO across eight Southern urban cities with high HIV incidence.

METHODS

HIV-negative PWUO (N = 308) seeking services in community-based programs participated in this cross-sectional survey study. Measures included demographics, sexual risk behavior, substance use frequency, and awareness of and willingness to take oral and injectable PrEP. Data were analyzed using mixed-effects models.

RESULTS

Willingness to take daily oral and LAI PrEP was moderately high (69.16% and 62.02%, respectively). Half had heard of PrEP, but only 4% had ever taken it. Only education and condomless vaginal sex predicted willingness to take oral PrEP. Only education predicted willingness to take LAI PrEP. Polysubstance use was prevalent, with substantial proportions of PWUO reporting frequent use of injection drugs (opioids or stimulants, 79.5%), non-injection opioids (73.3%), non-injection stimulants (71.1%), cannabis (62.6%), and hazardous drinking (29.6%). About 20% reported past-year condomless anal sex, and one-third reported past-year condomless vaginal sex.

CONCLUSIONS

PWUO in this study were amenable to PrEP, particularly in light of education and condomless vaginal sex. Careful consideration for matching PrEP messaging to the PWUO audience is needed. PrEP promotion should expand beyond men who have sex with men to include groups such as these predominantly heterosexual, polysubstance-using PWUO with HIV risk who were open to both formulations of PrEP.

Investigating Substance Use via Reddit: Systematic Scoping Review

BACKGROUND

Reddit's (Reddit Inc) large user base, diverse communities, and anonymity make it a useful platform for substance use research. Despite a growing body of literature on substance use on Reddit, challenges and limitations must be carefully considered. However, no systematic scoping review has been conducted on the use of Reddit as a data source for substance use research.

OBJECTIVE

This review aims to investigate the use of Reddit for studying substance use by examining previous studies' objectives, reasons, limitations, and methods for using Reddit. In addition, we discuss the implications and contributions of previous studies and identify gaps in the literature that require further attention.

METHODS

A total of 7 databases were searched using keyword combinations including Reddit and substance-related keywords in April 2022. The initial search resulted in 456 articles, and 227 articles remained after removing duplicates. All included studies were peer reviewed, empirical, available in full text, and pertinent to Reddit and substance use, and they were all written in English. After screening, 60 articles met the eligibility criteria for the review, with 57 articles identified from the initial database search and 3 from the ancestry search. A codebook was developed, and qualitative content analysis was performed to extract relevant evidence related to the research questions.

RESULTS

The use of Reddit for studying substance use has grown steadily since 2015, with a sharp increase in 2021. The primary objective was to identify tendencies and patterns in various types of substance use discussions (52/60, 87%). Reddit was also used to explore unique user experiences, propose methodologies, investigate user interactions, and develop interventions. A total of 9 reasons for using Reddit to study substance use were identified, such as the platform's anonymity, its widespread popularity, and the explicit topics of subreddits. However, 7 limitations were noted, including the platform's low representativeness of the general population with substance use and the lack of demographic information. Most studies use application programming interfaces for data collection and quantitative approaches for analysis, with few using qualitative approaches. Machine learning algorithms are commonly used for natural language processing tasks. The theoretical, methodological, and practical implications and contributions of the included articles are summarized and discussed. The most prevalent practical implications are investigating prevailing topics in Reddit discussions, providing recommendations for clinical practices and policies, and comparing Reddit discussions on substance use across various sources.

CONCLUSIONS

This systematic scoping review provides an overview of Reddit's use as a data source for substance use research. Although the limitations of Reddit data must be considered, analyzing them can be useful for understanding patterns and user experiences related to substance use. Our review also highlights gaps in the literature and suggests avenues for future research.

Automating Detection of Drug-Related Harms on Social Media: Machine Learning Framework

BACKGROUND

A hallmark of unregulated drug markets is their unpredictability and constant evolution with newly introduced substances. People who use drugs and the public health workforce are often unaware of the appearance of new drugs on the unregulated market and their type, safe dosage, and potential adverse effects. This increases risks to people who use drugs, including the risk of unknown consumption and unintentional drug poisoning. Early warning systems (EWSs) can help monitor the landscape of emerging drugs in a given community by collecting and tracking up-to-date information and determining trends. However, there are currently few ways to systematically monitor the appearance and harms of new drugs on the unregulated market in Canada.

OBJECTIVE

The goal of this work is to examine how artificial intelligence can assist in identifying patterns of drug-related risks and harms, by monitoring the social media activity of public health and law enforcement groups. This information is beneficial in the form of an EWS as it can be used to identify new and emerging drug trends in various communities.

