Scaling Up Research on Drug Abuse and Addiction Through Social Media Big Data

The Together for Health - Virginia Research Program: A Multi-Modal Approach for Population Health Assessment

Background

The Together for Health-Virginia (T4H-VA) Research Program aimed to advance cancer prevention, education, and outreach in Virginia. Creating a representative and inclusive cohort is critical to the program's mission and quality of outcomes. The T4H-VA Research Program utilized a multi-modal sampling approach to improve population health assessment. The current study describes the technology-based, non-probability platform developed for this purpose and compares differences between the probability-based (mail-based) and non-probability-based (e-cohort) methods with respect to participant demographics, health characteristics, and health information and technology use.

Methods

T4H-VA is a research registry focusing on 54 counties within the Massey Comprehensive Cancer Center (MCCC) catchment area in Richmond, VA. Adult residents proficient in English were eligible. For the probability-based sampling, surveys were mailed to residents within the catchment area. For the non-probability sampling, an online study platform was developed and surveys were completed through the web/mobile app.

Results

Both cohorts fell short of recruitment goals. The study yielded 1158 participants (M=57, SD=16 years; 55.0% female; 72.1% White); 899 (77.6%) were sampled through the probability, mail-based approach. Participants who identified as "other" race were significantly less likely to be sampled by the non-probability method. Significant differences emerged, including health protective (greater moderate and high physical activity) and risk factors (greater alcohol consumption and personal history of cancer) in the non-probability, e-cohort relative to the probability sample. E-Cohort participants were significantly more likely to report using electronic health records.

Discussion

Overall difficulties in recruiting were caused, at least in part, by the onset of the COVID-19 pandemic and related factors. The e-cohort, which used exclusively digital recruitment strategies, fell significantly short of recruitment goals. This suggests in-person and mail-based strategies remain important for recruitment. Moreover, instead of favoring a singular approach, a combined approach to survey sampling may capitalize on the strengths of each sampling mode to increase diversity in sociodemographic and health risk characteristics.

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

BACKGROUND

Substance 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.

OBJECTIVE

By 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.

METHODS

We 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.

RESULTS

Our 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.

CONCLUSIONS

This 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.

Detecting Substance Use Disorder Using Social Media Data and the Dark Web: Time- and Knowledge-Aware Study

Background

Opioid and substance misuse has become a widespread problem in the United States, leading to the "opioid crisis." The relationship between substance misuse and mental health has been extensively studied, with one possible relationship being that substance misuse causes poor mental health. However, the lack of evidence on the relationship has resulted in opioids being largely inaccessible through legal means.

objectives

This study aims to analyze social media posts related to substance use and opioids being sold through cryptomarket listings. The study aims to use state-of-the-art deep learning models to generate sentiment and emotion from social media posts to understand users' perceptions of social media. The study also aims to investigate questions such as which synthetic opioids people are optimistic, neutral, or negative about; what kind of drugs induced fear and sorrow; what kind of drugs people love or are thankful about; which drugs people think negatively about; and which opioids cause little to no sentimental reaction.

Methods

The study used the drug abuse ontology and state-of-the-art deep learning models, including knowledge-aware Bidirectional Encoder Representations From Transformers-based models, to generate sentiment and emotion from social media posts related to substance use and opioids being sold through cryptomarket listings. The study crawled cryptomarket data and extracted posts for fentanyl, fentanyl analogs, and other novel synthetic opioids. The study performed topic analysis associated with the generated sentiments and emotions to understand which topics correlate with people's responses to various drugs. Additionally, the study analyzed time-aware neural models built on these features while considering historical sentiment and emotional activity of posts related to a drug.

Results

The study found that the most effective model performed well (statistically significant, with a macro-F1-score of 82.12 and recall of 83.58) in identifying substance use disorder. The study also found that there were varying levels of sentiment and emotion associated with different synthetic opioids, with some drugs eliciting more positive or negative responses than others. The study identified topics that correlated with people's responses to various drugs, such as pain relief, addiction, and withdrawal symptoms.

Conclusions

The study provides insight into users' perceptions of synthetic opioids based on sentiment and emotion expressed in social media posts. The study's findings can be used to inform interventions and policies aimed at reducing substance misuse and addressing the opioid crisis. The study demonstrates the potential of deep learning models for analyzing social media data to gain insights into public health issues.

Effects of internet and smartphone addiction on cognitive control in adolescents and young adults: A systematic review of fMRI studies

BACKGROUND

Internet Addiction (IA) refers to excessive or uncontrolled behaviors related to Internet access, causing impairment or distress. Similarly, Smartphone Addiction (SPA) involves the excessive use of smartphones, disrupting users' daily lives. Cognitive control plays a relevant role in the development of IA and SPA, with executive functions and rewards processing being particularly important.

OBJECTIVE

This study aims to provide a synthesis of scientific evidence on the differential effects of IA and SPA on cognition in young adults and adolescents, using fMRI.

METHODS

Relevant articles published between 2013 and May 2023 were identified following the PRISMA protocol.

RESULTS

Included studies (n = 21) revealed that both behavioral addictions were associated with impairments in cognitive control related to reward processing (ACC, insula, amygdala) and executive function (DLPFC, frontal, parietal lobes) in adolescents and young adults.

CONCLUSION

These findings provide evidence for neuroanatomical similarities between IA and SPA in young adults and adolescents related to executive functions and rewards processing. However, differential effects of SPA and IA on cognitive control were not found in this study. Future research should explore the relationship between these addictions with other cognitive domains to further expand our understanding within this field. While both IA and SPA involve the excessive use of online technology, SPA could involve a higher risk, given that smartphones allow internet connectivity anytime.

Insights from the Twittersphere: a cross-sectional study of public perceptions, usage patterns, and geographical differences of tweets discussing cocaine

Introduction

Cocaine abuse represents a major public health concern. The social perception of cocaine has been changing over the decades, a phenomenon closely tied to its patterns of use and abuse. Twitter is a valuable tool to understand the status of drug use and abuse globally. However, no specific studies discussing cocaine have been conducted on this platform.

Methods

111,508 English and Spanish tweets containing "cocaine" from 2018 to 2022 were analyzed. 550 were manually studied, and the largest subset underwent automated classification. Then, tweets related to cocaine were analyzed to examine their content, types of Twitter users, usage patterns, health effects, and personal experiences. Geolocation data was also considered to understand regional differences.

Results

A total of 71,844 classifiable tweets were obtained. Among these, 15.95% of users discussed the harm of cocaine consumption to health. Media outlets had the highest number of tweets (35.11%) and the most frequent theme was social/political denunciation (67.88%). Regarding the experience related to consumption, there are more tweets with a negative sentiment. The 9.03% of tweets explicitly mention frequent use of the drug. The continent with the highest number of tweets was America (55.44% of the total).

Discussion

The findings underscore the significance of cocaine as a current social and political issue, with a predominant focus on political and social denunciation in the majority of tweets. Notably, the study reveals a concentration of tweets from the United States and South American countries, reflecting the high prevalence of cocaine-related disorders and overdose cases in these regions. Alarmingly, the study highlights the trivialization of cocaine consumption on Twitter, accompanied by a misleading promotion of its health benefits, emphasizing the urgent need for targeted interventions and antidrug content on social media platforms. Finally, the unexpected advocacy for cocaine by healthcare professionals raises concerns about potential drug abuse within this demographic, warranting further investigation.

