The Canary in the Coal Mine Tweets: Social Media Reveals Public Perceptions of Non-Medical Use of Opioids

Which Social Media Platforms Provide the Most Informative Data for Monitoring the Opioid Crisis?

Background and Aims

Social media can provide real-time insight into trends in substance use, addiction, and recovery. Prior studies have leveraged data from platforms such as Reddit and X (formerly Twitter), but evolving policies around data access have threatened their usability in opioid overdose surveillance systems. Here, we evaluate the potential of a broad set of platforms to detect emerging trends in the opioid crisis.

Design

We identified 72 online platforms with a substantial global user base or prior citations in opioid-related research. We evaluated each platform's fit with our definition of social media, size of North American user base, and volume of opioid-related discourse. We created a shortlist of 11 platforms that met our criteria. We documented basic characteristics, volume and nature of opioid discussion, official policies regulating drug-related discussion, and data accessibility of shortlisted platforms.

Setting

USA and Canada.

Measurements

We quantified the volume of opioid discussion by number of platform-specific Google search hits for opioid terms. We captured informal language by including slang generated using a large language model. We report the number of opioid-related hits and proportion of opioid-related hits to hits for common nouns.

Findings

We found that TikTok, YouTube, and Facebook have the most potential for use in opioid-related surveillance. TikTok and Facebook have the highest relative amount of drug-related discussions. Language on TikTok was predominantly informal. Many platforms offer data access tools for research, but changing company policies and user norms create instability. The demographics of users varies substantially across platforms.

Conclusions

Social media data sources hold promise for detecting trends in opioid use, but researchers must consider the utility, accessibility, and stability of data on each platform. A strategy mixing several platforms may be required to cover all demographics suffering in the epidemic.

Natural Language Processing-Powered Real-Time Monitoring Solution for Vaccine Sentiments and Hesitancy on Social Media: System Development and Validation

BACKGROUND

Vaccines serve as a crucial public health tool, although vaccine hesitancy continues to pose a significant threat to full vaccine uptake and, consequently, community health. Understanding and tracking vaccine hesitancy is essential for effective public health interventions; however, traditional survey methods present various limitations.

OBJECTIVE

This study aimed to create a real-time, natural language processing (NLP)-based tool to assess vaccine sentiment and hesitancy across 3 prominent social media platforms.

METHODS

We mined and curated discussions in English from Twitter (subsequently rebranded as X), Reddit, and YouTube social media platforms posted between January 1, 2011, and October 31, 2021, concerning human papillomavirus; measles, mumps, and rubella; and unspecified vaccines. We tested multiple NLP algorithms to classify vaccine sentiment into positive, neutral, or negative and to classify vaccine hesitancy using the World Health Organization's (WHO) 3Cs (confidence, complacency, and convenience) hesitancy model, conceptualizing an online dashboard to illustrate and contextualize trends.

RESULTS

We compiled over 86 million discussions. Our top-performing NLP models displayed accuracies ranging from 0.51 to 0.78 for sentiment classification and from 0.69 to 0.91 for hesitancy classification. Explorative analysis on our platform highlighted variations in online activity about vaccine sentiment and hesitancy, suggesting unique patterns for different vaccines.

CONCLUSIONS

Our innovative system performs real-time analysis of sentiment and hesitancy on 3 vaccine topics across major social networks, providing crucial trend insights to assist campaigns aimed at enhancing vaccine uptake and public health.

Understanding Public Perceptions and Discussions on Opioids Through Twitter: Cross-Sectional Infodemiology Study

BACKGROUND

Opioids are used for the treatment of refractory pain, but their inappropriate use has detrimental consequences for health. Understanding the current experiences and perceptions of patients in a spontaneous and colloquial environment regarding the key drugs involved in the opioid crisis is of utmost significance.

OBJECTIVE

The study aims to analyze Twitter content related to opioids, with objectives including characterizing users participating in these conversations, identifying prevalent topics and gauging public perception, assessing opinions on drug efficacy and tolerability, and detecting discussions related to drug dispensing, prescription, or acquisition.

METHODS

In this cross-sectional study, we gathered public tweets concerning major opioids posted in English or Spanish between January 1, 2019, and December 31, 2020. A total of 256,218 tweets were collected. Approximately 27% (69,222/256,218) were excluded. Subsequently, 7000 tweets were subjected to manual analysis based on a codebook developed by the researchers. The remaining databases underwent analysis using machine learning classifiers. In the codebook, the type of user was the initial classification domain. We differentiated between patients, family members and friends, health care professionals, and institutions. Next, a distinction was made between medical and nonmedical content. If it was medical in nature, we classified it according to whether it referred to the drug's efficacy or adverse effects. In nonmedical content tweets, we analyzed whether the content referred to management issues (eg, pharmacy dispensation, medical appointment prescriptions, commercial advertisements, or legal aspects) or the trivialization of the drug.

RESULTS

Among the entire array of scrutinized pharmaceuticals, fentanyl emerged as the predominant subject, featuring in 27% (39,997/148,335 posts) of the tweets. Concerning user categorization, roughly 70% (101,259/148,335) were classified as patients. Nevertheless, tweets posted by health care professionals obtained the highest number of retweets (37/16,956, 0.2% of their posts received over 100 retweets). We found statistically significant differences in the distribution concerning efficacy and side effects among distinct drug categories (P<.001). Nearly 60% (84,401/148,335) of the posts were devoted to nonmedical subjects. Within this category, legal facets and recreational use surfaced as the most prevalent themes, while in the medical discourse, efficacy constituted the most frequent topic, with over 90% (45,621/48,777) of instances characterizing it as poor or null. The opioid with the greatest proportion of tweets concerning legal considerations was fentanyl. Furthermore, fentanyl was the drug most frequently offered for sale on Twitter, while methadone generated the most tweets about pharmacy delivery.

CONCLUSIONS

The opioid crisis is present on social media, where tweets discuss legal and recreational use. Opioid users are the most active participants, prioritizing medication efficacy over side effects. Surprisingly, health care professionals generate the most engagement, indicating their positive reception. Authorities must monitor web-based opioid discussions to detect illicit acquisitions and recreational use.

Twitter Perspectives on Cochlear Implantation: Sentiment and Thematic Analysis

OBJECTIVE

To identify themes that contribute to the most positive and negative perspectives of cochlear implants (CIs) on Twitter.

STUDY DESIGN

A cross-sectional qualitative study.

SETTING

Social media (Twitter).

METHODS

In this study, all English-language original tweets mentioning "cochlear implant" from 2019 to 2021 were collected from Twitter's Academic Research Database using a custom Python script. Sentiment analysis was performed using VADER, a sentiment analysis tool built to analyze the inherent positivity or negativity of social media posts. Tweets were quantitatively sorted by compound sentiment score (range -1 [most negative] to 1 [most positive]). Thematic analysis based on grounded theory was performed on the most positive, negative, and "liked" tweets.