METHODS

To collect data for this study, 145 relevant Twitter accounts throughout Quebec (n=33), Ontario (n=78), and British Columbia (n=34) were manually identified. Tweets posted between August 23 and December 21, 2021, were collected via the application programming interface developed by Twitter for a total of 40,393 tweets. Next, subject matter experts (1) developed keyword filters that reduced the data set to 3746 tweets and (2) manually identified relevant tweets for monitoring and early warning efforts for a total of 464 tweets. Using this information, a zero-shot classifier was applied to tweets from step 1 with a set of keep (drug arrest, drug discovery, and drug report) and not-keep (drug addiction support, public safety report, and others) labels to see how accurately it could extract the tweets identified in step 2.

RESULTS

When looking at the accuracy in identifying relevant posts, the system extracted a total of 584 tweets and had an overlap of 392 out of 477 (specificity of ~84.5%) with the subject matter experts. Conversely, the system identified a total of 3162 irrelevant tweets and had an overlap of 3090 (sensitivity of ~94.1%) with the subject matter experts.

CONCLUSIONS

This study demonstrates the benefits of using artificial intelligence to assist in finding relevant tweets for an EWS. The results showed that it can be quite accurate in filtering out irrelevant information, which greatly reduces the amount of manual work required. Although the accuracy in retaining relevant information was observed to be lower, an analysis showed that the label definitions can impact the results significantly and would therefore be suitable for future work to refine. Nonetheless, the performance is promising and demonstrates the usefulness of artificial intelligence in this domain.

Digital Marketing: A Unique Multidisciplinary Approach towards the Elimination of Viral Hepatitis

New technologies are supported by the global implementation of the internet. These improvements have deeply affected various disciplines of sciences and consequently changed services such as daily business, particularly health sectors. Innovative digital marketing strategies utilize the channels of social media and retrieved user data to analyze and improve relevant services. These multidisciplinary innovations can assist specialists, physicians and researchers in diagnostic, prophylaxis and treatment issues in the health sector. Accordingly, compared to recent decades, health decision makers are more accurate and trustful in defining new strategies. Interestingly, using social media and mobile health apps in current pandemics of SARS-CoV-2 could be an important instance of the key role of these platforms at the local and global level of health policies. These digital technologies provide platforms to connect public health sectors and health politicians for communicating and spreading relevant information. Adding influencers and campaigns to this toolbox strengthens the implementation of public health programs. In 2016, the WHO adopted a global program to eliminate viral hepatitis by 2030. Recent constructive measures that have been used in the battle against COVID-19 could be adopted for the elimination of viral hepatitis program. The presented evidence in our narrative review demonstrates that the application of digital marketing tools to create campaigns on social media, armed with professional influencers, can efficiently consolidate this program. The application of different strategies in using these popular tools will raise the public awareness about viral hepatitis. Subsequently, the availability of an effective vaccine for HBV and antiviral medication for HCV can motivate the audience to take steps towards prophylaxis and screening methods against these infectious illnesses. The encouragement of health policy makers to apply digital communication technologies and comprehensive roadmaps to implement this global program will certainly decrease the burden of viral hepatitis worldwide.

Detecting risk level in individuals misusing fentanyl utilizing posts from an online community on Reddit

INTRODUCTION

Opioid misuse is a public health crisis in the US, and misuse of synthetic opioids such as fentanyl have driven the most recent waves of opioid-related deaths. Because those who misuse fentanyl are often a hidden and high-risk group, innovative methods for identifying individuals at risk for fentanyl misuse are needed. Machine learning has been used in the past to investigate discussions surrounding substance use on Reddit, and this study leverages similar techniques to identify risky content from discussions of fentanyl on this platform.

METHODS

A codebook was developed by clinical domain experts with 12 categories indicative of fentanyl misuse risk, and this was used to manually label 391 Reddit posts and comments. Using this data, we built machine learning classification models to identify fentanyl risk.

RESULTS

Our machine learning risk model was able to detect posts or comments labeled as risky by our clinical experts with 76% accuracy and 76% sensitivity. Furthermore, we provide a vocabulary of community-specific, colloquial words for fentanyl and its analogues.

DISCUSSION

This study uses an interdisciplinary approach leveraging machine learning techniques and clinical domain expertise to automatically detect risky discourse, which may elicit and benefit from timely intervention. Moreover, our vocabulary of online terms for fentanyl and its analogues expands our understanding of online "street" nomenclature for opiates. Through an improved understanding of substance misuse risk factors, these findings allow for identification of risk concepts among those misusing fentanyl to inform outreach and intervention strategies tailored to this at-risk group.