Prospects and challenges of online pharmacy in post-Covid world: A qualitative study of pharmacists' experiences in Ghana

Background

Online pharmacies continue to grow worldwide, especially in the wake of the COVID-19 pandemic. Ghana is experiencing this growth in an unprecedented way since its government initiated an online pharmacy pilot in December 2021, which was followed by the launch of the National Electronic Pharmacy Platform in July 2022. This pioneering initiative calls for extensive research with pharmacists to gain their perspectives. However, there is a dearth of such studies in the sub-Saharan African countries.

Objective

This study sought to understand how pharmacists in Ghana perceive online pharmacies in terms of the larger socio-cultural and policy implications, as well as the challenges they face in its implementation.

Methods

Using a qualitative research design, local licensed pharmacists were recruited through purposive sampling and by specifically combining the maximum variation and snowball sample techniques. Semi-structured interviews were conducted virtually with 21 pharmacists over the months of February and March 2022. The data were analyzed by using interpretive thematic analysis.

Results

Pharmacists perceived that online pharmacies would transform the pharmaceutical industry in Ghana by making it convenient for people to access medication easily and at a reasonable price, while also offering them privacy. However, concerns were expressed around existing poor infrastructure and inequities, low health literacy, and inadequate regulatory practices that could pose major challenges in the operation of this platform. Engagement of stakeholders was deemed essential for success.

Conclusion

Online pharmacy in Ghana and much of the developing world has the potential to transform and advance the pharmaceutical industry to better serve people. However, it could also lead to increased and irrational use of medications, if not properly regulated. Government, policy makers, and leaders in the field of digital health and pharmacy must also address poor infrastructure and inequities in digital access.

Benzodiazepine Boom: Tracking Etizolam, Pyrazolam, and Flubromazepam from Pre-UK Psychoactive Act 2016 to Present Using Analytical and Social Listening Techniques

INTRODUCTION

The designer benzodiazepine (DBZD) market continues to expand whilst evading regulatory controls. The widespread adoption of social media by pro-drug use communities encourages positive discussions around DBZD use/misuse, driving demand. This research addresses the evolution of three popular DBZDs, etizolam (E), flubromazepam (F), and pyrazolam (P), available on the drug market for over a decade, comparing the quantitative chemical analyses of tablet samples, purchased from the internet prior to the implementation of the Psychoactive Substances Act UK 2016, with the thematic netnographic analyses of social media content.

METHOD

Drug samples were purchased from the internet in early 2016. The characterisation of all drug batches were performed using UHPLC-MS and supported with 1H NMR. In addition, netnographic studies across the platforms X (formerly Twitter) and Reddit, between 2016-2023, were conducted. The latter was supported by both manual and artificial intelligence (AI)-driven thematic analyses, using numerous.ai and ChatGPT, of social media threads and discussions.

RESULTS

UHPLC-MS confirmed the expected drug in every sample, showing remarkable inter/intra batch variability across all batches (E = 13.8 ± 0.6 to 24.7 ± 0.9 mg; F = 4.0 ± 0.2 to 23.5 ± 0.8 mg; P = 5.2 ± 0.2 to 11.5 ± 0.4 mg). 1H NMR could not confirm etizolam as a lone compound in any etizolam batch. Thematic analyses showed etizolam dominated social media discussions (59% of all posts), with 24.2% of posts involving sale/purchase and 17.8% detailing new administration trends/poly-drug use scenarios. Artificial intelligence confirmed three of the top five trends identified manually.

CONCLUSIONS

Purity variability identified across all tested samples emphasises the increased potential health risks associated with DBZD consumption. We propose the global DBZD market is exacerbated by surface web social media discussions, recorded across X and Reddit. Despite the appearance of newer analogues, these three DBZDs remain prevalent and popularised. Reporting themes on harm/effects and new developments in poly-drug use trends, demand for DBZDs continues to grow, despite their potent nature and potential risk to life. It is proposed that greater controls and constant live monitoring of social media user content is warranted to drive active regulation strategies and targeted, effective, harm reduction strategies.

Patterns in negative emotions, sleep disorders, and temperature: Evidence from microblog big data

Existing studies have shown that temperature is related to mental illness and sleep disorders. However, few studies have explored the relationship between temperature and microblog negative emotions (MNE) and the predictive effect of MNE on sleep disorders. The present study elucidating the temperature patterns of MNE and sleep disorders, examines the predictive capability of these adverse emotions in precipitating sleep disorders, and operating within the schema of "climate-psychology-behavior". A negative binomial regression model (NBR) was formulated, amalgamating Temperature data, negative affective information procured from microblog, and sleep disorder records. Temperature and Apparent Air Temperature (AAT) were found to have a non-linear association with microblog negative emotions and sleep disorders, exhibiting a modest effect within a specified range, while extreme temperatures (both high and low) demonstrated substantial effects. In the constructed model, gender serves as a moderating factor, with females being more susceptible to temperature and AAT effects on MNE and sleep disorders than their male counterparts. Interestingly, AAT surfaced as a superior predictor compared to actual temperature. MNE were effective predictors of sleep disorders. Employing social media-centric models, as showcased in this study, augments the identification and prevention strategies targeting disease symptoms or pathologies within mental and public health domains.

Comparing the Discussion of Telehealth in Two Social Media Platforms: Social Listening Analysis

Background

Social media is used as a source of information and platform to discuss health care; however, there is little research on discussion of telehealth in social media. Past research has looked at individual platforms, but a comparison of discussion on two platforms (Reddit and Twitter) has not been performed. Understanding telehealth-related social media discourse and the differences between platforms may provide insights into how telehealth is characterized online and which platforms provide patient perspectives. The COVID-19 pandemic provides a unique case study to examine how social media users approached both Reddit and Twitter during an international health crisis. This study used natural language processing tools and two social media platforms to (1) characterize and contrast each platform's telehealth-related posts according to themes and (2) assess the frequency of telehealth and telehealth-related terms posts before and during the onset of the COVID-19 pandemic.

Methods

We collected 6 years (2016 through 2021) of social media posts from Twitter and Reddit. The themes of the corpus were extracted using hashtags, subreddits, and Latent Dirichlet Allocation (LDA) and were analyzed using descriptive statistics.

Results

Both Twitter and Reddit showed exponential growth in the use of the term "telehealth" and telehealth-related terms in early 2020. The use of telehealth-related terms and discussion of COVID-19 coincided in both social media sites; however, other themes were discussed, including how to use telehealth. Reddit LDA clusters showed greatest usage of "telehealth" when associated with using or suggesting telehealth for receiving therapy, counseling, or psychoanalysis while Twitter focused on sharing telehealth news, products, and services.

Discussion

Twitter and Reddit had extensive growth in the use of telehealth-related terms after the COVID-19 pandemic. Twitter and Reddit showed themes connecting COVID-19 to telehealth, especially in reference to services, therapy, and counseling, however, Reddit had more discussion suggesting use of telehealth services or requesting peer insights into how to use telehealth as compared with Twitter, which appeared more focused on telehealth as a business or product.