RESULTS

Of the 19,376 tweets included, the average (standard deviation) compound sentiment score was 0.21 (0.46). A total of 10,375 (53.5%) tweets had a positive tone, 4965 (25.6%) were neutral and 4036 (20.8%) were negative. Of the 100 most negative tweets, the most prominent themes were media representation (21.9%), the controversy of CI within the Deaf community (19.8%), and unrelated to direct patient experience (16.7%). Of the 100 most positive tweets, the most prominent themes were tweets of happiness and support (25.0%), tweets unrelated to direct patient experience (18.0%), and tweets about hearing new sounds (10.0%).

CONCLUSION

While the majority of tweets on CI carried a positive tone, there are ongoing challenges with the stigma surrounding CI. Public perspectives of CI on social media may help clinicians counsel CI patients and elucidate issues that lead to newfound acceptance or ongoing stigma of CI in the general population.

A deep semantic matching approach for identifying relevant messages for social media analysis

There is a growing interest in using social media content for Natural Language Processing applications. However, it is not easy to computationally identify the most relevant set of tweets related to any specific event. Challenging semantics coupled with different ways for using natural language in social media make it difficult for retrieving the most relevant set of data from any social media outlet. This paper seeks to demonstrate a way to present the changing semantics of Twitter within the context of a crisis event, specifically tweets during Hurricane Irma. These methods can be used to identify the most relevant corpus of text for analysis in relevance to a specific incident such as a hurricane. Using an implementation of the Word2Vec method of Neural Network training mechanisms to create Word Embeddings, this paper will: discuss how the relative meaning of words changes as events unfold; present a mechanism for scoring tweets based upon dynamic, relative context relatedness; and show that similarity between words is not necessarily static. We present different methods for training the vector model in Word2Vec for identification of the most relevant tweets for any search query. The impact of tuning parameters such as Word Window Size, Minimum Word Frequency, Hidden Layer Dimensionality, and Negative Sampling on model performance was explored. The window containing the local maximum for AU_ROC for each parameter serves as a guide for other studies using the methods presented here for social media data analysis.

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.

#TurntTrending: a systematic review of substance use portrayals on social media platforms

AIMS

There is a growing body of literature exploring the types of substance-related content and their portrayals on various social media platforms. We aimed to summarize how content related to substances is portrayed on various social media platforms.

METHODS

This systematic review was pre-registered on PROSPERO (ref: CRD42021291853). A comprehensive search was conducted in the databases of PubMed, Scopus, PsycINFO and Web of Science in April 2021. Original qualitative studies published post-2004 that included thematic and sentiment analyses of social media content on tobacco, alcohol, psychostimulant, e-cigarette, cannabis, opiate, stimulant/amphetamine, inhalant and novel psychoactive substance were included. Social media platforms were defined as online web- or application-based platforms that allowed users to generate content and interact via 'liking', comment or messaging features. Only studies that included summative and/or thematic content analyses of substance-related social media content were included.

RESULTS

A total of 73 studies, which covered 15 905 182 substance-related posts on Twitter, YouTube, Instagram, Pinterest, TikTok and Weibo, were identified. A total of 76.3% of all substance-related content was positive in its depiction of substance use, with 20.2% of content depicting use negatively. Sentiment regarding opiate use however was commonly negative (55.5%). Most studies identified themes relating to Health, Safety and Harms (65.0%) of substance use. Themes relating to Promotions/Advertisements (63.3%), Informative content (55.0%) and Use behaviours (43.3%) were also frequently identified.

CONCLUSIONS

Substance-related content that promotes engagement with substance use or actively depicts use appears to be widely available on social media. The large public presence of this content may have concerning influences on attitudes, behaviours and risk perceptions relating to substance use, particularly among the most vulnerable and heaviest users of social media-adolescents and young adults.

Opioids in Satirical News Shows: Exploring Topics, Sentiments, and Engagement in Last Week Tonight on YouTube

Prescription drug misuse and opioid overdose have become one of the most serious health crises in the US health system. In this study, we examined Last Week Tonight as hosted by John Oliver in order to explore topics, sentiments, and reactions by referring to online comments on YouTube. Using an unsupervised machine learning approach, we analyzed 33,741 user generated comments taken from three selected episodes published between 2016 and 2021. Our results revealed 13 topics: the politicization of the opioid crisis, side effects of opioid use, seeking help, legal issues, reactions to the John Oliver show, drug addiction, the US in comparison with other countries, case settlements, the drug business, addiction and pharmaceutical companies, profitability of companies from deaths, drugs and elections, as well as general awareness and education. In addition, we found that opioid side effects, seeking help, and the drug business had received the highest user engagements in terms of likes and replies. Our study suggests that comments with positive sentiments were more likely to cause a more widespread engagement from YouTube users than negative comments.

Automating Global Threat-Maps Generation via Advancements of News Sensors and AI

Negative events are prevalent all over the globe round the clock. People demonstrate psychological affinity to negative events, and they incline to stay away from troubled locations. This paper proposes an automated geospatial imagery application that would allow a user to remotely extract knowledge of troubled locations. The autonomous application uses thousands of connected news sensors to obtain real-time news pertaining to all global troubles. From the captured news, the proposed application uses artificial intelligence-based services and algorithms like sentiment analysis, entity detection, geolocation decoder, news fidelity analysis, and decomposition tree analysis to reconstruct global threat maps representing troubled locations interactively. The fully deployed system was evaluated for full three months of summer 2021, during which the autonomous system processed above 22 k news from 2397 connected news sources involving BBC, CNN, NY Times, Government websites of 192 countries, and all possible major social media sites. The study revealed 11,668 troubled locations classified successfully with outstanding precision, recall, and F1-score, all evaluated in ubiquitous environment covering mobile, tablet, desktop, and cloud platforms. The system generated interesting global threat maps for robust scenario set of $$3.71 \times {10}^{29}$$ 3.71 × 10 29 , to be reported as original fully autonomous remote sensing application of this kind. The research discloses attractive news and global threat-maps with trusted overall classification accuracy.