Comparing the feasibility of four web-based recruitment strategies to evaluate the treatment preferences of rural and urban adults who misuse non-prescribed opioids

This cross-sectional study examined the feasibility of using four different web-based strategies to recruit rural and urban adults who use opioids non-medically for a survey on opioid use disorder (OUD) treatment preferences, and compared the treatment preferences of rural versus urban participants. Preferences for medication for opioid use disorder (MOUD) formulation and OUD treatment models were assessed through an online survey. Recruitment advertisements were shown on Facebook, Google AdWords, Reddit, and Amazon Mechanical Turk (MTurk). Participants were categorized by zip code into urban versus rural residence using the Centers for Medicaid and Medicaid Health Resources and Services Administration definitions. OUD treatment preferences were compared using chi-square and t-tests. Among the 851 participants recruited, 815 provided zip codes and were classified as residing in rural (n = 200, 24.5%) or urban (n = 615, 75.4%) regions. A crowdsourcing service (MTurk) recruited the most rural participants, while posts on a social news website (Reddit) recruited the most urban participants (χ²₃ = 17.0, p < 0.01). While preferred MOUD formulation and OUD treatment model did not differ by rurality, rural participants were more likely to report a willingness to receive OUD treatment integrated with general medical care (χ²₁ = 18.9, p < 0.0001). This study demonstrated that web-based strategies are feasible for recruiting rural adults who misuse opioids. Results suggest OUD treatment preferences largely did not differ by rural residence, and highlight the importance of enhancing the availability and increasing education about MOUD formulations in rural regions.

Determining the prevalence of cannabis, tobacco, and vaping device mentions in online communities using natural language processing

INTRODUCTION

The relationship between cannabis, tobacco, and vaping devices is both rapidly changing and poorly understood, with consumers rapidly shifting between use of all three product types. Given this dynamic and evolving landscape, there is an urgent need to monitor and better understand co-use, dual-use, and transition patterns between these products. This study describes work that utilizes social media - in this case, Reddit - in conjunction with automated Natural Language Processing (NLP) methods to better understand cannabis, tobacco, and vaping device product usage patterns.

METHODS

We collected Reddit data from the period 2013-2018, sourced from eight popular, high-volume Reddit communities (subreddits) related to the three product categories. We then manually annotated (coded) a set of 2640 Reddit posts and trained a machine learning-based NLP algorithm to automatically identify and disambiguate between cannabis or tobacco mentions (both smoking and vaping) in Reddit posts. This classifier was then applied to all data derived from the eight subreddits, 767,788 posts in total.

RESULTS

The NLP algorithm achieved an overall moderate performance (overall F-score of 0.77). When applied to our large corpus of Reddit posts, we discovered that over 10% of posts in the smoking cessation subreddit r/stopsmoking were classified as referring to vaping nicotine, and that only 2% of posts from the subreddits r/electronic_cigarette and r/vaping were classified as referring to smoking (tobacco) cessation.

CONCLUSIONS

This study presents the results of applying an NLP algorithm designed to identify and distinguish between cannabis and tobacco mentions (both smoking and vaping) in Reddit posts, hence contributing to our currently limited understanding of co-use, dual-use, and transition patterns between these products.

COVID-19 Pandemic Impact on Substance Misuse: A Social Media Listening, Mixed Method Analysis

The restrictive measures adopted during the COVID-19 pandemic modified some previously consolidated drug use patterns. A focus on social networks allowed drug users to discuss, share opinions and provide advice during a worldwide emergency context. In order to explore COVID-19-related implications on drug trends/behaviour and on most popular psychotropic substances debated, the focus here was on the constantly updated, very popular, Reddit social platform’s posts and comments. A quantitative and qualitative analysis of r/Drugs and related subreddits, using a social media listening netnographic approach, was carried out. The post/comments analysed covered the time-frame December 2019–May 2020. Between December 2019 and May 2020, the number of whole r/Drugs subreddit members increased from 619,563 to 676,581 members, respectively, thus increasing by 9.2% by the end of the data collection. Both the top-level r/Drugs subreddit and 92 related subreddits were quantitatively analysed, with posts/comments related to 12 drug categories. The drugs most frequently commented on included cannabinoids, psychedelics, opiates/opioids, alcohol, stimulants and prescribed medications. The qualitative analysis was carried out focussing on four subreddits, relating to some 1685 posts and 3263 comments. Four main themes of discussion (e.g., lockdown-associated immunity and drug intake issues; drug-related behaviour/after-quarantine plans’ issues; lockdown-related psychopathological issues; and peer-to-peer advice at the time of COVID-19) and four categories of Redditors (e.g., those continuing the use of drugs despite the pandemic; the “couch epidemiologists”; the conspirationists/pseudo-science influencers; and the recovery-focused users) were tentatively identified here. A mixed-methods, social network-based analysis provided a range of valuable information on Redditors’ drug use/behaviour during the first phase of the COVID-19 pandemic. Further studies should be carried out focusing on other social networks as well as later phases of the pandemic.