Emotional Expression on Social Media Support Forums for Substance Cessation: Observational Study of Text-Based Reddit Posts

BACKGROUND

Substance use disorder is characterized by distinct cognitive processes involved in emotion regulation as well as unique emotional experiences related to the relapsing cycle of drug use and recovery. Web-based communities and the posts they generate represent an unprecedented resource for studying subjective emotional experiences, capturing population types and sizes not typically available in the laboratory. Here, we mined text data from Reddit, a social media website that hosts discussions from pseudonymous users on specific topic forums, including forums for individuals who are trying to abstain from using drugs, to explore the putative specificity of the emotional experience of substance cessation.

OBJECTIVE

An important motivation for this study was to investigate transdiagnostic clues that could ultimately be used for mental health outreach. Specifically, we aimed to characterize the emotions associated with cessation of 3 major substances and compare them to emotional experiences reported in nonsubstance cessation posts, including on forums related to psychiatric conditions of high comorbidity with addiction.

METHODS

Raw text from 2 million posts made, respectively, in the fall of 2020 (discovery data set) and fall of 2019 (replication data set) were obtained from 394 forums hosted by Reddit through the application programming interface. We quantified emotion word frequencies in 3 substance cessation forums for alcohol, nicotine, and cannabis topic categories and performed comparisons with general forums. Emotion word frequencies were classified into distinct categories and represented as a multidimensional emotion vector for each forum. We further quantified the degree of emotional resemblance between different forums by computing cosine similarity on these vectorized representations. For substance cessation posts with self-reported time since last use, we explored changes in the use of emotion words as a function of abstinence duration.

RESULTS

Compared to posts from general forums, substance cessation posts showed more expressions of anxiety, disgust, pride, and gratitude words. "Anxiety" emotion words were attenuated for abstinence durations >100 days compared to shorter durations (t12=3.08, 2-tailed; P=.001). The cosine similarity analysis identified an emotion profile preferentially expressed in the cessation posts across substances, with lesser but still prominent similarities to posts about social anxiety and attention-deficit/hyperactivity disorder. These results were replicated in the 2019 (pre-COVID-19) data and were distinct from control analyses using nonemotion words.

CONCLUSIONS

We identified a unique subjective experience phenotype of emotions associated with the cessation of 3 major substances, replicable across 2 time periods, with changes as a function of abstinence duration. Although to a lesser extent, this phenotype also quantifiably resembled the emotion phenomenology of other relevant subjective experiences (social anxiety and attention-deficit/hyperactivity disorder). Taken together, these transdiagnostic results suggest a novel approach for the future identification of at-risk populations, allowing for the development and deployment of specific and timely interventions.

The evolution of Big Data in neuroscience and neurology

Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40537-023-00751-2.

Barriers to opioid use disorder treatment: A comparison of self-reported information from social media with barriers found in literature

Introduction

Medications such as buprenorphine and methadone are effective for treating opioid use disorder (OUD), but many patients face barriers related to treatment and access. We analyzed two sources of data-social media and published literature-to categorize and quantify such barriers.

Methods

In this mixed methods study, we analyzed social media (Reddit) posts from three OUD-related forums (subreddits): r/suboxone, r/Methadone, and r/naltrexone. We applied natural language processing to identify posts relevant to treatment barriers, categorized them into insurance- and non-insurance-related, and manually subcategorized them into fine-grained topics. For comparison, we used substance use-, OUD- and barrier-related keywords to identify relevant articles from PubMed published between 2006 and 2022. We searched publications for language expressing fear of barriers, and hesitation or disinterest in medication treatment because of barriers, paying particular attention to the affected population groups described.

Results

On social media, the top three insurance-related barriers included having no insurance (22.5%), insurance not covering OUD treatment (24.7%), and general difficulties of using insurance for OUD treatment (38.2%); while the top two non-insurance-related barriers included stigma (47.6%), and financial difficulties (26.2%). For published literature, stigma was the most prominently reported barrier, occurring in 78.9% of the publications reviewed, followed by financial and/or logistical issues to receiving medication treatment (73.7%), gender-specific barriers (36.8%), and fear (31.5%).

Conclusion

The stigma associated with OUD and/or seeking treatment and insurance/cost are the two most common types of barriers reported in the two sources combined. Harm reduction efforts addressing barriers to recovery may benefit from leveraging multiple data sources.

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.

Biomedical Research on Substances of Abuse: The Italian Case Study

Substances of abuse have the potential to cause addiction, habituation or altered consciousness. Most of the research on these substances focuses on addiction, and is carried out through observational and clinical studies on humans, or experimental studies on animals. The transposition of the EU Directive 2010/63 into Italian law in 2014 (IT Law 2014/26) includes a ban on the use of animals for research on substances of abuse. Since then, in Italy, public debate has continued on the topic, while the application of the Article prohibiting animal research in this area has been postponed every couple of years. In the light of this debate, we briefly review a range of methodologies - including animal and non-animal, as well as patient or population-based studies - that have been employed to address the biochemical, neurobiological, toxicological, clinical and behavioural effects of substances of abuse and their dependency. We then discuss the implications of the Italian ban on the use of animals for such research, proposing concrete and evidence-based solutions to allow scientists to pursue high-quality basic and translational studies within the boundaries of the regulatory and legislative framework.

The role of social media during the COVID-19 pandemic: Salvaging its 'power' for positive social behaviour change in Africa

The ongoing coronavirus disease 2019 (COVID-19) pandemic remains a significant global public health crisis. The unique evolution of the COVID-19 pandemic has seen social media emerging and growing into an important vehicle for rapid information dissemination. This has in turn given rise to multiple sources of information, leading to what has come to be known as ‘infodemic’, associated with the plethora of misinformation and conspiracy theories. In this perspective, we explore the growth of the social media industry and the impact it has had during the ongoing COVID-19 crisis. We argue that while the multiple information pieces circulating on social media cause misinformation and panic, this might not necessarily and in all the cases influence sustained behaviours in the target population groups. We offer suggestions on how the power of social media can be harnessed and integrated into social and public health for a better digital balance for communication for development.

Spontaneously generated online patient experience data - how and why is it being used in health research: an umbrella scoping review

PURPOSE

Social media has led to fundamental changes in the way that people look for and share health related information. There is increasing interest in using this spontaneously generated patient experience data as a data source for health research. The aim was to summarise the state of the art regarding how and why SGOPE data has been used in health research. We determined the sites and platforms used as data sources, the purposes of the studies, the tools and methods being used, and any identified research gaps.

METHODS

A scoping umbrella review was conducted looking at review papers from 2015 to Jan 2021 that studied the use of SGOPE data for health research. Using keyword searches we identified 1759 papers from which we included 58 relevant studies in our review.

RESULTS

Data was used from many individual general or health specific platforms, although Twitter was the most widely used data source. The most frequent purposes were surveillance based, tracking infectious disease, adverse event identification and mental health triaging. Despite the developments in machine learning the reviews included lots of small qualitative studies. Most NLP used supervised methods for sentiment analysis and classification. Very early days, methods need development. Methods not being explained. Disciplinary differences - accuracy tweaks vs application. There is little evidence of any work that either compares the results in both methods on the same data set or brings the ideas together.