Tracking the COVID-19 outbreak in India through Twitter: Opportunities for social media based global pandemic surveillance

We investigated the utility of Twitter for conducting multi-faceted geolocation-centric pandemic surveillance, using India as an example. We collected over 4 million COVID19-related tweets related to the Indian outbreak between January and July 2021. We geolocated the tweets, applied natural language processing to characterize the tweets (eg., identifying symptoms and emotions), and compared tweet volumes with the numbers of confirmed COVID-19 cases. Tweet numbers closely mirrored the outbreak, with the 7-day average strongly correlated with confirmed COVID-19 cases nationally (Spearman r=0.944; p=0.001), and also at the state level (Spearman r=0.84, p=0.0003). Fatigue, Dyspnea and Cough were the top symptoms detected, while there was a significant increase in the proportion of tweets expressing negative emotions (eg., fear and sadness). The surge in COVID-19 tweets was followed by increased number of posts expressing concern about black fungus and oxygen supply. Our study illustrates the potential of social media for multi-faceted pandemic surveillance.

Esthetic Dentistry on Twitter: Benefits and Dangers

Objectives

The demand for esthetic dentistry is growing, and social media constitute the main driving force behind this revolution. Twitter is a leading social media platform; however, there is a lack of research on the pattern of communications and the impact of Twitter on esthetic dentistry. The purpose of this study was to analyze the content of tweets related to esthetic dentistry and to investigate how Twitter has affected the perception of, and demand for, esthetic dentistry. Moreover, it aimed to assess the potential benefits and risks of esthetic dentistry advertising strategies on Twitter as a potential social media platform.

Materials and Methods

Using a specialized web scrapping tool made available by the Python programming language, a total of 54,000 public tweets were downloaded from Twitter at real-time by matching specific keywords. The downloaded tweets were manually filtered, and 13,787 tweets were included and categorized into four tweet sets by content.

Results

The main categories were tweets regarding specific cosmetic procedure, totaling 56.53% (43.2% for veneers, 13.3% for whitening), advertisements (36.72%), information (5.53%), and general cosmetic dentistry (1.22%). Negative opinions towards veneers and whitening were the most common themes (68.03% and 43.44%). For veneers, illegal use for kids was the most common negative theme (59.7%) and being destructive for whitening (86.3%). Positive opinions counted 6.26% of veneers and 4.3% of whitening tweets. Advertising for whitening products was the second most common between all tweets (16.6%), and advertising for dental practices was the third (14.7%). Presenting facilities/services was the most common marketing strategy for dental practices (49.7%). Twitter was weakly used to circulate educational content (5.5%) and to post reviews (0.75%).

Conclusion

Twitter was extensively used to share experiences/opinions towards dental cosmetic interventions. Advertising is another vital use of this platform. However, circulating information was the least common use of this social media. Additionally, tweeters were rarely to post online reviews and specific advices and recommendations regarding esthetic dentistry. Moreover, females tended to participate and tweet more significantly in cosmetic-related topics than males. This platform should be tailored efficiently to target users' priorities and to improve health literacy and self-care. Twitter was weakly used to circulate educational content according to this study and rarely used to share online reviews.

Thematic Analysis of Reddit Content About Buprenorphine-naloxone Using Manual Annotation and Natural Language Processing Techniques

BACKGROUND

Opioid use disorder (OUD) is a major public health crisis for which buprenorphine-naloxone is an effective evidence-based treatment. Analysis of Reddit data yields detailed information about firsthand experiences with buprenorphine-naloxone that has the potential to inform treatment of OUD.

METHODS

We conducted a thematic analysis of posts about buprenorphine-naloxone from a Reddit forum in which Reddit users anonymously discuss topics related to opioid use. We used an application programming interface to retrieve posts about buprenorphine-naloxone, then applied natural language processing to generate meta-information and curate samples of salient posts. We manually categorized posts according to their content and conducted natural language processing-aided analysis of posts about buprenorphine tapering strategies, withdrawal symptoms, and adjunctive substances/behaviors useful in the tapering process.

RESULTS

A total of 16,146 posts from 1933 redditors were retrieved from the /r/suboxone subreddit. Thematic analysis of sample posts (N = 200) revealed descriptions of personal experiences (74%), nonpersonal accounts (24%), and other content (2%). Among redditors who reported tapering to termination (N = 40), 0.063 mg and 0.125 mg were the most common termination doses. Fatigue, gastrointestinal disturbance, and mood disturbance were the most frequent adverse effects, and loperamide and vitamins/dietary supplements the most frequently discussed adverse effects adjunctive substances/behaviors respectively.

CONCLUSIONS

Discussions on Reddit are rich in information about buprenorphine-naloxone. Information derived from analysis of Reddit posts about buprenorphine-naloxone may not be available elsewhere and may help providers improve treatment of people with OUD through better understanding of the experiences of people who have used buprenorphine-naloxone.

Contemporary English Pain Descriptors as Detected on Social Media Using Artificial Intelligence and Emotion Analytics Algorithms: Cross-sectional Study

BACKGROUND

Pain description is fundamental to health care. The McGill Pain Questionnaire (MPQ) has been validated as a tool for the multidimensional measurement of pain; however, its use relies heavily on language proficiency. Although the MPQ has remained unchanged since its inception, the English language has evolved significantly since then. The advent of the internet and social media has allowed for the generation of a staggering amount of publicly available data, allowing linguistic analysis at a scale never seen before.

OBJECTIVE

The aim of this study is to use social media data to examine the relevance of pain descriptors from the existing MPQ, identify novel contemporary English descriptors for pain among users of social media, and suggest a modification for a new MPQ for future validation and testing.

METHODS

All posts from social media platforms from January 1, 2019, to December 31, 2019, were extracted. Artificial intelligence and emotion analytics algorithms (Crystalace and CrystalFeel) were used to measure the emotional properties of the text, including sarcasm, anger, fear, sadness, joy, and valence. Word2Vec was used to identify new pain descriptors associated with the original descriptors from the MPQ. Analysis of count and pain intensity formed the basis for proposing new pain descriptors and determining the order of pain descriptors within each subclass.

RESULTS

A total of 118 new associated words were found via Word2Vec. Of these 118 words, 49 (41.5%) words had a count of at least 110, which corresponded to the count of the bottom 10% (8/78) of the original MPQ pain descriptors. The count and intensity of pain descriptors were used to formulate the inclusion criteria for a new pain questionnaire. For the suggested new pain questionnaire, 11 existing pain descriptors were removed, 13 new descriptors were added to existing subclasses, and a new Psychological subclass comprising 9 descriptors was added.

CONCLUSIONS

This study presents a novel methodology using social media data to identify new pain descriptors and can be repeated at regular intervals to ensure the relevance of pain questionnaires. The original MPQ contains several potentially outdated pain descriptors and is inadequate for reporting the psychological aspects of pain. Further research is needed to examine the reliability and validity of the revised MPQ.

