Predicting state level suicide fatalities in the united states with realtime data and machine learning.

Digital trace data and machine learning techniques are increasingly being adopted to predict suicide-related outcomes at the individual level; however, there is also considerable public health need for timely data about suicide trends at the population level. Although significant geographic variation in suicide rates exist by state within the United States, national systems for reporting state suicide trends typically lag by one or more years. We developed and validated a deep learning based approach to utilize real-time, state-level online (Mental Health America web-based depression screenings; Google and YouTube Search Trends), social media (Twitter), and health administrative data (National Syndromic Surveillance Program emergency department visits) to estimate weekly suicide counts in four participating states. Specifically, per state, we built a long short-term memory (LSTM) neural network model to combine signals from the real-time data sources and compared predicted values of suicide deaths from our model to observed values in the same state. Our LSTM model produced accurate estimates of state-specific suicide rates in all four states (percentage error in suicide rate of -2.768% for Utah, -2.823% for Louisiana, -3.449% for New York, and -5.323% for Colorado). Furthermore, our deep learning based approach outperformed current gold-standard baseline autoregressive models that use historical death data alone. We demonstrate an approach to incorporate signals from multiple proxy real-time data sources that can potentially provide more timely estimates of suicide trends at the state level. Timely suicide data at the state level has the potential to improve suicide prevention planning and response tailored to the needs of specific geographic communities.

Identification of Myths and Misinformation About Treatment for Opioid Use Disorder on Social Media: Infodemiology Study.

BACKGROUND: Health misinformation and myths about treatment for opioid use disorder (OUD) are present on social media and contribute to challenges in preventing drug overdose deaths. However, no systematic, quantitative methodology exists to identify what types of misinformation are being shared and discussed. OBJECTIVE: We developed a multistage analytic pipeline to assess social media posts from Twitter (subsequently rebranded as X), YouTube, Reddit, and Drugs-Forum for the presence of health misinformation about treatment for OUD. METHODS: Our approach first used document embeddings to identify potential new statements of misinformation from known myths. These statements were grouped into themes using hierarchical agglomerative clustering, and public health experts then reviewed the results for misinformation. RESULTS: We collected a total of 19,953,599 posts discussing opioid-related content across the aforementioned platforms. Our multistage analytic pipeline identified 7 main clusters or discussion themes. Among a high-yield data set of posts (n=303) for further public health expert review, these included discussion about potential treatments for OUD (90/303, 29.8%), the nature of addiction (68/303, 22.5%), pharmacologic properties of substances (52/303, 16.9%), injection drug use (36/303, 11.9%), pain and opioids (28/303, 9.3%), physical dependence of medications (22/303, 7.2%), and tramadol use (7/303, 2.3%). A public health expert review of the content within each cluster identified the presence of misinformation and myths beyond those used as seed myths to initialize the algorithm. CONCLUSIONS: Identifying and addressing misinformation through appropriate communication strategies could be an increasingly important component of preventing overdose deaths. To further this goal, we developed and tested an approach to aid in the identification of myths and misinformation about OUD from large-scale social media content.

Misogynistic Extremism: A Scoping Review.

In recent years, the concept of "misogynistic extremism" has emerged as a subject of interest among scholars, governments, law enforcement personnel, and the media. Yet a consistent understanding of how misogynistic extremism is defined and conceptualized has not yet emerged. Varying epistemological orientations may contribute to the current conceptual muddle of this topic, reflecting long-standing and on-going challenges with the conceptualization of its individual components. To address the potential impact of misogynistic extremism (i.e., violent attacks), a more precise understanding of what this phenomenon entails is needed. To summarize the existing knowledge base on the nature of misogynistic extremism, this scoping review analyzed publications within English-language peer-reviewed and gray literature sources. Seven electronic databases and citation indexes were systematically searched using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) checklist and charted using the 2020 PRISMA flow diagram. Inclusion criteria included English peer-reviewed articles and relevant gray literature publications, which contained the term "misogynistic extremism" and other closely related terms. No date restrictions were imposed. The search strategy initially yielded 475 publications. After exclusion of ineligible articles, 40 publications remained for synthesis. We found that misogynistic extremism is most frequently conceptualized in the context of misogynistic incels, male supremacism, far-right extremism, terrorism, and the black pill ideology. Policy recommendations include increased education among law enforcement and Countering and Preventing Violent Extremism experts on male supremacist violence and encouraging legal and educational mechanisms to bolster gender equality. Violence stemming from misogynistic worldviews must be addressed by directly acknowledging and challenging socially embedded systems of oppression such as white supremacy and cisheteropatriarchy.