CONCLUSION

Tools, methods, and techniques are still at an early stage of development, but strong consensus exists that this data source will become very important to patient centred health research.

Crowdsourcing for Machine Learning in Public Health Surveillance: Lessons Learned From Amazon Mechanical Turk

Background

Crowdsourcing services, such as Amazon Mechanical Turk (AMT), allow researchers to use the collective intelligence of a wide range of web users for labor-intensive tasks. As the manual verification of the quality of the collected results is difficult because of the large volume of data and the quick turnaround time of the process, many questions remain to be explored regarding the reliability of these resources for developing digital public health systems.

Objective

This study aims to explore and evaluate the application of crowdsourcing, generally, and AMT, specifically, for developing digital public health surveillance systems.

Methods

We collected 296,166 crowd-generated labels for 98,722 tweets, labeled by 610 AMT workers, to develop machine learning (ML) models for detecting behaviors related to physical activity, sedentary behavior, and sleep quality among Twitter users. To infer the ground truth labels and explore the quality of these labels, we studied 4 statistical consensus methods that are agnostic of task features and only focus on worker labeling behavior. Moreover, to model the meta-information associated with each labeling task and leverage the potential of context-sensitive data in the truth inference process, we developed 7 ML models, including traditional classifiers (offline and active), a deep learning–based classification model, and a hybrid convolutional neural network model.

Results

Although most crowdsourcing-based studies in public health have often equated majority vote with quality, the results of our study using a truth set of 9000 manually labeled tweets showed that consensus-based inference models mask underlying uncertainty in data and overlook the importance of task meta-information. Our evaluations across 3 physical activity, sedentary behavior, and sleep quality data sets showed that truth inference is a context-sensitive process, and none of the methods studied in this paper were consistently superior to others in predicting the truth label. We also found that the performance of the ML models trained on crowd-labeled data was sensitive to the quality of these labels, and poor-quality labels led to incorrect assessment of these models. Finally, we have provided a set of practical recommendations to improve the quality and reliability of crowdsourced data.

Conclusions

Our findings indicate the importance of the quality of crowd-generated labels in developing ML models designed for decision-making purposes, such as public health surveillance decisions. A combination of inference models outlined and analyzed in this study could be used to quantitatively measure and improve the quality of crowd-generated labels for training ML models.

Adolescent Health on Social Media and the Mentorship of Youth Investigators: Five Content Analysis Studies Conducted by Youth Investigators

Although the literature on adolescent health includes studies that incorporate youth perspectives via a participatory design, research that is designed, conducted, and presented by youth remains absent. This paper presents the work of 5 youth investigators on the intersecting topics of adolescent health and social media. Each of these youths was equipped with tools, knowledge, and mentorship for scientifically evaluating a research question. The youths developed a research question that aligned with their interests and filled a gap that they identified in the literature. The youths, whose projects are featured in this paper, designed and conducted their own research project, drafted their own manuscript, and revised and resubmitted a draft based on reviewer input. Each youth worked with a research mentor; however, the research questions, study designs, and suggestions for future research were their own.

Using big data for evaluating development outcomes: A systematic map

BACKGROUND

Policy makers need access to reliable data to monitor and evaluate the progress of development outcomes and targets such as sustainable development outcomes (SDGs). However, significant data and evidence gaps remain. Lack of resources, limited capacity within governments and logistical difficulties in collecting data are some of the reasons for the data gaps. Big data-that is digitally generated, passively produced and automatically collected-offers a great potential for answering some of the data needs. Satellite and sensors, mobile phone call detail records, online transactions and search data, and social media are some of the examples of big data. Integrating big data with the traditional household surveys and administrative data can complement data availability, quality, granularity, accuracy and frequency, and help measure development outcomes temporally and spatially in a number of new ways.The study maps different sources of big data onto development outcomes (based on SDGs) to identify current evidence base, use and the gaps. The map provides a visual overview of existing and ongoing studies. This study also discusses the risks, biases and ethical challenges in using big data for measuring and evaluating development outcomes. The study is a valuable resource for evaluators, researchers, funders, policymakers and practitioners in their effort to contributing to evidence informed policy making and in achieving the SDGs.

OBJECTIVES

Identify and appraise rigorous impact evaluations (IEs), systematic reviews and the studies that have innovatively used big data to measure any development outcomes with special reference to difficult contexts.

SEARCH METHODS

A number of general and specialised data bases and reporsitories of organisations were searched using keywords related to big data by an information specialist.

SELECTION CRITERIA

The studies were selected on basis of whether they used big data sources to measure or evaluate development outcomes.

DATA COLLECTION AND ANALYSIS

Data collection was conducted using a data extraction tool and all extracted data was entered into excel and then analysed using Stata. The data analysis involved looking at trends and descriptive statistics only.

MAIN RESULTS

The search yielded over 17,000 records, which we then screened down to 437 studies which became the foundation of our systematic map. We found that overall, there is a sizable and rapidly growing number of measurement studies using big data but a much smaller number of IEs. We also see that the bulk of the big data sources are machine-generated (mostly satellites) represented in the light blue. We find that satellite data was used in over 70% of the measurement studies and in over 80% of the IEs.

AUTHORS' CONCLUSIONS

This map gives us a sense that there is a lot of work being done to develop appropriate measures using big data which could subsequently be used in IEs. Information on costs, ethics, transparency is lacking in the studies and more work is needed in this area to understand the efficacies related to the use of big data. There are a number of outcomes which are not being studied using big data, either due to the lack to applicability such as education or due to lack of awareness about the new methods and data sources. The map points to a number of gaps as well as opportunities where future researchers can conduct research.

Regional variation in discussion of opioids on social media

BACKGROUND

New data sources and analysis methods are urgently needed to improve opioid surveillance and prevent potential overdose. Social media data is one potential data source that might be used and integrated to address this issue. Objective: This study explored opioid-related topics discussed across geographical regions of varying population sizes to determine whether social media data might inform opioid surveillance. Methods: Between March 17th to July 17th, 2020, we collected tweets (N = 19,721) mentioning opioid-related keywords across seven cities within the United States. Results: Results found that opioid-related keywords were distributed as follows: New York (29%), Los Angeles (23%), Chicago (18%), Atlanta (18%), San Francisco (8%), Iowa (3%), and Orange County, CA (1%). We also found regional differences in the types of opioids and topics mentioned. Conclusions: Findings suggest the feasibility of using opioid-related social media data to inform surveillance efforts, as well as potential regional and time-varying differences in topics discussed.

Virtual Engagement in a Social Media Community of Mothers With Substance Use Disorders: Content Analysis

BACKGROUND

Co-occurring substance use disorder is common among pregnant and parenting women with mental illness, but their engagement with and utilization of relevant services and treatment is low. Social media has the potential to convey benefits and facilitate engagement among this target group.

OBJECTIVE

This study aimed to explore the reach and engagement of specific social media posts among pregnant women and mothers with substance use disorders.