What are Ophthalmology Patients Asking Online? An Analysis of the Eye Triage Subreddit

Importance

Ophthalmology patients are seeking medical advice on social media websites like Reddit, where users are able to post comments and discuss issues pertaining to different topics that are organized in ‘subreddits’. Understanding which issues are most pertinent will guide ophthalmic providers in delivering more effective patient education.

Methods

This cross-sectional study assessed a systematic sample of the first 22 posts and their top 3 comments from each month since January 27th, 2019, the subreddit’s creation. Information was gathered from reddit.com/r/eyetriage in October 2019 and analyzed in November 2019.

Main Outcomes

The posts were characterized by date and time, inclusion of an image, type, content, emotional tone, and number of upvotes and comments. The comments were categorized based on content, emotional tone, time of comment, and user background. Post and comment content codes were categorized in an iterative manner with differences resolved by author consensus. Categorical statistics were compiled.

Results

Two hundred posts and 456 comments were analyzed since the creation of r/eyetriage, a forum created exclusively for patients to seek advice from health-care professionals. Twenty-six (13%) of the total posts included an image. On average, comments received 1.76 ± 2.17 upvotes along with 4.50 ± 4.47 replies. The most common content codes among the posts were 42 (21.0%) seeking diagnoses, 23 (11.5%) surgical complications, and 13 (6.50%) alternative medication options. Eighty-two (41%) posts conveyed a clear emotional tone, most notably 11 (13.4%) with anxiety and 10 (12.2%) with worry. The top comments came from 165 (36.2%) self-identified patients, 151 (33.1%) optometrists, and 49 (10.8%) ophthalmologists. The top comment codes for replies included 158 (34.7%) with treatment advice, 70 (15.4%) with advice deferred to follow-up appointment with other health-care specialists, and 60 (13.2%) with sharing information.

Conclusions

Patients are asking ophthalmology-related questions on the Eye Triage subreddit, and they are more likely to receive information from other patients or optometrists than from self-identified ophthalmologists. When emotions were revealed, patients were often anxious and worried. Opportunities exist for ophthalmologists to take a more active role on this subreddit and help educate patients.

Characterizing Pathways of Non-oral Prescription Stimulant Non-medical Use Among Adults Recruited From Reddit

Objective: Prescription stimulant non-medical use (NMU) is a national predicament. While the risks of prescription stimulant NMU have been considered, less is known about non-oral use. To focus on this gap, a sample of adults with non-oral prescription stimulant NMU within the last 5-years was recruited. The purpose of the present study was to characterize the pathways and substance transitions associated with prescription stimulant NMU and non-oral prescription stimulant NMU in this unique sample of adults. Methods: Adults (n = 225) reporting non-oral prescription stimulant NMU within the last 5 years were recruited to complete an online survey by banner ads placed on the Reddit website between February and September 2019. After completion of the survey, a second study consisting of an in-depth telephone interview was conducted with 23 participants: interviews took place between July and September 2019. Data reported here include substance, route of administration and class transitions, as well as qualitative data from the interviews. Results: Approximately 1 in 5 began their substance use trajectory with prescription stimulants (19.1%). Other than marijuana, most exposures to illicit substances occurred after both initial prescription stimulant NMU and initial non-oral prescription stimulant NMU. The most frequently reported route of administration transition was from oral use to snorting (n = 158, 70.2%), however, other route of administration transitions included oral use to injection drug use (n = 14, 6%). In-depth interviews elaborated upon these transitions and indicated that prescription stimulant NMU was consequential to substance use pathways. Conclusions: Oral prescription stimulant NMU was a precursor to non-oral prescription stimulant NMU. Non-oral prescription stimulant NMU was a precursor to illicit substance use, suggesting that prescription stimulant NMU impacts substance use pathways and revealing opportunities for intervention.