Models of Gender Dysphoria Using Social Media Data for Use in Technology-Delivered Interventions: Machine Learning and Natural Language Processing Validation Study.

BACKGROUND: The optimal treatment for gender dysphoria is medical intervention, but many transgender and nonbinary people face significant treatment barriers when seeking help for gender dysphoria. When untreated, gender dysphoria is associated with depression, anxiety, suicidality, and substance misuse. Technology-delivered interventions for transgender and nonbinary people can be used discretely, safely, and flexibly, thereby reducing treatment barriers and increasing access to psychological interventions to manage distress that accompanies gender dysphoria. Technology-delivered interventions are beginning to incorporate machine learning (ML) and natural language processing (NLP) to automate intervention components and tailor intervention content. A critical step in using ML and NLP in technology-delivered interventions is demonstrating how accurately these methods model clinical constructs. OBJECTIVE: This study aimed to determine the preliminary effectiveness of modeling gender dysphoria with ML and NLP, using transgender and nonbinary people's social media data. METHODS: Overall, 6 ML models and 949 NLP-generated independent variables were used to model gender dysphoria from the text data of 1573 Reddit (Reddit Inc) posts created on transgender- and nonbinary-specific web-based forums. After developing a codebook grounded in clinical science, a research team of clinicians and students experienced in working with transgender and nonbinary clients used qualitative content analysis to determine whether gender dysphoria was present in each Reddit post (ie, the dependent variable). NLP (eg, n-grams, Linguistic Inquiry and Word Count, word embedding, sentiment, and transfer learning) was used to transform the linguistic content of each post into predictors for ML algorithms. A k-fold cross-validation was performed. Hyperparameters were tuned with random search. Feature selection was performed to demonstrate the relative importance of each NLP-generated independent variable in predicting gender dysphoria. Misclassified posts were analyzed to improve future modeling of gender dysphoria. RESULTS: Results indicated that a supervised ML algorithm (ie, optimized extreme gradient boosting [XGBoost]) modeled gender dysphoria with a high degree of accuracy (0.84), precision (0.83), and speed (1.23 seconds). Of the NLP-generated independent variables, Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) clinical keywords (eg, dysphoria and disorder) were most predictive of gender dysphoria. Misclassifications of gender dysphoria were common in posts that expressed uncertainty, featured a stressful experience unrelated to gender dysphoria, were incorrectly coded, expressed insufficient linguistic markers of gender dysphoria, described past experiences of gender dysphoria, showed evidence of identity exploration, expressed aspects of human sexuality unrelated to gender dysphoria, described socially based gender dysphoria, expressed strong affective or cognitive reactions unrelated to gender dysphoria, or discussed body image. CONCLUSIONS: Findings suggest that ML- and NLP-based models of gender dysphoria have significant potential to be integrated into technology-delivered interventions. The results contribute to the growing evidence on the importance of incorporating ML and NLP designs in clinical science, especially when studying marginalized populations.

Empirical networks for localized COVID-19 interventions using WiFi infrastructure at university campuses.

Infectious diseases, like COVID-19, pose serious challenges to university campuses, which typically adopt closure as a non-pharmaceutical intervention to control spread and ensure a gradual return to normalcy. Intervention policies, such as remote instruction (RI) where large classes are offered online, reduce potential contact but also have broad side-effects on campus by hampering the local economy, students' learning outcomes, and community wellbeing. In this paper, we demonstrate that university policymakers can mitigate these tradeoffs by leveraging anonymized data from their WiFi infrastructure to learn community mobility-a methodology we refer to as WiFi mobility models (WiMob). This approach enables policymakers to explore more granular policies like localized closures (LC). WiMob can construct contact networks that capture behavior in various spaces, highlighting new potential transmission pathways and temporal variation in contact behavior. Additionally, WiMob enables us to design LC policies that close super-spreader locations on campus. By simulating disease spread with contact networks from WiMob, we find that LC maintains the same reduction in cumulative infections as RI while showing greater reduction in peak infections and internal transmission. Moreover, LC reduces campus burden by closing fewer locations, forcing fewer students into completely online schedules, and requiring no additional isolation. WiMob can empower universities to conceive and assess a variety of closure policies to prevent future outbreaks.

