Automated detection of vaping-related tweets on Twitter during the 2019 EVALI outbreak using machine learning classification

There are increasingly strict regulations surrounding the purchase and use of combustible tobacco products (i.e., cigarettes); simultaneously, the use of other tobacco products, including e-cigarettes (i.e., vaping products), has dramatically increased. However, public attitudes toward vaping vary widely, and the health effects of vaping are still largely unknown. As a popular social media, Twitter contains rich information shared by users about their behaviors and experiences, including opinions on vaping. It is very challenging to identify vaping-related tweets to source useful information manually. In the current study, we proposed to develop a detection model to accurately identify vaping-related tweets using machine learning and deep learning methods. Specifically, we applied seven popular machine learning and deep learning algorithms, including Naïve Bayes, Support Vector Machine, Random Forest, XGBoost, Multilayer Perception, Transformer Neural Network, and stacking and voting ensemble models to build our customized classification model. We extracted a set of sample tweets during an outbreak of e-cigarette or vaping-related lung injury (EVALI) in 2019 and created an annotated corpus to train and evaluate these models. After comparing the performance of each model, we found that the stacking ensemble learning achieved the highest performance with an F1-score of 0.97. All models could achieve 0.90 or higher after tuning hyperparameters. The ensemble learning model has the best average performance. Our study findings provide informative guidelines and practical implications for the automated detection of themed social media data for public opinions and health surveillance purposes

Topics and sentiment surrounding vaping on Twitter and Reddit during the 2019 e-cigarette and vaping use-associated lung injury outbreak: Comparative study

BACKGROUND: Vaping or e-cigarette use has become dramatically more popular in the United States in recent years. e-Cigarette and vaping use-associated lung injury (EVALI) cases caused an increase in hospitalizations and deaths in 2019, and many instances were later linked to unregulated products. Previous literature has leveraged social media data for surveillance of health topics. Individuals are willing to share mental health experiences and other personal stories on social media platforms where they feel a sense of community, reduced stigma, and empowerment. OBJECTIVE: This study aimed to compare vaping-related content on 2 popular social media platforms (ie, Twitter and Reddit) to explore the context surrounding vaping during the 2019 EVALI outbreak and to support the feasibility of using data from both social platforms to develop in-depth and intelligent vaping detection models on social media. METHODS: Data were extracted from both Twitter (316,620 tweets) and Reddit (17,320 posts) from July 2019 to September 2019 at the peak of the EVALI crisis. High-throughput computational analyses (sentiment analysis and topic analysis) were conducted. In addition, in-depth manual content analyses were performed and compared with computational analyses of content on both platforms (577 tweets and 613 posts). RESULTS: Vaping-related posts and unique users on Twitter and Reddit increased from July 2019 to September 2019, with the average post per user increasing from 1.68 to 1.81 on Twitter and 1.19 to 1.21 on Reddit. Computational analyses found the number of positive sentiment posts to be higher on Reddit (P\u3c.001, 95% CI 0.4305-0.4475) and the number of negative posts to be higher on Twitter (P\u3c.001, 95% CI -0.4289 to -0.4111). These results were consistent with the clinical content analyses results indicating that negative sentiment posts were higher on Twitter (273/577, 47.3%) than Reddit (184/613, 30%). Furthermore, topics prevalent on both platforms by keywords and based on manual post reviews included mentions of youth, marketing or regulation, marijuana, and interest in quitting. CONCLUSIONS: Post content and trending topics overlapped on both Twitter and Reddit during the EVALI period in 2019. However, crucial differences in user type and content keywords were also found, including more frequent mentions of health-related keywords on Twitter and more negative health outcomes from vaping mentioned on both Reddit and Twitter. Use of both computational and clinical content analyses is critical to not only identify signals of public health trends among vaping-related social media content but also to provide context for vaping risks and behaviors. By leveraging the strengths of both Twitter and Reddit as publicly available data sources, this research may provide technical and clinical insights to inform automatic detection of social media users who are vaping and may benefit from digital intervention and proactive outreach strategies on these platforms

Exploring social media recruitment strategies and preliminary acceptability of an mHealth tool for teens with eating disorders

(1) Background: The current study leveraged social media to connect with teens with EDs to identify population specific characteristics and to gather feedback on an mHealth intervention. (2) Methods: We recruited teens with EDs from social media in two phases: (1) Discovery Group, (2) Testing Group. The Discovery Group

Examining the Gateway Hypothesis and Mapping Substance Use Pathways on Social Media: Machine Learning Approach