What About Drug Checking? Systematic Review and Netnographic Analysis of Social Media

Drug checking services have been operating worldwide as a harm reduction tool in places like festivals and night clubs. A systematic review and netnographic analysis were conducted to explore the public’s perception of drug checking. Although public perceptions of drug checking had not previously been evaluated in the literature, some positive and negative perceptions were captured. From twitter, a total of 1316 tweets were initially identified. Following the removal of irrelevant tweets, 235 relevant tweets were identified, of which about 95% (n = 223) tweets were in favour, and about 5% (n = 12) were not in favour of drug checking as a harm reduction intervention. Tweets perceived the service as part of effective law reform, public health intervention that serves in raising awareness and countering the role of the internet, initiative to reduce drug related harms and/ or potentially deaths, help in identifying Novel drug trends related to drugs, enabling a scientific basis to capture data, reducing harm from risky drugs or risky consumption, reducing the economic and social burden on society and preventing young people from having criminal records and punitive fines. Drug checking was perceived to support engagement with treatment services and support individuals in making more informed decisions. Tweets against drug checking focussed on the concerns over the quality of drug checking, particularly with false-positive results, which may lead to punitive outcomes, discrimination, and prejudice. The present study showed that twitter can be a useful platform to capture people’s perceptions of drug checking.

Mining social media for prescription medication abuse monitoring: a review and proposal for a data-centric framework

Objective

Prescription medication (PM) misuse and abuse is a major health problem globally, and a number of recent studies have focused on exploring social media as a resource for monitoring nonmedical PM use. Our objectives are to present a methodological review of social media–based PM abuse or misuse monitoring studies, and to propose a potential generalizable, data-centric processing pipeline for the curation of data from this resource.

Materials and Methods

We identified studies involving social media, PMs, and misuse or abuse (inclusion criteria) from Medline, Embase, Scopus, Web of Science, and Google Scholar. We categorized studies based on multiple characteristics including but not limited to data size; social media source(s); medications studied; and primary objectives, methods, and findings.

Results

A total of 39 studies met our inclusion criteria, with 31 (∼79.5%) published since 2015. Twitter has been the most popular resource, with Reddit and Instagram gaining popularity recently. Early studies focused mostly on manual, qualitative analyses, with a growing trend toward the use of data-centric methods involving natural language processing and machine learning.

Discussion

There is a paucity of standardized, data-centric frameworks for curating social media data for task-specific analyses and near real-time surveillance of nonmedical PM use. Many existing studies do not quantify human agreements for manual annotation tasks or take into account the presence of noise in data.

Conclusion

The development of reproducible and standardized data-centric frameworks that build on the current state-of-the-art methods in data and text mining may enable effective utilization of social media data for understanding and monitoring nonmedical PM use.

Text classification models for the automatic detection of nonmedical prescription medication use from social media

BACKGROUND

Prescription medication (PM) misuse/abuse has emerged as a national crisis in the United States, and social media has been suggested as a potential resource for performing active monitoring. However, automating a social media-based monitoring system is challenging-requiring advanced natural language processing (NLP) and machine learning methods. In this paper, we describe the development and evaluation of automatic text classification models for detecting self-reports of PM abuse from Twitter.

METHODS

We experimented with state-of-the-art bi-directional transformer-based language models, which utilize tweet-level representations that enable transfer learning (e.g., BERT, RoBERTa, XLNet, AlBERT, and DistilBERT), proposed fusion-based approaches, and compared the developed models with several traditional machine learning, including deep learning, approaches. Using a public dataset, we evaluated the performances of the classifiers on their abilities to classify the non-majority "abuse/misuse" class.

RESULTS

Our proposed fusion-based model performs significantly better than the best traditional model (F1-score [95% CI]: 0.67 [0.64-0.69] vs. 0.45 [0.42-0.48]). We illustrate, via experimentation using varying training set sizes, that the transformer-based models are more stable and require less annotated data compared to the other models. The significant improvements achieved by our best-performing classification model over past approaches makes it suitable for automated continuous monitoring of nonmedical PM use from Twitter.

CONCLUSIONS

BERT, BERT-like and fusion-based models outperform traditional machine learning and deep learning models, achieving substantial improvements over many years of past research on the topic of prescription medication misuse/abuse classification from social media, which had been shown to be a complex task due to the unique ways in which information about nonmedical use is presented. Several challenges associated with the lack of context and the nature of social media language need to be overcome to further improve BERT and BERT-like models. These experimental driven challenges are represented as potential future research directions.

Analysis of associations between emotions and activities of drug users and their addiction recovery tendencies from social media posts using structural equation modeling

Background

Addiction to drugs and alcohol constitutes one of the significant factors underlying the decline in life expectancy in the US. Several context-specific reasons influence drug use and recovery. In particular emotional distress, physical pain, relationships, and self-development efforts are known to be some of the factors associated with addiction recovery. Unfortunately, many of these factors are not directly observable and quantifying, and assessing their impact can be difficult. Based on social media posts of users engaged in substance use and recovery on the forum Reddit, we employed two psycholinguistic tools, Linguistic Inquiry and Word Count and Empath and activities of substance users on various Reddit sub-forums to analyze behavior underlining addiction recovery and relapse. We then employed a statistical analysis technique called structural equation modeling to assess the effects of these latent factors on recovery and relapse.

Results

We found that both emotional distress and physical pain significantly influence addiction recovery behavior. Self-development activities and social relationships of the substance users were also found to enable recovery. Furthermore, within the context of self-development activities, those that were related to influencing the mental and physical well-being of substance users were found to be positively associated with addiction recovery. We also determined that lack of social activities and physical exercise can enable a relapse. Moreover, geography, especially life in rural areas, appears to have a greater correlation with addiction relapse.

Conclusions

The paper describes how observable variables can be extracted from social media and then be used to model important latent constructs that impact addiction recovery and relapse. We also report factors that impact self-induced addiction recovery and relapse. To the best of our knowledge, this paper represents the first use of structural equation modeling of social media data with the goal of analyzing factors influencing addiction recovery.

Detection of Suicidality Among Opioid Users on Reddit: Machine Learning-Based Approach

BACKGROUND

In recent years, both suicide and overdose rates have been increasing. Many individuals who struggle with opioid use disorder are prone to suicidal ideation; this may often result in overdose. However, these fatal overdoses are difficult to classify as intentional or unintentional. Intentional overdose is difficult to detect, partially due to the lack of predictors and social stigmas that push individuals away from seeking help. These individuals may instead use web-based means to articulate their concerns.

OBJECTIVE

This study aimed to extract posts of suicidality among opioid users on Reddit using machine learning methods. The performance of the models is derivative of the data purity, and the results will help us to better understand the rationale of these users, providing new insights into individuals who are part of the opioid epidemic.