METHODS

Eighteen posts providing content related to substance use (cannabis, opioids, or alcohol), varying in type of content (informational or experiential) and target (policy-, practice-, or perception-related), were posted in a closed Facebook community page comprising over 33,000 pregnant women and mothers between May 2019 and October 2019.

RESULTS

The overall level of reach of these Facebook posts ranged from 453 to 3045 community members. Engagement levels, measured via the number of likes, comments, or posts shared, varied based on the type of post content (ie, informational or experiential).

CONCLUSIONS

Participation in a virtual community via social media platforms can facilitate engagement among pregnant women and mothers with mental illness by communicating relevant information about substance use, as well as potentially promoting awareness of, access to, and engagement with treatment services.

Patterns of Routes of Administration and Drug Tampering for Nonmedical Opioid Consumption: Data Mining and Content Analysis of Reddit Discussions

BACKGROUND

The complex unfolding of the US opioid epidemic in the last 20 years has been the subject of a large body of medical and pharmacological research, and it has sparked a multidisciplinary discussion on how to implement interventions and policies to effectively control its impact on public health.

OBJECTIVE

This study leverages Reddit, a social media platform, as the primary data source to investigate the opioid crisis. We aimed to find a large cohort of Reddit users interested in discussing the use of opioids, trace the temporal evolution of their interest, and extensively characterize patterns of the nonmedical consumption of opioids, with a focus on routes of administration and drug tampering.

METHODS

We used a semiautomatic information retrieval algorithm to identify subreddits discussing nonmedical opioid consumption and developed a methodology based on word embedding to find alternative colloquial and nonmedical terms referring to opioid substances, routes of administration, and drug-tampering methods. We modeled the preferences of adoption of substances and routes of administration, estimating their prevalence and temporal unfolding. Ultimately, through the evaluation of odds ratios based on co-mentions, we measured the strength of association between opioid substances, routes of administration, and drug tampering.

RESULTS

We identified 32 subreddits discussing nonmedical opioid usage from 2014 to 2018 and observed the evolution of interest among over 86,000 Reddit users potentially involved in firsthand opioid usage. We learned the language model of opioid consumption and provided alternative vocabularies for opioid substances, routes of administration, and drug tampering. A data-driven taxonomy of nonmedical routes of administration was proposed. We modeled the temporal evolution of interest in opioid consumption by ranking the popularity of the adoption of opioid substances and routes of administration, observing relevant trends, such as the surge in synthetic opioids like fentanyl and an increasing interest in rectal administration. In addition, we measured the strength of association between drug tampering, routes of administration, and substance consumption, finding evidence of understudied abusive behaviors, like chewing fentanyl patches and dissolving buprenorphine sublingually.

CONCLUSIONS

This work investigated some important consumption-related aspects of the opioid epidemic using Reddit data. We believe that our approach may provide a novel perspective for a more comprehensive understanding of nonmedical abuse of opioids substances and inform the prevention, treatment, and control of the public health effects.

Associations Between Substance Use and Instagram Participation to Inform Social Network-Based Screening Models: Multimodal Cross-Sectional Study

BACKGROUND

Technology-based computational strategies that leverage social network site (SNS) data to detect substance use are promising screening tools but rely on the presence of sufficient data to detect risk if it is present. A better understanding of the association between substance use and SNS participation may inform the utility of these technology-based screening tools.

OBJECTIVE

This paper aims to examine associations between substance use and Instagram posts and to test whether such associations differ as a function of age, gender, and race/ethnicity.

METHODS

Participants with an Instagram account were recruited primarily via Clickworker (N=3117). With participant permission and Instagram's approval, participants' Instagram photo posts were downloaded with an application program interface. Participants' past-year substance use was measured with an adapted version of the National Institute on Drug Abuse Quick Screen. At-risk drinking was defined as at least one past-year instance having "had more than a few alcoholic drinks a day," drug use was defined as any use of nonprescription drugs, and prescription drug use was defined as any nonmedical use of prescription medications. We used logistic regression to examine the associations between substance use and any Instagram posts and negative binomial regression to examine the associations between substance use and number of Instagram posts. We examined whether age (18-25, 26-38, 39+ years), gender, and race/ethnicity moderated associations in both logistic and negative binomial models. All differences noted were significant at the .05 level.

RESULTS

Compared with no at-risk drinking, any at-risk drinking was associated with both a higher likelihood of any Instagram posts and a higher number of posts, except among Hispanic/Latino individuals, in whom at-risk drinking was associated with a similar number of posts. Compared with no drug use, any drug use was associated with a higher likelihood of any posts but was associated with a similar number of posts. Compared with no prescription drug use, any prescription drug use was associated with a similar likelihood of any posts and was associated with a lower number of posts only among those aged 39 years and older. Of note, main effects showed that being female compared with being male and being Hispanic/Latino compared with being White were significantly associated with both a greater likelihood of any posts and a greater number of posts.

CONCLUSIONS

Researchers developing computational substance use risk detection models using Instagram or other SNS data may wish to consider our findings showing that at-risk drinking and drug use were positively associated with Instagram participation, while prescription drug use was negatively associated with Instagram participation for middle- and older-aged adults. As more is learned about SNS behaviors among those who use substances, researchers may be better positioned to successfully design and interpret innovative risk detection approaches.

Sentiment Analysis Methods for HPV VaccinesRelated Tweets Based on Transfer Learning

The widespread use of social media provides a large amount of data for public sentimentanalysis. Based on social media data, researchers can study public opinions on humanpapillomavirus (HPV) vaccines on social media using machine learning-based approaches that willhelp us understand the reasons behind the low vaccine coverage. However, social media data isusually unannotated, and data annotation is costly. The lack of an abundant annotated dataset limitsthe application of deep learning methods in effectively training models. To tackle this problem, wepropose three transfer learning approaches to analyze the public sentiment on HPV vaccines onTwitter. One was transferring static embeddings and embeddings from language models (ELMo)and then processing by bidirectional gated recurrent unit with attention (BiGRU-Att), called DWEBiGRU-Att. The others were fine-tuning pre-trained models with limited annotated data, called finetuninggenerative pre-training (GPT) and fine-tuning bidirectional encoder representations fromtransformers (BERT). The fine-tuned GPT model was built on the pre-trained generative pretraining(GPT) model. The fine-tuned BERT model was constructed with BERT model. Theexperimental results on the HPV dataset demonstrated the efficacy of the three methods in thesentiment analysis of the HPV vaccination task. The experimental results on the HPV datasetdemonstrated the efficacy of the methods in the sentiment analysis of the HPV vaccination task. Thefine-tuned BERT model outperforms all other methods. It can help to find strategies to improvevaccine uptake.

Social Media Text Mining Framework for Drug Abuse: Development and Validation Study With an Opioid Crisis Case Analysis

Background

Social media are considered promising and viable sources of data for gaining insights into various disease conditions and patients’ attitudes, behaviors, and medications. They can be used to recognize communication and behavioral themes of problematic use of prescription drugs. However, mining and analyzing social media data have challenges and limitations related to topic deduction and data quality. As a result, we need a structured approach to analyze social media content related to drug abuse in a manner that can mitigate the challenges and limitations surrounding the use of such data.