BackgroundSubstance misuse presents significant global public health challenges. Understanding transitions between substance types and the timing of shifts to polysubstance use is vital to developing effective prevention and recovery strategies. The gateway hypothesis suggests that high-risk substance use is preceded by lower-risk substance use. However, the source of this correlation is hotly contested. While some claim that low-risk substance use causes subsequent, riskier substance use, most people using low-risk substances also do not escalate to higher-risk substances. Social media data hold the potential to shed light on the factors contributing to substance use transitions. ObjectiveBy leveraging social media data, our study aimed to gain a better understanding of substance use pathways. By identifying and analyzing the transitions of individuals between different risk levels of substance use, our goal was to find specific linguistic cues in individuals’ social media posts that could indicate escalating or de-escalating patterns in substance use. MethodsWe conducted a large-scale analysis using data from Reddit, collected between 2015 and 2019, consisting of over 2.29 million posts and approximately 29.37 million comments by around 1.4 million users from subreddits. These data, derived from substance use subreddits, facilitated the creation of a risk transition data set reflecting the substance use behaviors of over 1.4 million users. We deployed deep learning and machine learning techniques to predict the escalation or de-escalation transitions in risk levels, based on initial transition phases documented in posts and comments. We conducted a linguistic analysis to analyze the language patterns associated with transitions in substance use, emphasizing the role of n-gram features in predicting future risk trajectories. ResultsOur results showed promise in predicting the escalation or de-escalation transition in risk levels, based on the historical data of Reddit users created on initial transition phases among drug-related subreddits, with an accuracy of 78.48% and an F1-score of 79.20%. We highlighted the vital predictive features, such as specific substance names and tools indicative of future risk escalations. Our linguistic analysis showed that terms linked with harm reduction strategies were instrumental in signaling de-escalation, whereas descriptors of frequent substance use were characteristic of escalating transitions. ConclusionsThis study sheds light on the complexities surrounding the gateway hypothesis of substance use through an examination of web-based behavior on Reddit. While certain findings validate the hypothesis, indicating a progression from lower-risk substances such as marijuana to higher-risk ones, a significant number of individuals did not show this transition. The research underscores the potential of using machine learning with social media analysis to predict substance use transitions. Our results point toward future directions for leveraging social media data in substance use research, underlining the importance of continued exploration before suggesting direct implications for interventions

Impact of the COVID-19 pandemic on burnout and perceived workplace quality among addiction treatment providers

Abstract Background This study examines the impact of the COVID-19 pandemic on work satisfaction, work-related stress, and perceived work quality among substance use treatment providers to better understand challenges faced among this group during the pandemic. Methods Participants of this study were 91 addiction treatment providers (e.g., therapists, physicians, community support specialists, administrative staff) recruited from various treatment facilities (e.g., inpatient and outpatient settings). Mixed method analyses were conducted to assess self-reported burnout, sources of work-related stress, and perceived work quality during the pandemic. Responses from providers reporting COVID-19 related decreases in work quality were compared to responses from providers who reported their quality of work had increased or remained the same. Results Results demonstrated half of providers (51%) reported their quality of work had decreased. This perceived decrease in quality of work was associated with higher levels of emotional exhaustion (M = 17.41 vs. M = 12.48, p = 0.002), workplace stress (M = 42.80 vs. M = 30.84, p = 0.001), as well as decreased enjoyment of work (83% vs. 51%, p = 0.001) and decreased personal accomplishment (M = 20.64 vs. M = 23.05 p = 0.001). Qualitative investigations further illustrated that increased hours, changes in work schedules, work-life balance challenges, difficulties with client communication, and increased client needs were contributing factors increasing stress/burnout and decreasing perceived work quality. Conclusions Addiction treatment providers experience high levels of burnout and workplace stress. Additionally, many individuals perceived a decrease in their quality of work during the COVID-19 pandemic. Addiction treatment facility administration should address these challenges to support the well-being of clinical staff and the clients they serve both during and after the COVID-19 pandemic

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

Funding Information: Funding for this work was provided by the National Institutes of Health (NIH) [Grant No: K02 DA043657 (Dr. Cavazos-Rehg) and Grant No: R01MH117172 (Dr. De Choudhury)], and through a postdoctoral fellowship to Dr. Aledavood from the James S. McDonnell Foundation . We would also like to acknowledge Vivian Agbonavbare and Nnenna Anako for their work to manually code posts and comments for this study. Publisher Copyright: © 2021 The AuthorsIntroduction: 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.Peer reviewe