METHODS

Reddit posts between June 2017 and June 2018 were collected from r/suicidewatch, r/depression, a set of opioid-related subreddits, and a control subreddit set. We first classified suicidal versus nonsuicidal languages and then classified users with opioid usage versus those without opioid usage. Several traditional baselines and neural network (NN) text classifiers were trained using subreddit names as the labels and combinations of semantic inputs. We then attempted to extract out-of-sample data belonging to the intersection of suicide ideation and opioid abuse. Amazon Mechanical Turk was used to provide labels for the out-of-sample data.

RESULTS

Classification results were at least 90% across all models for at least one combination of input; the best classifier was convolutional neural network, which obtained an F1 score of 96.6%. When predicting out-of-sample data for posts containing both suicidal ideation and signs of opioid addiction, NN classifiers produced more false positives and traditional methods produced more false negatives, which is less desirable for predicting suicidal sentiments.

CONCLUSIONS

Opioid abuse is linked to the risk of unintentional overdose and suicide risk. Social media platforms such as Reddit contain metadata that can aid machine learning and provide information at a personal level that cannot be obtained elsewhere. We demonstrate that it is possible to use NNs as a tool to predict an out-of-sample target with a model built from data sets labeled by characteristics we wish to distinguish in the out-of-sample target.

Use of Deep Learning to Analyze Social Media Discussions About the Human Papillomavirus Vaccine

IMPORTANCE

Human papillomavirus (HPV) vaccine hesitancy or refusal is common among parents of adolescents. An understanding of public perceptions from the perspective of behavior change theories can facilitate effective and targeted vaccine promotion strategies.

OBJECTIVE

To develop and validate deep learning models for understanding public perceptions of HPV vaccines from the perspective of behavior change theories using data from social media.

DESIGN, SETTING, AND PARTICIPANTS

This retrospective cohort study, conducted from April to August 2019, included longitudinal and geographic analyses of public perceptions regarding HPV vaccines, using sampled HPV vaccine-related Twitter discussions collected from January 2014 to October 2018.

MAIN OUTCOMES AND MEASURES

The prevalence of social media discussions related to the construct of health belief model (HBM) and theory of planned behavior (TPB), categorized by deep learning algorithms. Locally estimated scatterplot smoothing (LOESS) revealed trends of constructs. Social media users' US state-level home location information was extracted from their profiles, and geographic analyses were performed to identify the clustering of public perceptions of the HPV vaccine.

RESULTS

A total of 1 431 463 English-language posts from 486 116 unique usernames were collected. Deep learning algorithms achieved F-1 scores ranging from 0.6805 (95% CI, 0.6516-0.7094) to 0.9421 (95% CI, 0.9380-0.9462) in mapping discussions to the constructs of behavior change theories. LOESS revealed trends in constructs; for example, prevalence of perceived barriers, a construct of HBM, deceased from its apex in July 2015 (56.2%) to its lowest prevalence in October 2018 (28.4%; difference, 27.8%; P < .001); Positive attitudes toward the HPV vaccine, a construct of TPB, increased from early 2017 (30.7%) to 41.9% at the end of the study (difference, 11.2%; P < .001), while negative attitudes decreased from 42.3% to 31.3% (difference, 11.0%; P < .001) during the same period. Interstate variations in public perceptions of the HPV vaccine were also identified; for example, the states of Ohio and Maine showed a relatively high prevalence of perceived barriers (11 531 of 17 106 [67.4%] and 1157 of 1684 [68.7%]) and negative attitudes (9655 of 17 197 [56.1%] and 1080 of 1793 [60.2%]).

CONCLUSIONS AND RELEVANCE

This cohort study provided a good understanding of public perceptions on social media and evolving trends in terms of multiple dimensions. The interstate variations of public perceptions could be associated with the rise of local antivaccine sentiment. The methods described in this study represent an early contribution to using existing empirically and theoretically based frameworks that describe human decision-making in conjunction with more intelligent deep learning algorithms. Furthermore, these data demonstrate the ability to collect large-scale HPV vaccine perception and intention data that can inform public health communication and education programs designed to improve immunization rates at the community, state, or even national level.

Recommendations From the Twitter Hashtag #DoctorsAreDickheads: Qualitative Analysis

BACKGROUND

The social media site Twitter has 145 million daily active users worldwide and has become a popular forum for users to communicate their health care concerns and experiences as patients. In the fall of 2018, a hashtag titled #DoctorsAreDickheads emerged, with almost 40,000 posts calling attention to health care experiences.

OBJECTIVE

This study aims to identify common health care conditions and conceptual themes represented within the phenomenon of this viral Twitter hashtag.

METHODS

We analyzed a random sample of 5.67% (500/8818) available tweets for qualitative analysis between October 15 and December 31, 2018, when the hashtag was the most active. Team coders reviewed the same 20.0% (100/500) tweets and the remainder individually. We abstracted the user's health care role and clinical conditions from the tweet and user profile, and used phenomenological content analysis to identify prevalent conceptual themes through sequential open coding, memoing, and discussion of concepts until an agreement was reached.

RESULTS

Our final sample comprised 491 tweets and unique Twitter users. Of this sample, 50.5% (248/491) were from patients or patient advocates, 9.6% (47/491) from health care professionals, 4.3% (21/491) from caregivers, 3.7% (18/491) from academics or researchers, 1.0% (5/491) from journalists or media, and 31.6% (155/491) from non-health care individuals or other. The most commonly mentioned clinical conditions were chronic pain, mental health, and musculoskeletal conditions (mainly Ehlers-Danlos syndrome). We identified 3 major themes: disbelief in patients' experience and knowledge that contributes to medical errors and harm, the power inequity between patients and providers, and metacommentary on the meaning and impact of the #DoctorsAreDickheads hashtag.

CONCLUSIONS

People publicly disclose personal and often troubling health care experiences on Twitter. This adds new accountability for the patient-provider interaction, highlights how harmful communication affects diagnostic safety, and shapes the public's viewpoint of how clinicians behave. Hashtags such as this offer valuable opportunities to learn from patient experiences. Recommendations include developing best practices for providers to improve communication, supporting patients through challenging diagnoses, and promoting patient engagement.

Complementing the US Food and Drug Administration Adverse Event Reporting System With Adverse Drug Reaction Reporting From Social Media: Comparative Analysis

Background

Adverse drug reactions (ADRs) can occur any time someone uses a medication. ADRs are systematically tracked and cataloged, with varying degrees of success, in order to better understand their etiology and develop methods of prevention. The US Food and Drug Administration (FDA) has developed the FDA Adverse Event Reporting System (FAERS) for this purpose. FAERS collects information from myriad sources, but the primary reporters have traditionally been medical professionals and pharmacovigilance data from manufacturers. Recent studies suggest that information shared publicly on social media platforms related to medication use could be of benefit in complementing FAERS data in order to have a richer picture of how medications are actually being used and the experiences people are having across large populations.