Objective

This study aimed to develop and evaluate a framework for mining and analyzing social media content related to drug abuse. The framework is designed to mitigate challenges and limitations related to topic deduction and data quality in social media data analytics for drug abuse.

Methods

The proposed framework started with defining different terms related to the keywords, categories, and characteristics of the topic of interest. We then used the Crimson Hexagon platform to collect data based on a search query informed by a drug abuse ontology developed using the identified terms. We subsequently preprocessed the data and examined the quality using an evaluation matrix. Finally, a suitable data analysis approach could be used to analyze the collected data.

Results

The framework was evaluated using the opioid epidemic as a drug abuse case analysis. We demonstrated the applicability of the proposed framework to identify public concerns toward the opioid epidemic and the most discussed topics on social media related to opioids. The results from the case analysis showed that the framework could improve the discovery and identification of topics in social media domains characterized by a plethora of highly diverse terms and lack of a commonly available dictionary or language by the community, such as in the case of opioid and drug abuse.

Conclusions

The proposed framework addressed the challenges related to topic detection and data quality. We demonstrated the applicability of the proposed framework to identify the common concerns toward the opioid epidemic and the most discussed topics on social media related to opioids.

Mining Social Media Data for Biomedical Signals and Health-Related Behavior

Social media data have been increasingly used to study biomedical and health-related phenomena. From cohort-level discussions of a condition to population-level analyses of sentiment, social media have provided scientists with unprecedented amounts of data to study human behavior associated with a variety of health conditions and medical treatments. Here we review recent work in mining social media for biomedical, epidemiological, and social phenomena information relevant to the multilevel complexity of human health. We pay particular attention to topics where social media data analysis has shown the most progress, including pharmacovigilance and sentiment analysis, especially for mental health. We also discuss a variety of innovative uses of social media data for health-related applications as well as important limitations of social media data access and use.

Harnessing the power of social media: how can it help in axial spondyloarthritis research?

PURPOSE OF REVIEW

Axial spondyloarthritis (axSpA) is a chronic inflammatory rheumatic disease that is relatively unknown among the general public. Most patients with axSpA are young or middle-aged adults and more likely to use some social media. This review highlights trends in the application of social media and different ways in which these tools do already or may benefit clinical research, delivery of care, and education in rheumatology, particularly in the field of axSpA.

RECENT FINDINGS

This article discusses four areas in the biomedical field that social media has infused with novel ideas: (i) the use of patient-generated health data from social media to learn about their disease experience, (ii) delivering health education and interventions, (iii) recruiting study participants, and (iv) reform, transfer, and disseminate medical education. We conclude with promising studies in rheumatology that have incorporated social media and suggestions for future directions.

SUMMARY

Rheumatologists now have the opportunity to use social media and innovate on many aspects of their practice. We propose further exploration of multiple ways in which social media might help with the identification, diagnosis, education, and research study enrollment of axSpA patients. However, standardization in study design, reporting, and managing ethical and regulatory aspects will be required to take full advantage of this opportunity.

Data Mining and Content Analysis of the Chinese Social Media Platform Weibo During the Early COVID-19 Outbreak: Retrospective Observational Infoveillance Study

BACKGROUND

The coronavirus disease (COVID-19) pandemic, which began in Wuhan, China in December 2019, is rapidly spreading worldwide with over 1.9 million cases as of mid-April 2020. Infoveillance approaches using social media can help characterize disease distribution and public knowledge, attitudes, and behaviors critical to the early stages of an outbreak.

OBJECTIVE

The aim of this study is to conduct a quantitative and qualitative assessment of Chinese social media posts originating in Wuhan City on the Chinese microblogging platform Weibo during the early stages of the COVID-19 outbreak.

METHODS

Chinese-language messages from Wuhan were collected for 39 days between December 23, 2019, and January 30, 2020, on Weibo. For quantitative analysis, the total daily cases of COVID-19 in Wuhan were obtained from the Chinese National Health Commission, and a linear regression model was used to determine if Weibo COVID-19 posts were predictive of the number of cases reported. Qualitative content analysis and an inductive manual coding approach were used to identify parent classifications of news and user-generated COVID-19 topics.

RESULTS

A total of 115,299 Weibo posts were collected during the study time frame consisting of an average of 2956 posts per day (minimum 0, maximum 13,587). Quantitative analysis found a positive correlation between the number of Weibo posts and the number of reported cases from Wuhan, with approximately 10 more COVID-19 cases per 40 social media posts (P<.001). This effect size was also larger than what was observed for the rest of China excluding Hubei Province (where Wuhan is the capital city) and held when comparing the number of Weibo posts to the incidence proportion of cases in Hubei Province. Qualitative analysis of 11,893 posts during the first 21 days of the study period with COVID-19-related posts uncovered four parent classifications including Weibo discussions about the causative agent of the disease, changing epidemiological characteristics of the outbreak, public reaction to outbreak control and response measures, and other topics. Generally, these themes also exhibited public uncertainty and changing knowledge and attitudes about COVID-19, including posts exhibiting both protective and higher-risk behaviors.

CONCLUSIONS

The results of this study provide initial insight into the origins of the COVID-19 outbreak based on quantitative and qualitative analysis of Chinese social media data at the initial epicenter in Wuhan City. Future studies should continue to explore the utility of social media data to predict COVID-19 disease severity, measure public reaction and behavior, and evaluate effectiveness of outbreak communication.

Fuzzy Weighted Clustering Method for Numerical Attributes of Communication Big Data Based on Cloud Computing

It is necessary to optimize clustering processing of communication big data numerical attribute feature information in order to improve the ability of numerical attribute mining of communication big data, and thus a big data clustering algorithm based on cloud computing was proposed. The cloud extended distributed feature fitting method was used to process the numerical attribute linear programming of communication big data, and the mutual information feature quantity of communication big data numerical attribute was extracted. Combined with fuzzy C-means clustering and linear regression analysis, the statistical analysis of big data numerical attribute feature information was carried out, and the associated attribute sample set of communication big data numerical attribute cloud grid distribution was constructed. Cloud computing and adaptive quantitative recurrent classifiers were used for data classification, and block template matching and multi-sensor information fusion were combined to search the clustering center automatically to improve the convergence of clustering. The simulation results show that, after the application of this method, the information fusion performance of the clustering process was better, the automatic searching ability of the data clustering center was stronger, the frequency domain equalization control effect was good, the bit error rate was low, the energy consumption was small, and the ability of fuzzy weighted clustering retrieval of numerical attributes of communication big data was effectively improved.

Novel Opioids: Systematic Web Crawling Within the e-Psychonauts' Scenario

Background

A wide range of novel psychoactive substances (NPSs) are regularly searched and discussed online by e-psychonauts. Among NPSs, the range of prescription/non-prescription opioids (fentanyl and non-fentanyl analogs) and herbal derivatives currently represents a challenge for governments and clinicians.

Methods

Using a web crawler (i.e., NPS.Finder^®), the present study aimed at assessing psychonaut fora/platforms to better understand the online situation regarding opioids.