An mHealth Intervention to Address Depression and Improve Antiretroviral Therapy Adherence Among Youths Living With HIV in Uganda: Protocol for a Pilot Randomized Controlled Trial

BackgroundPeople living with HIV often struggle with mental health comorbidities that lower their antiretroviral therapy (ART) adherence. There is growing evidence that depression treatment may improve ART adherence and result in improved HIV outcomes. Given that mental health services are severely underequipped in low-resource settings, including in Uganda, new solutions to increase access to mental health care and close the treatment gap are urgently needed. This protocol paper presents the Suubi-Mhealth study, which proposed to develop a mobile health (mHealth) intervention for use among Ugandan youths (14-17 years) with comorbid HIV and depression, taking into account their unique contextual, cultural, and developmental needs. ObjectiveThe proposed study is guided by the following objectives: (1) to develop and iteratively refine an intervention protocol for Suubi-Mhealth based on formative work to understand the needs of youths living with HIV; (2) to explore the feasibility and acceptability of Suubi-Mhealth on a small scale to inform subsequent refinement; (3) to test the preliminary impact of Suubi-Mhealth versus a waitlist control group on youths’ outcomes, including depression and treatment adherence; and (4) to examine barriers and facilitators for integrating Suubi-Mhealth into health care settings. MethodsYouths will be eligible to participate in the study if they are (1) 14-17 years of age, (2) HIV-positive and aware of their status, (3) receiving care and ART from one of the participating clinics, and (4) living within a family. The study will be conducted in 2 phases. In phase 1, we will conduct focus group discussions with youths and health care providers, for feedback on the proposed intervention content and methods, and explore the feasibility and acceptability of the intervention. In phase II, we will pilot-test the preliminary impact of the intervention on reducing depression and improving ART adherence. Assessments will be conducted at baseline, 1-, 2-, and 6-months post intervention completion. ResultsParticipant recruitment for phase 1 is completed. Youths and health care providers participated in focus group discussions to share their feedback on the proposed Suubi-Mhealth intervention content, methods, design, and format. Transcription and translation of focus group discussions have been completed. The team is currently developing Suubi-Mhealth content based on participants’ feedback. ConclusionsThis study will lay important groundwork for several initiatives at the intersection of digital therapeutics, HIV treatment, and mental health, especially among sub-Saharan African youths, as they transition through adolescence and into adult HIV care settings. Trial RegistrationClinicalTrials.gov NCT05965245; https://clinicaltrials.gov/study/NCT05965245 International Registered Report Identifier (IRRID)DERR1-10.2196/5463

Assessment of Online Marketing and Sales Practices Among Recreational Cannabis Retailers in Five U.S. Cities

With more states legalizing recreational cannabis, examining cannabis retail and marketing is crucial, as it may influence consumers\u27 perceptions and behaviors. Particularly understudied is online cannabis retail. In Spring 2022, coders collected and analyzed data regarding retailer characteristics, age verification, and marketing strategies (e.g., product availability, health-related content, promotions, website imagery) among 195 cannabis retail websites in five U.S. cities (Denver, Colorado; Seattle, Washington; Portland, Oregon; Las Vegas, Nevada; Los Angeles, California). Descriptive analyses characterized the websites overall and across cities. Overall, 80.5% verified age for website entry, and 92.8% offered online purchases (92.3% of retailers in Seattle, where prohibited). Of these, 82.9% required age verification for purchases, and 30.9% offered delivery. Almost all (\u3e92%) offered flower/bud, concentrates, edibles, vaping devices, topicals, and tinctures. Health warnings were displayed on 38.3% of websites. Although all five states required health warnings regarding use during pregnancy, only 10.3% had these warnings. In addition, 59.0% posted some unsubstantiated health claims, most often indicating physical and mental health benefits (44.6%). Although Colorado, Washington, and Oregon prohibit health claims, 51.2-53.8% of these retailers posted them. Discounts, samples, or promotions were present on 90.8% of websites; 63.6% had subscription/membership programs. Subpopulations represented in website content included the following: 27.2% teens/young adults, 26.2% veterans, 7.2% sexual/gender minorities, and 5.6% racial/ethnic minorities. Imagery also targeted young people (e.g., 29.7% party/cool/popularity, 18.5% celebrity/influencer endorsement). Regulatory efforts are needed to better monitor promotional strategies and regulatory compliance (e.g., health claims, youth-oriented content, underage access) among online cannabis retailers