Objective

The aim of this study is to validate the accuracy and precision of social media methodology and conduct evaluations of Twitter ADR reporting for commonly used pharmaceutical agents.

Methods

ADR data from the 10 most prescribed medications according to pharmacy claims data were collected from both FAERS and Twitter. In order to obtain data from FAERS, the SafeRx database, a curated collection of FAERS data, was used to collect data from March 1, 2016, to March 31, 2017. Twitter data were manually scraped during the same time period to extract similar data using an algorithm designed to minimize noise and false signals in social media data.

Results

A total of 40,539 FAERS ADR reports were obtained via SafeRx and more than 40,000 tweets containing the drug names were obtained from Twitter’s Advanced Search engine. While the FAERS data were specific to ADRs, the Twitter data were more limited. Only hydrocodone/acetaminophen, prednisone, amoxicillin, gabapentin, and metformin had a sufficient volume of ADR content for review and comparison. For metformin, diarrhea was the side effect that resulted in no difference between the two platforms (P=.30). For hydrocodone/acetaminophen, ineffectiveness as an ADR that resulted in no difference (P=.60). For gabapentin, there were no differences in terms of the ADRs ineffectiveness and fatigue (P=.15 and P=.67, respectively). For amoxicillin, hypersensitivity, nausea, and rash shared similar profiles between platforms (P=.35, P=.05, and P=.31, respectively).

Conclusions

FAERS and Twitter shared similarities in types of data reported and a few unique items to each data set as well. The use of Twitter as an ADR pharmacovigilance platform should continue to be studied as a unique and complementary source of information rather than a validation tool of existing ADR databases.

Using Twitter to Surveil the Opioid Epidemic in North Carolina: An Exploratory Study

BACKGROUND

Over the last two decades, deaths associated with opioids have escalated in number and geographic spread, impacting more and more individuals, families, and communities. Reflecting on the shifting nature of the opioid overdose crisis, Dasgupta, Beletsky, and Ciccarone offer a triphasic framework to explain that opioid overdose deaths (OODs) shifted from prescription opioids for pain (beginning in 2000), to heroin (2010 to 2015), and then to synthetic opioids (beginning in 2013). Given the rapidly shifting nature of OODs, timelier surveillance data are critical to inform strategies that combat the opioid crisis. Using easily accessible and near real-time social media data to improve public health surveillance efforts related to the opioid crisis is a promising area of research.

OBJECTIVE

This study explored the potential of using Twitter data to monitor the opioid epidemic. Specifically, this study investigated the extent to which the content of opioid-related tweets corresponds with the triphasic nature of the opioid crisis and correlates with OODs in North Carolina between 2009 and 2017.

METHODS

Opioid-related Twitter posts were obtained using Crimson Hexagon, and were classified as relating to prescription opioids, heroin, and synthetic opioids using natural language processing. This process resulted in a corpus of 100,777 posts consisting of tweets, retweets, mentions, and replies. Using a random sample of 10,000 posts from the corpus, we identified opioid-related terms by analyzing word frequency for each year. OODs were obtained from the Multiple Cause of Death database from the Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (CDC WONDER). Least squares regression and Granger tests compared patterns of opioid-related posts with OODs.

RESULTS

The pattern of tweets related to prescription opioids, heroin, and synthetic opioids resembled the triphasic nature of OODs. For prescription opioids, tweet counts and OODs were statistically unrelated. Tweets mentioning heroin and synthetic opioids were significantly associated with heroin OODs and synthetic OODs in the same year (P=.01 and P<.001, respectively), as well as in the following year (P=.03 and P=.01, respectively). Moreover, heroin tweets in a given year predicted heroin deaths better than lagged heroin OODs alone (P=.03).

CONCLUSIONS

Findings support using Twitter data as a timely indicator of opioid overdose mortality, especially for heroin.

Using socially-sensed data to infer ZIP level characteristics for the spatiotemporal analysis of drug-related health problems in Maryland

This research investigated how socially sensed data can be used to detect ZIP level characteristics that are associated with spatial and temporal patterns of Emergency Department patients with a chief complaint and/or diagnosis of overdose or drug-related health problems for four hospitals in Baltimore and Anne Arundel County, MD during 2016-2018. Dynamic characteristics were identified using socially-sensed data (i.e., geo-tagged Twitter data) at ZIP code level over varying temporal resolutions. Data about three place-based variables including comments and concerns about crime, drug use, and negative or depressed sentiments, were extracted from tweets, along with data from four socio-environmental variables from the American Community Survey were collected to explore socio-environmental characteristics during the same period. Our study showed a statistically significant increase in adjusted rates of Emergency Department (ED) visits occurred between June and November 2017 for patients residing in ZIP codes in western Baltimore and northeastern Anne Arundel County. During this period, the three topics extracted from Twitter data were highly correlated with the ZIP codes where the patients were residing. Exploring the dynamic spatial associations between socio-environmental variables and ED visits for acute overdose assists local health officials in optimizing interventions for vulnerable locations.

Strengths and weaknesses of existing data sources to support research to address the opioids crisis

Better opioid prescribing practices, promoting effective opioid use disorder treatment, improving naloxone access, and enhancing public health surveillance are strategies central to reducing opioid-related morbidity and mortality. Successfully advancing and evaluating these strategies requires leveraging and linking existing secondary data sources. We conducted a scoping study in Fall 2017 at RAND, including a literature search (updated in December 2018) complemented by semi-structured interviews with policymakers and researchers, to identify data sources and linking strategies commonly used in opioid studies, describe data source strengths and limitations, and highlight opportunities to use data to address high-priority public health research questions. We identified 306 articles, published between 2005 and 2018, that conducted secondary analyses of existing data to examine one or more public health strategies. Multiple secondary data sources, available at national, state, and local levels, support such research, with substantial breadth in data availability, data contents, and the data's ability to support multi-level analyses over time. Interviewees identified opportunities to expand existing capabilities through systematic enhancements, including greater support to states for creating and facilitating data use, as well as key data challenges, such as data availability lags and difficulties matching individual-level data over time or across datasets. Multiple secondary data sources exist that can be used to examine the impact of public health approaches to addressing the opioid crisis. Greater data access, improved usability for research purposes, and data element standardization can enhance their value, as can improved data availability timeliness and better data comparability across jurisdictions.