Results

The open-web crawling/navigating software identified some 426 opioids, including 234 fentanyl analogs. Of these, 176 substances (162 were very potent fentanyls, including two ohmefentanyl and seven carfentanyl analogs) were not listed in either international or European NPS databases.

Conclusion

A web crawling approach helped in identifying a large number, indeed higher than that listed by European/international agencies, of unknown opioids likely to possess a significant misuse potential. Most of these novel/emerging substances are still relatively unknown. This is a reason of concern; each of these analogs potentially presents with different toxicodynamic profiles, and there is a lack of docking, preclinical, and clinical observations. Strengthening multidisciplinary collaboration between clinicians and bioinformatics may prove useful in better assessing public health risks associated with opioids.

Tapentadol abuse and dependence in India

BACKGROUND

Tapentadol is a synthetic opioid analgesic available in India since 2011. International evidence suggests a low risk of abuse and diversion. Our study aims to question this perception in Indian context.

METHOD

We report the trend and profile of Tapentadol abuse cases that were treated at a tertiary level addiction treatment centre in southern India. We also describe the ease of repurposing oral tablets of Tapentadol into an injection. At the national level, we have examined the temporal and spatial trends of online interest in Tapentadol and compared it with a non-opioid drug Ilaprazole and an opioid drug Tramadol using Google Trends. We have used the National Drug Use Survey 2019 to illustrate the regional data.

RESULTS

23 cases of Tapentadol abuse sought treatment between 01/01/2011 and 30/08/2019. In last one year, the number of cases has more than doubled. A majority (N = 19, 83 %) of cases had intravenous Tapentadol abuse, needle sharing and 60 % were diagnosed with Hepatitis C. Tapentadol is attracting new users (N = 13, 56.5 %) as well as replacing other opioids (N = 10, 43.5 %) amongst drug users. Tapentadol has received more online interest than Ilaprazole. Temporal and spatial trends of online interest in Tapentadol parallel Tramadol. States with high prevalence of opioid users have shown high online interest in both opioid drugs.

CONCLUSION

Tapentadol is being widely abused, and urgent regulatory measures are required.

Methods for Population Research on Substance Use and Consequences

This chapter reviews the array of methods used in contemporary research on population-level research on substance use and its consequences. We argue that there are critical questions that can best - or in some cases, only - be addressed at the level of a population. We then describe the major categories of data collection methods used in population research, including surveys, ecological momentary assessment, administrative data, audit methods, and unobtrusive assessment of substance use. Two categories of measures are then discussed: measures of an individual's use of substances and related problems and measures of harm to others caused by one's use. We then review factors that may be considered causes or correlates of substance use and consequences, including both individual and environmental factors. We close with a few thoughts on the accumulation of knowledge and its translation to policy and practice.

Drug Abuse Research Trend Investigation with Text Mining

Drug abuse poses great physical and psychological harm to humans, thereby attracting scholarly attention. It often requires experience and time for a researcher, just entering this field, to find an appropriate method to study drug abuse issue. It is crucial for researchers to rapidly understand the existing research on a particular topic and be able to propose an effective new research method. Text mining analysis has been widely applied in recent years, and this study integrated the text mining method into a review of drug abuse research. Through searches for keywords related to the drug abuse, all related publications were identified and downloaded from PubMed. After removing the duplicate and incomplete literature, the retained data were imported for analysis through text mining. A total of 19,843 papers were analyzed, and the text mining technique was used to search for keyword and questionnaire types. The results showed the associations between these questionnaires, with the top five being the Addiction Severity Index (16.44%), the Quality of Life survey (5.01%), the Beck Depression Inventory (3.24%), the Addiction Research Center Inventory (2.81%), and the Profile of Mood States (1.10%). Specifically, the Addiction Severity Index was most commonly used in combination with Quality of Life scales. In conclusion, association analysis is useful to extract core knowledge. Researchers can learn and visualize the latest research trend.

Public feedback on a proposed statewide virtual translational research community

Introduction:

Researchers have explored using the internet and social media to recruit participants to specific research projects. Less systematic work has been done to inform the engagement of large populations in virtual communities to advance clinical and translational science. We report on our first step to use social media to engage Minnesota residents by studying the willingness of participants to engage in a virtual (Facebook) community about the concepts of health and health-related research.

Methods:

Data were collected at the 2018 Minnesota State Fair using a cross-sectional, 46-item survey with assessment including sociodemographics and willingness to engage in a Facebook group for health-related research. Quantitative analysis included univariate, bivariate, and multivariate analyses. Content analysis was used to generate themes from open-ended survey responses.

Results:

Five hundred people completed the survey; after data cleaning, 418 participant responses informed this report. A majority were younger than age 50 (73%), female (66%), and married/partnered (54%). Overall, 46% of participants agreed/strongly agreed they are willing to join the Facebook group. Multivariate logistic regression identified social media use over the past 6 months as the sole variable independently associated with willingness to join the Facebook group (once a day vs. never or rarely OR = 1.82 (0.86, 3.88), several hours a day vs. never or rarely OR = 2.17 (1.17, 4.02, overall p-value 0.048).

Conclusion:

Facebook holds potential for reaching a broader community, democratizing access to and engagement with clinical and translational research. Social media infrastructure and content could be disseminated to other institutions with Clinical and Translational Science Awards.

Internet Searches for Medical Symptoms Before Seeking Information on 12-Step Addiction Treatment Programs: A Web-Search Log Analysis

Background

Brief intervention is a critical method for identifying patients with problematic substance use in primary care settings and for motivating them to consider treatment options. However, despite considerable evidence of delay discounting in patients with substance use disorders, most brief advice by physicians focuses on the long-term negative medical consequences, which may not be the best way to motivate patients to seek treatment information.

Objective

Identification of the specific symptoms that most motivate individuals to seek treatment information may offer insights for further improving brief interventions. To this end, we used anonymized internet search engine data to investigate which medical conditions and symptoms preceded searches for 12-step meeting locators and general 12-step information.

Methods

We extracted all queries made by people in the United States on the Bing search engine from November 2016 to July 2017. These queries were filtered for those who mentioned seeking Alcoholics Anonymous (AA) or Narcotics Anonymous (NA); in addition, queries that contained a medical symptom or condition or a synonym thereof were analyzed. We identified medical symptoms and conditions that predicted searches for seeking treatment at different time lags. Specifically, symptom queries were first determined to be significantly predictive of subsequent 12-step queries if the probability of querying a medical symptom by those who later sought information about the 12-step program exceeded the probability of that same query being made by a comparison group of all other Bing users in the United States. Second, we examined symptom queries preceding queries on the 12-step program at time lags of 0-7 days, 7-14 days, and 14-30 days, where the probability of asking about a medical symptom was greater in the 30-day time window preceding 12-step program information-seeking as compared to all previous times that the symptom was queried.

Results

In our sample of 11,784 persons, we found 10 medical symptoms that predicted AA information seeking and 9 symptoms that predicted NA information seeking. Of these symptoms, a substantial number could be categorized as nonsevere in nature. Moreover, when medical symptom persistence was examined across a 1-month time period, a substantial number of nonsevere, yet persistent, symptoms were identified.