Social network structure as a suicide prevention target.

PURPOSE: The structure of relationships in a social network affects the suicide risk of the people embedded within it. Although current interventions often modify the social perceptions (e.g., perceived support and sense of belonging) for people at elevated risk, few seek to directly modify the structure of their surrounding social networks. We show social network structure is a worthwhile intervention target in its own right. METHODS: A simple model illustrates the potential of interventions to modify social structure. The effect of these basic structural interventions on suicide risk is simulated and evaluated. Its results are briefly compared to emerging empirical findings for real network interventions. RESULTS: Even an intentionally simplified intervention on social network structure (i.e., random addition of social connections) is likely to be both effective and safe. Specifically, this illustrative intervention had a high probability of reducing the overall suicide risk, without increasing the risk of those who were healthy at baseline. It also frequently resolved stable, high-risk clusters of people at elevated risk. These illustrative results are generally consistent with emerging evidence from real social network interventions for suicide. CONCLUSION: Social network structure is a neglected, but valuable intervention target for suicide prevention.

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.

Leveraging Symptom Search Data to Understand Disparities in US Mental Health Care: Demographic Analysis of Search Engine Trace Data.

BACKGROUND: In the United States, 1 out of every 3 people lives in a mental health professional shortage area. Shortage areas tend to be rural, have higher levels of poverty, and have poor mental health outcomes. Previous work has demonstrated that these poor outcomes may arise from interactions between a lack of resources and lack of recognition of mental illness by medical professionals. OBJECTIVE: We aimed to understand the differences in how people in shortage and nonshortage areas search for information about mental health on the web. METHODS: We analyzed search engine log data related to health from 2017-2021 and examined the differences in mental health search behavior between shortage and nonshortage areas. We analyzed several axes of difference, including shortage versus nonshortage comparisons, urban versus rural comparisons, and temporal comparisons. RESULTS: We found specific differences in search behavior between shortage and nonshortage areas. In shortage areas, broader and more general mental health symptom categories, namely anxiety (mean 2.03%, SD 0.44%), depression (mean 1.15%, SD 0.27%), fatigue (mean 1.21%, SD 0.28%), and headache (mean 1.03%, SD 0.23%), were searched significantly more often (Q<.0003). In contrast, specific symptom categories and mental health disorders such as binge eating (mean 0.02%, SD 0.02%), psychosis (mean 0.37%, SD 0.06%), and attention-deficit/hyperactivity disorder (mean 0.77%, SD 0.10%) were searched significantly more often (Q<.0009) in nonshortage areas. Although suicide rates are consistently known to be higher in shortage and rural areas, we see that the rates of suicide-related searching are lower in shortage areas (mean 0.05%, SD 0.04%) than in nonshortage areas (mean 0.10%, SD 0.03%; Q<.0003), more so when a shortage area is rural (mean 0.024%, SD 0.029%; Q<2 × 10-12). CONCLUSIONS: This study demonstrates differences in how people from geographically marginalized groups search on the web for mental health. One main implication of this work is the influence that search engine ranking algorithms and interface design might have on the kinds of resources that individuals use when in distress. Our results support the idea that search engine algorithm designers should be conscientious of the role that structural factors play in expressions of distress and they should attempt to design search engine algorithms and interfaces to close gaps in care.

Cross-Platform Detection of Psychiatric Hospitalization via Social Media Data: Comparison Study.