Opioids on Twitter: A Content Analysis of Conversations regarding Prescription Drugs on Social Media and Implications for Message Design

As many as 130 lives are lost every day in the United States due to prescription drug misuse. The opioid crisis is gripping the country and disrupting the lives of millions. Not surprisingly, health organizations are desperately seeking solutions to educate and inform people regarding the issue, often seeking the help of various social media platforms. In this study, we do a content analysis of Twitter messages related to opioids in order to understand the factors that are most likely to influence content sharing. Our findings indicate that structure, source, and the actual content of the post all influence the likelihood of the content's being shared. Specifically, certain types of content enhanced the likelihood of content sharing whereas use of the term "addiction" discouraged retweeting. Theoretical and practical implications for message design are discussed.

Prescription Opioid Misuse: Examining the Role of Opioid-Related Attitudes among Youth and Young Adults by Sexual Orientation

Background: High prevalence of opioid misuse has been noted among youth and young adults-particularly among those who identify as lesbian/gay or bisexual (LGB). Yet, little is known about the relationship between opioid-related attitudes and misuse among those who identify as LGB. Objectives: This study examined the role of opioid-related attitudes (i.e. acceptance of misuse and risk perception) in relation to ever and past 6-month prescription opioid misuse, stratified by sexual orientation. Methods: This study used a nationally representative, probability-based sample of youth and young adults aged 15-34 (N = 12,745; lesbian/gay = 398, bisexual = 857, heterosexual = 11,490). Data were collected via online surveys in Spring 2018. Wald chi-square tests assessed differences in ever and past 6-month opioid misuse by sexual orientation identity. Weighted adjusted logistic regression models estimated the influence of opioid-related attitudes (acceptance of misuse and risk perceptions) in relation to ever and past 6-month opioid misuse, controlling for demographic characteristics, other past drug use, and psychosocial variables. Results: Logistic regression models indicated that attitudes related to the acceptance of opioid misuse was predictive of (a) higher odds of ever misuse among heterosexual and bisexual individuals and (b) higher odds of past 6-month misuse regardless of sexual orientation. Conclusions: Prescription opioid misuse among LGB youth and young adults may be addressed by shifting key attitudes surrounding opioids. To help stem this deadly epidemic, prevention efforts should be guided by the unique stressors facing the LGB community and focus on reducing the acceptability of misusing opioids.

Online data collection strategies used in qualitative research of the health field: a scoping review

ABSTRACT Objective: To identify and map the online data collection strategies used in qualitative researches in the health field. Methods: This is a scoping review guided by the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) from the Joanna Briggs Institute. We analyzed scientific articles, theses and dissertations from 12 databases. The analysis was made by descriptive statistics. Results: The final sample consisted of 121 researches. It was found that the number of publications increased sharply in the last five years, with predominance of studies from the United Kingdom. The highlight fields were psychology (28.1%), medicine (25.6%) and nursing (12.4%). The publications used 10 online data collection strategies: Online questionnaires, online forums, Facebook, websites, blogs, e-mail, online focus group, Twitter, chats, and YouTube. Conclusions: Online data collection strategies are constantly expanding and increasingly used in the health area.

Machine Learning and Natural Language Processing for Geolocation-Centric Monitoring and Characterization of Opioid-Related Social Media Chatter

IMPORTANCE

Automatic curation of consumer-generated, opioid-related social media big data may enable real-time monitoring of the opioid epidemic in the United States.

OBJECTIVE

To develop and validate an automatic text-processing pipeline for geospatial and temporal analysis of opioid-mentioning social media chatter.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional, population-based study was conducted from December 1, 2017, to August 31, 2019, and used more than 3 years of publicly available social media posts on Twitter, dated from January 1, 2012, to October 31, 2015, that were geolocated in Pennsylvania. Opioid-mentioning tweets were extracted using prescription and illicit opioid names, including street names and misspellings. Social media posts (tweets) (n = 9006) were manually categorized into 4 classes, and training and evaluation of several machine learning algorithms were performed. Temporal and geospatial patterns were analyzed with the best-performing classifier on unlabeled data.

MAIN OUTCOMES AND MEASURES

Pearson and Spearman correlations of county- and substate-level abuse-indicating tweet rates with opioid overdose death rates from the Centers for Disease Control and Prevention WONDER database and with 4 metrics from the National Survey on Drug Use and Health for 3 years were calculated. Classifier performances were measured through microaveraged F1 scores (harmonic mean of precision and recall) or accuracies and 95% CIs.

RESULTS

A total of 9006 social media posts were annotated, of which 1748 (19.4%) were related to abuse, 2001 (22.2%) were related to information, 4830 (53.6%) were unrelated, and 427 (4.7%) were not in the English language. Yearly rates of abuse-indicating social media post showed statistically significant correlation with county-level opioid-related overdose death rates (n = 75) for 3 years (Pearson r = 0.451, P < .001; Spearman r = 0.331, P = .004). Abuse-indicating tweet rates showed consistent correlations with 4 NSDUH metrics (n = 13) associated with nonmedical prescription opioid use (Pearson r = 0.683, P = .01; Spearman r = 0.346, P = .25), illicit drug use (Pearson r = 0.850, P < .001; Spearman r = 0.341, P = .25), illicit drug dependence (Pearson r = 0.937, P < .001; Spearman r = 0.495, P = .09), and illicit drug dependence or abuse (Pearson r = 0.935, P < .001; Spearman r = 0.401, P = .17) over the same 3-year period, although the tests lacked power to demonstrate statistical significance. A classification approach involving an ensemble of classifiers produced the best performance in accuracy or microaveraged F1 score (0.726; 95% CI, 0.708-0.743).

CONCLUSIONS AND RELEVANCE

The correlations obtained in this study suggest that a social media-based approach reliant on supervised machine learning may be suitable for geolocation-centric monitoring of the US opioid epidemic in near real time.

SMARTS: the social media-based addiction recovery and intervention targeting server

MOTIVATION

Substance abuse and addiction is a significant contemporary health crisis. Modeling its epidemiology and designing effective interventions requires real-time data analysis along with the means to contextualize addiction patterns across the individual-to-community scale. In this context, social media platforms have begun to receive significant attention as a novel source of real-time user-reported information. However, the ability of epidemiologists to use such information is significantly stymied by the lack of publicly available algorithms and software for addiction information extraction, analysis and modeling.

RESULTS

SMARTS is a public, open source, web-based application that addresses the aforementioned deficiency. SMARTS is designed to analyze data from two popular social media forums, namely, Reddit and Twitter and can be used to study the effect of various intoxicants including, opioids, weed, kratom, alcohol, and cigarettes. The SMARTS software analyzes social media posts using natural language processing, and machine learning to characterize drug use at both the individual- and population-levels. Included in SMARTS is a predictive modeling functionality that can, with high accuracy, identify individuals open to addiction recovery interventions. SMARTS also supports extraction, analysis and visualization of a number of key informational and demographic characteristics including post topics and sentiment, drug- and recovery-term usage, geolocation, and age. Finally, the distributions of the aforementioned characteristics as derived from a set of 170,097 drug users are provided as part of SMARTS and can be used by researchers as a reference.