Conclusions

Our results suggest that many common or nonsevere medical symptoms and conditions motivate subsequent interest in AA and NA programs. In addition to highlighting severe long-term consequences, brief interventions could be restructured to highlight how increasing substance misuse can worsen discomfort from common medical symptoms in the short term, as well as how these worsening symptoms could exacerbate social embarrassment or decrease physical attractiveness.

Digital Trespass: Ethical and Terms-of-Use Violations by Researchers Accessing Data From an Online Patient Community

With the expansion and popularity of research on websites such as Facebook and Twitter, there has been increasing concern about investigator conduct and social media ethics. The availability of large data sets has attracted researchers who are not traditionally associated with health data and its associated ethical considerations, such as computer and data scientists. Reliance on oversight by ethics review boards is inadequate and, due to the public availability of social media data, there is often confusion between public and private spaces. In addition, social media participants and researchers may pay little attention to traditional terms of use. In this paper, we review four cases involving ethical and terms-of-use violations by researchers seeking to conduct social media studies in an online patient research network. These violations involved unauthorized scraping of social media data, entry of false information, misrepresentation of researcher identities of participants on forums, lack of ethical approval and informed consent, use of member quotations, and presentation of findings at conferences and in journals without verifying accurate potential biases and limitations of the data. The correction of these ethical lapses often involves much effort in detecting and responding to violators, addressing these lapses with members of an online community, and correcting inaccuracies in the literature (including retraction of publications and conference presentations). Despite these corrective actions, we do not regard these episodes solely as violations. Instead, they represent broader ethical issues that may arise from potential sources of confusion, misinformation, inadequacies in applying traditional informed consent procedures to social media research, and differences in ethics training and scientific methodology across research disciplines. Social media research stakeholders need to assure participants that their studies will not compromise anonymity or lead to harmful outcomes while preserving the societal value of their health-related studies. Based on our experience and published recommendations by social media researchers, we offer potential directions for future prevention-oriented measures that can be applied by data producers, computer/data scientists, institutional review boards, research ethics committees, and publishers.

Using Twitter for Public Health Surveillance from Monitoring and Prediction to Public Response

Twitter is a social media platform where over 500 million people worldwide publish their ideas and discuss diverse topics, including their health conditions and public health events. Twitter has proved to be an important source of health-related information on the Internet, given the amount of information that is shared by both citizens and official sources. Twitter provides researchers with a real-time source of public health information on a global scale, and can be very important in public health research. Classifying Twitter data into topics or categories is helpful to better understand how users react and communicate. A literature review is presented on the use of mining Twitter data or similar short-text datasets for public health applications. Each method is analyzed for ways to use Twitter data in public health surveillance. Papers in which Twitter content was classified according to users or tweets for better surveillance of public health were selected for review. Only papers published between 2010–2017 were considered. The reviewed publications are distinguished by the methods that were used to categorize the Twitter content in different ways. While comparing studies is difficult due to the number of different methods that have been used for applying Twitter and interpreting data, this state-of-the-art review demonstrates the vast potential of utilizing Twitter for public health surveillance purposes.

Exploring the Utility of Community-Generated Social Media Content for Detecting Depression: An Analytical Study on Instagram

Background

The content produced by individuals on various social media platforms has been successfully used to identify mental illness, including depression. However, most of the previous work in this area has focused on user-generated content, that is, content created by the individual, such as an individual’s posts and pictures. In this study, we explored the predictive capability of community-generated content, that is, the data generated by a community of friends or followers, rather than by a sole individual, to identify depression among social media users.

Objective

The objective of this research was to evaluate the utility of community-generated content on social media, such as comments on an individual’s posts, to predict depression as defined by the clinically validated Patient Health Questionnaire-8 (PHQ-8) assessment questionnaire. We hypothesized that the results of this research may provide new insights into next generation of population-level mental illness risk assessment and intervention delivery.

Methods

We created a Web-based survey on a crowdsourcing platform through which participants granted access to their Instagram profiles as well as provided their responses to PHQ-8 as a reference standard for depression status. After data quality assurance and postprocessing, the study analyzed the data of 749 participants. To build our predictive model, linguistic features were extracted from Instagram post captions and comments, including multiple sentiment scores, emoji sentiment analysis results, and meta-variables such as the number of likes and average comment length. In this study, 10.4% (78/749) of the data were held out as a test set. The remaining 89.6% (671/749) of the data were used to train an elastic-net regularized linear regression model to predict PHQ-8 scores. We compared different versions of this model (ie, a model trained on only user-generated data, a model trained on only community-generated data, and a model trained on the combination of both types of data) on a test set to explore the utility of community-generated data in our predictive analysis.

Results

The 2 models, the first trained on only community-generated data (area under curve [AUC]=0.71) and the second trained on a combination of user-generated and community-generated data (AUC=0.72), had statistically significant performances for predicting depression based on the Mann-Whitney U test (P=.03 and P=.02, respectively). The model trained on only user-generated data (AUC=0.63; P=.11) did not achieve statistically significant results. The coefficients of the models revealed that our combined data classifier effectively amalgamated both user-generated and community-generated data and that the 2 feature sets were complementary and contained nonoverlapping information in our predictive analysis.

Conclusions

The results presented in this study indicate that leveraging community-generated data from social media, in addition to user-generated data, can be informative for predicting depression among social media users.

Substance Use Among Young Mothers: An Analysis of Facebook Posts

BACKGROUND

Substance use among young pregnant women is a common and significant public health concern associated with a number of adverse outcomes for both mothers and infants. Social media posts by young women can provide valuable, real-world insight into their perceptions of substance use immediately before and during pregnancy.

OBJECTIVE

The aim of this study was to characterize the frequency and content of posts regarding substance use in the year before pregnancy and during pregnancy among young mothers.

METHODS

Facebook posts were mined from young pregnant women (age, 16-24 years) who consented from 2 Midwest primary care clinics that serve a predominantly low-income community. Natural language processing was used to identify posts related to substance use by keyword searching (eg, drunk, drugs, pot, and meth). Using mixed-methods techniques, 2 investigators iteratively coded and identified major themes around substance use from these mined Facebook posts. Outcome measures include the frequency of posts and major themes expressed regarding substance use before and during pregnancy.

RESULTS

Women in our sample (N=43) had a mean age of 21 (SD 2.3) years, and the largest subgroup (21/43, 49%) identified as non-Hispanic black; 26% (11/43) identified as non-Hispanic white; 16% (7/43) as Hispanic; and 9% (4/43) as non-Hispanic mixed race, Native American, or other. The largest subgroup (20/43, 47%) graduated high school without further education, while 30% (13/43) completed only some high school and 23% (10/43) completed at least some postsecondary education. Young women discussed substance use on social media before and during pregnancy, although compared with the year before pregnancy, the average frequency of substance-related posts during pregnancy decreased. Themes identified included craving alcohol or marijuana, social use of alcohol or marijuana, reasons for abstaining from substance use, and intoxication.

CONCLUSIONS

Facebook posts reveal that young pregnant women discuss the use of substances, predominantly alcohol and marijuana. Future work can explore clinical opportunities to prevent and treat substance use before and during pregnancy among young, at-risk mothers.