BACKGROUND: Previous research has shown the feasibility of using machine learning models trained on social media data from a single platform (eg, Facebook or Twitter) to distinguish individuals either with a diagnosis of mental illness or experiencing an adverse outcome from healthy controls. However, the performance of such models on data from novel social media platforms unseen in the training data (eg, Instagram and TikTok) has not been investigated in previous literature. OBJECTIVE: Our study examined the feasibility of building machine learning classifiers that can effectively predict an upcoming psychiatric hospitalization given social media data from platforms unseen in the classifiers' training data despite the preliminary evidence on identity fragmentation on the investigated social media platforms. METHODS: Windowed timeline data of patients with a diagnosis of schizophrenia spectrum disorder before a known hospitalization event and healthy controls were gathered from 3 platforms: Facebook (254/268, 94.8% of participants), Twitter (51/268, 19% of participants), and Instagram (134/268, 50% of participants). We then used a 3 × 3 combinatorial binary classification design to train machine learning classifiers and evaluate their performance on testing data from all available platforms. We further compared results from models in intraplatform experiments (ie, training and testing data belonging to the same platform) to those from models in interplatform experiments (ie, training and testing data belonging to different platforms). Finally, we used Shapley Additive Explanation values to extract the top predictive features to explain and compare the underlying constructs that predict hospitalization on each platform. RESULTS: We found that models in intraplatform experiments on average achieved an F1-score of 0.72 (SD 0.07) in predicting a psychiatric hospitalization because of schizophrenia spectrum disorder, which is 68% higher than the average of models in interplatform experiments at an F1-score of 0.428 (SD 0.11). When investigating the key drivers for divergence in construct validities between models, an analysis of top features for the intraplatform models showed both low predictive feature overlap between the platforms and low pairwise rank correlation (<0.1) between the platforms' top feature rankings. Furthermore, low average cosine similarity of data between platforms within participants in comparison with the same measurement on data within platforms between participants points to evidence of identity fragmentation of participants between platforms. CONCLUSIONS: We demonstrated that models built on one platform's data to predict critical mental health treatment outcomes such as hospitalization do not generalize to another platform. In our case, this is because different social media platforms consistently reflect different segments of participants' identities. With the changing ecosystem of social media use among different demographic groups and as web-based identities continue to become fragmented across platforms, further research on holistic approaches to harnessing these diverse data sources is required.

Social media-based interventions for adolescent and young adult mental health: A scoping review.

Background: Mental health conditions are common among adolescents and young adults, yet few receive adequate mental health treatment. Many young people seek support and information online through social media, and report preferences for digital interventions. Thus, digital interventions deployed through social media have promise to reach a population not yet engaged in treatment, and at risk of worsening symptoms. Objective: In this scoping review, we aimed to identify and review empirical research on social media-based interventions aimed at improving adolescent and young adult mental health. A secondary objective was to identify the features and functionalities of platforms described as social media. Methods: Adhering to the PRISMA-ScR guidelines for scoping reviews, the search was conducted in PubMed MEDLINE; Embase Central Register of Controlled Trials (Wiley); PsycINFO (Ebsco); Scopus; Web of Science; IEEE Xplore; ACM Digital Library; and ClinicalTrials.gov from inception until November 2021. Studies were included if they involved adolescents or young adults (10-26 years of age) that meet clinical, or subclinical, levels of a mental health condition and include a pre- and post-assessment of mental health outcomes. Results: Among the 18,380 references identified, 15 met full inclusion criteria and were published between 2017 and 2021-this included four randomized controlled trials, seven non-randomized pre-post trials, and four were experimental or quasi-experimental designs. Just five studies were delivered through an existing social media site (Facebook or Pixtori), with the remainder focused on purpose-built networks. Three studies involved adolescents or young adults who self-reported a mental health condition, seven involved young people diagnosed with a mental health condition by a clinician or who scored above a clinical threshold on valid clinical measure, three involved college students without a mental health inclusion criterion, and two studies focused on young people with a cancer diagnosis. Conclusions: The review highlights innovations in the delivery of mental health interventions, provides preliminary evidence of the ability of social media interventions to improve mental health outcomes, and underscores the need for, and merit of, future work in this area. We discuss opportunities and challenges for future research, including the potential to leveragei existing peer networks, the use of just-in-time interventions, and scaling interventions to meet need.

From promise to practice: towards the realisation of AI-informed mental health care.

In this Series paper, we explore the promises and challenges of artificial intelligence (AI)-based precision medicine tools in mental health care from clinical, ethical, and regulatory perspectives. The real-world implementation of these tools is increasingly considered the prime solution for key issues in mental health, such as delayed, inaccurate, and inefficient care delivery. Similarly, machine-learning-based empirical strategies are becoming commonplace in psychiatric research because of their potential to adequately deconstruct the biopsychosocial complexity of mental health disorders, and hence to improve nosology of prognostic and preventive paradigms. However, the implementation steps needed to translate these promises into practice are currently hampered by multiple interacting challenges. These obstructions range from the current technology-distant state of clinical practice, over the lack of valid real-world databases required to feed data-intensive AI algorithms, to model development and validation considerations being disconnected from the core principles of clinical utility and ethical acceptability. In this Series paper, we provide recommendations on how these challenges could be addressed from an interdisciplinary perspective to pave the way towards a framework for mental health care, leveraging the combined strengths of human intelligence and AI.

The promise of a model-based psychiatry: building computational models of mental ill health.

Computational models have great potential to revolutionise psychiatry research and clinical practice. These models are now used across multiple subfields, including computational psychiatry and precision psychiatry. Their goals vary from understanding mechanisms underlying disorders to deriving reliable classification and personalised predictions. Rapid growth of new tools and data sources (eg, digital data, gamification, and social media) requires an understanding of the constraints and advantages of different modelling approaches in psychiatry. In this Series paper, we take a critical look at the range of computational models that are used in psychiatry and evaluate their advantages and disadvantages for different purposes and data sources. We describe mechanism-driven and mechanism-agnostic computational models and discuss how interpretability of models is crucial for clinical translation. Based on these evaluations, we provide recommendations on how to build computational models that are clinically useful.

Interventions to mitigate vaping misinformation: protocol for a scoping review.

BACKGROUND: The impact of misinformation about vapes' relative harms compared with smoking may lead to increased tobacco-related burden of disease. To date, no systematic efforts have been made to chart interventions that mitigate vaping-related misinformation. We plan to conduct a scoping review that seeks to fill gaps in the current knowledge of interventions that mitigate vaping-related misinformation. METHODS: A scoping review focusing on interventions that mitigate vaping-related misinformation will be conducted. We will search (no date restrictions) MEDLINE, Scopus, EMBASE, CINAHL, PsycINFO, Web of Science Core Collection, Global Health, ERIC, and Sociological Abstracts. Gray literature will be identified using Disaster Lit, Google Scholar, Open Science Framework, governmental websites, and preprint servers (e.g., EuropePMC, PsyArXiv, MedRxiv, JMIR Preprints). Study selection will conform to Joanna Briggs Institute Reviewers' Manual 2020 Methodology for JBI Scoping Reviews. Only English language, original studies will be considered for inclusion. Two reviewers will independently screen all citations, full-text articles, and abstract data. A narrative summary of findings will be conducted. Data analysis will involve quantitative (e.g., frequencies) and qualitative (e.g., content and thematic analysis) methods. Where possible, a single effect size of exposure to the mitigation of vaping-related misinformation will be calculated per sample. Similarly, where possible, each study will be coded for moderating characteristics to find and account for systematic differences in the size of the effect or outcome that is being analyzed. Quality will be appraised with the study quality assessment tools utilized by the National Heart, Lung, and Blood Institute. Findings will be subjected to several different publication bias tests: Egger's regression test, Begg and Mazumdar's ran correlation test, and generation of a funnel plot with effect sizes plotted against a corresponding standard error. DISCUSSION: Original research is urgently needed to design interventions to mitigate vaping-related misinformation. The planned scoping review will help to address this gap. SYSTEMATIC REVIEW REGISTRATION: Open Science Framework osf/io/hy3tk.

Classifying Minority Stress Disclosure on Social Media with Bidirectional Long Short-Term Memory.

Because of their stigmatized social status, sexual and gender minority (SGM; e.g., gay, transgender) people experience minority stress (i.e., identity-based stress arising from adverse social conditions). Given that minority stress is the leading framework for understanding health inequity among SGM people, researchers and clinicians need accurate methods to detect minority stress. Since social media fulfills important developmental, affiliative, and coping functions for SGM people, social media may be an ecologically valid channel for detecting minority stress. In this paper, we propose a bidirectional long short-term memory (BI-LSTM) network for classifying minority stress disclosed on Reddit. Our experiments on a dataset of 12,645 Reddit posts resulted in an average accuracy of 65%.

Examining the impact of sharing COVID-19 misinformation online on mental health.

Misinformation about the COVID-19 pandemic proliferated widely on social media platforms during the course of the health crisis. Experts have speculated that consuming misinformation online can potentially worsen the mental health of individuals, by causing heightened anxiety, stress, and even suicidal ideation. The present study aims to quantify the causal relationship between sharing misinformation, a strong indicator of consuming misinformation, and experiencing exacerbated anxiety. We conduct a large-scale observational study spanning over 80 million Twitter posts made by 76,985 Twitter users during an 18.5 month period. The results from this study demonstrate that users who shared COVID-19 misinformation experienced approximately two times additional increase in anxiety when compared to similar users who did not share misinformation. Socio-demographic analysis reveals that women, racial minorities, and individuals with lower levels of education in the United States experienced a disproportionately higher increase in anxiety when compared to the other users. These findings shed light on the mental health costs of consuming online misinformation. The work bears practical implications for social media platforms in curbing the adverse psychological impacts of misinformation, while also upholding the ethos of an online public sphere.

Interventions to mitigate COVID-19 misinformation: protocol for a scoping review.

BACKGROUND: The duration and impact of the COVID-19 pandemic depends in a large part on individual and societal actions which is influenced by the quality and salience of the information to which they are exposed. Unfortunately, COVID-19 misinformation has proliferated. To date, no systematic efforts have been made to evaluate interventions that mitigate COVID-19-related misinformation. We plan to conduct a scoping review that seeks to fill several of the gaps in the current knowledge of interventions that mitigate COVID-19-related misinformation. METHODS: A scoping review focusing on interventions that mitigate COVID-19 misinformation will be conducted. We will search (from January 2020 onwards) MEDLINE, EMBASE, CINAHL, PsycINFO, Web of Science Core Collection, Africa-Wide Information, Global Health, WHO Global Literature on Coronavirus Disease Database, WHO Global Index Medicus, and Sociological Abstracts. Gray literature will be identified using Disaster Lit, Google Scholar, Open Science Framework, governmental websites, and preprint servers (e.g., EuropePMC, PsyArXiv, MedRxiv, JMIR Preprints). Study selection will conform to Joanna Briggs Institute Reviewers' Manual 2020 Methodology for JBI Scoping Reviews. Only English language, original studies will be considered for inclusion. Two reviewers will independently screen all citations, full-text articles, and abstract data. A narrative summary of findings will be conducted. Data analysis will involve quantitative (e.g., frequencies) and qualitative (e.g., content and thematic analysis) methods. DISCUSSION: Original research is urgently needed to design interventions to mitigate COVID-19 misinformation. The planned scoping review will help to address this gap. SYSTEMATIC REVIEW REGISTRATIONS: Systematic Review Registration: Open Science Framework (osf/io/etw9d).

Interventions to Mitigate Vaping Misinformation: A Meta-Analysis.

The impact of misinformation about vapes' relative harms compared with smoking may lead to increased tobacco-related burden of disease and youth vaping. Unfortunately, vaping misinformation has proliferated. Despite growing attempts to mitigate vaping misinformation, there is still considerable ambiguity regarding the ability to effectively curb the negative impact of misinformation. To address this gap, we use a meta-analysis to evaluate the relative impact of interventions designed to mitigate vaping-related misinformation. We searched (from January 2020 till August 2021) various databases and gray literature. Only English language, original studies that employed experimental designs where participants were randomly assigned either to receive mitigating information or to a no-mitigation condition (either misinformation-only or neutral control) were included. Meta-analysis was conducted for the four eligible studies. The mean effect size of attempts to mitigate vaping misinformation was positive but not statistically significant (d = 0.383, 95% CI [-0.029, 0.796], p = .061, k = 5) with lack of evidence for publication bias. Given limited studies included, we were unable to determine factors affecting the efficacy of interventions. The limited focus on non-US studies and youth populations is concerning given the popularity of vaping in low- to middle-income countries (LMICs) and among youth. The findings of this meta-analysis describe the current state of the literature and prescribe specific recommendations to better address the proliferation of vaping misinformation, providing insights helpful in limiting the tobacco mortality burden and curtailing youth vaping.

Social Media Discussions Predict Mental Health Consultations on College Campuses.

The mental health of college students is a growing concern, and gauging the mental health needs of college students is difficult to assess in real-time and in scale. To address this gap, researchers and practitioners have encouraged the use of passive technologies. Social media is one such "passive sensor" that has shown potential as a viable "passive sensor" of mental health. However, the construct validity and in-practice reliability of computational assessments of mental health constructs with social media data remain largely unexplored. Towards this goal, we study how assessing the mental health of college students using social media data correspond with ground-truth data of on-campus mental health consultations. For a large U.S. public university, we obtained ground-truth data of on-campus mental health consultations between 2011-2016, and collected 66,000 posts from the university's Reddit community. We adopted machine learning and natural language methodologies to measure symptomatic mental health expressions of depression, anxiety, stress, suicidal ideation, and psychosis on the social media data. Seasonal auto-regressive integrated moving average (SARIMA) models of forecasting on-campus mental health consultations showed that incorporating social media data led to predictions with r = 0.86 and SMAPE = 13.30, outperforming models without social media data by 41%. Our language analyses revealed that social media discussions during high mental health consultations months consisted of discussions on academics and career, whereas months of low mental health consultations saliently show expressions of positive affect, collective identity, and socialization. This study reveals that social media data can improve our understanding of college students' mental health, particularly their mental health treatment needs.

Characterizing and Identifying the Prevalence of Web-Based Misinformation Relating to Medication for Opioid Use Disorder: Machine Learning Approach.

BACKGROUND: Expanding access to and use of medication for opioid use disorder (MOUD) is a key component of overdose prevention. An important barrier to the uptake of MOUD is exposure to inaccurate and potentially harmful health misinformation on social media or web-based forums where individuals commonly seek information. There is a significant need to devise computational techniques to describe the prevalence of web-based health misinformation related to MOUD to facilitate mitigation efforts. OBJECTIVE: By adopting a multidisciplinary, mixed methods strategy, this paper aims to present machine learning and natural language analysis approaches to identify the characteristics and prevalence of web-based misinformation related to MOUD to inform future prevention, treatment, and response efforts. METHODS: The team harnessed public social media posts and comments in the English language from Twitter (6,365,245 posts), YouTube (99,386 posts), Reddit (13,483,419 posts), and Drugs-Forum (5549 posts). Leveraging public health expert annotations on a sample of 2400 of these social media posts that were found to be semantically most similar to a variety of prevailing opioid use disorder-related myths based on representational learning, the team developed a supervised machine learning classifier. This classifier identified whether a post's language promoted one of the leading myths challenging addiction treatment: that the use of agonist therapy for MOUD is simply replacing one drug with another. Platform-level prevalence was calculated thereafter by machine labeling all unannotated posts with the classifier and noting the proportion of myth-indicative posts over all posts. RESULTS: Our results demonstrate promise in identifying social media postings that center on treatment myths about opioid use disorder with an accuracy of 91% and an area under the curve of 0.9, including how these discussions vary across platforms in terms of prevalence and linguistic characteristics, with the lowest prevalence on web-based health communities such as Reddit and Drugs-Forum and the highest on Twitter. Specifically, the prevalence of the stated MOUD myth ranged from 0.4% on web-based health communities to 0.9% on Twitter. CONCLUSIONS: This work provides one of the first large-scale assessments of a key MOUD-related myth across multiple social media platforms and highlights the feasibility and importance of ongoing assessment of health misinformation related to addiction treatment.

Suicide Risk and Protective Factors in Online Support Forum Posts: Annotation Scheme Development and Validation Study.

BACKGROUND: Online communities provide support for individuals looking for help with suicidal ideation and crisis. As community data are increasingly used to devise machine learning models to infer who might be at risk, there have been limited efforts to identify both risk and protective factors in web-based posts. These annotations can enrich and augment computational assessment approaches to identify appropriate intervention points, which are useful to public health professionals and suicide prevention researchers. OBJECTIVE: This qualitative study aims to develop a valid and reliable annotation scheme for evaluating risk and protective factors for suicidal ideation in posts in suicide crisis forums. METHODS: We designed a valid, reliable, and clinically grounded process for identifying risk and protective markers in social media data. This scheme draws on prior work on construct validity and the social sciences of measurement. We then applied the scheme to annotate 200 posts from r/SuicideWatch-a Reddit community focused on suicide crisis. RESULTS: We documented our results on producing an annotation scheme that is consistent with leading public health information coding schemes for suicide and advances attention to protective factors. Our study showed high internal validity, and we have presented results that indicate that our approach is consistent with findings from prior work. CONCLUSIONS: Our work formalizes a framework that incorporates construct validity into the development of annotation schemes for suicide risk on social media. This study furthers the understanding of risk and protective factors expressed in social media data. This may help public health programming to prevent suicide and computational social science research and investigations that rely on the quality of labels for downstream machine learning tasks.