AVAILABILITY

The SMARTS web server and source code are available at: http://haddock9.sfsu.edu/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Posting, texting, and related social risk behavior while high

Background: Posting on social media can have lasting consequences in one's social life and career. Research has not yet focused on social media or more modern forms of communication as social risk factors for individuals high on drugs. This study aims to examine prevalence and correlates of engaging in social media posting and related behavior while high. Methods: We examined data from 872 adults (39.8% female) who were surveyed entering electronic dance music (EDM) parties in New York City and reported lifetime illegal drug use. Participants were asked whether they were ever high on a drug while (1) posting on social media, (2) calling or texting someone, and (3) being in a photo. Those answering affirmatively were also asked whether they later regretted the behavior. We examined demographic and drug-related correlates of these behaviors. Results: Of the participants, 34.3% posted on social media while high (with 21.4% regretting it), 55.9% had texted or called someone while high (with 30.5% regretting it), and 47.6% had been in a photo while high (with 32.7% regretting it). Females and young adults (ages 18-24) were at high risk for posting on social media while high and at higher risk for engaging in more of these behaviors. Past-month marijuana users in particular were at increased risk for engaging in each of these behaviors. Conclusion: Engaging in these behaviors while high on drugs appears to be prevalent, and prevention and harm reduction efforts should seek to prevent or reduce likelihood of social harm that can result from such situations.

Themes in published obituaries of people who have died of opioid overdose

This study gauged public perceptions of the opioid epidemic in online obituaries related to opioid overdose by analyzing emotional themes and tones. A thematic analysis was performed on a consecutive listing of online obituaries from the United States. Three hundred electronic obituaries of people who died due to opioid, heroin, and prescription drug overdose, identified in an online obituary platform (Legacy.com) using the keyword "overdose" were used. Tones, emotions, terms used to describe death, and types of opioids were measured using the string search function of Stata and IBM Watson Tone Analyzer. Our analysis showed that joy and sadness are the most prevalent tones in these obituaries (92% and 88%, respectively) and the most common emotion was love (79%). The two most commonly used terms to describe death due to opioid overdose were 'accidental' (53%) and 'addiction' (34%). The two types of narcotics named were 'heroin' (35%) and 'prescription opioids' (7.8%). Obituaries of people who have died due to opioid overdose contain main themes of love, joy, and sadness. The fact that stigma and shame were less prevalent themes might suggest support of the concept that addiction should be regarded as a disease rather than a criminal behavior.

Leveraging deep learning to understand health beliefs about the Human Papillomavirus Vaccine from social media

Our aim was to characterize health beliefs about the human papillomavirus (HPV) vaccine in a large set of Twitter posts (tweets). We collected a Twitter data set related to the HPV vaccine from 1 January 2014, to 31 December 2017. We proposed a deep-learning-based framework to mine health beliefs on the HPV vaccine from Twitter. Deep learning achieved high performance in terms of sensitivity, specificity, and accuracy. A retrospective analysis of health beliefs found that HPV vaccine beliefs may be evolving on Twitter.

Social Media Based Analysis of Opioid Epidemic Using Reddit

Opioid-abuse epidemic in the United States has escalated to national attention due to the dramatic increase of opioid overdose deaths. Analyzing opioid-related social media has the potential to reveal patterns of opioid abuse at a national scale, understand opinions of the public, and provide insights to support prevention and treatment. Reddit is a community based social media with more reliable content curated by the community through voting. In this study, we collected and analyzed all opioid related discussions from January 2014 to October 2017, which contains 51,537 posts by 16,162 unique users. We analyzed the data to understand the psychological categories of the posts, and performed topic modeling to reveal the major topics of interest. We also characterized the extent of social support received from comments and scores by each post. Last, we analyzed statistically significant difference in the posts between anonymous and non-anonymous users.

Barriers to accurately assessing prescription opioid misuse on surveys

Surveys are the leading method of gathering data on prevalence and correlates of prescription opioid misuse. As the opioid crisis continues in the United States, reliable data on misuse have become increasingly important as under- and overreporting is common. This perspective discusses six potential barriers to researchers accurately assessing opioid misuse on surveys. These barriers include lack of respondent drug knowledge, researchers not properly defining opioids for respondents, querying overall opioid misuse rather than misuse of specific opioids, varying terminology (e.g., of opioids and misuse), and lack of understanding of medical versus nonmedical use or misuse. Finally, a recent barrier is use of counterfeit or adulterated drugs that contain fentanyl, its analogs, or other new synthetic opioids, which can lead to underreporting of exposure. Survey researchers should consider such barriers when querying opioid misuse.

At-a-glance - What can social media tell us about the opioid crisis in Canada?

We explored social media as a potential data source for acquiring realtime information on opioid use and perceptions in Canada. Twitter messages were collected through a social media analytics platform between June 15, 2017, and July 13, 2017, and analyzed to identify recurring topics mentioned in the messages. Messages concerning the medical use of opioids as well as commentary on the Canadian government's current response efforts to the opioid crisis were common. The findings of this study may help to inform public health practice and community stakeholders in their efforts to address the opioid crisis.

Identifying tweets of personal health experience through word embedding and LSTM neural network

BACKGROUND

As Twitter has become an active data source for health surveillance research, it is important that efficient and effective methods are developed to identify tweets related to personal health experience. Conventional classification algorithms rely on features engineered by human domain experts, and engineering such features is a challenging task and requires much human intelligence. The resultant features may not be optimal for the classification problem, and can make it challenging for conventional classifiers to correctly predict personal experience tweets (PETs) due to the various ways to express and/or describe personal experience in tweets. In this study, we developed a method that combines word embedding and long short-term memory (LSTM) model without the need to engineer any specific features. Through word embedding, tweet texts were represented as dense vectors which in turn were fed to the LSTM neural network as sequences.

RESULTS

Statistical analyses of the results of 10-fold cross-validations of our method and conventional methods indicate that there exist significant differences (p < 0.01) in performance measures of accuracy, precision, recall, F1-score, and ROC/AUC, demonstrating that our approach outperforms the conventional methods in identifying PETs.

CONCLUSION

We presented an efficient and effective method of identifying health-related personal experience tweets by combining word embedding and an LSTM neural network. It is conceivable that our method can help accelerate and scale up analyzing textual data of social media for health surveillance purposes, because of no need for the laborious and costly process of engineering features.

Using Social Media to Target Cancer Prevention in Young Adults: Viewpoint

Focusing on primary cancer prevention can reduce its incidence. Changing health behaviors is critical to cancer prevention. Modifiable cancer risk factors include lifestyle behaviors related to vaccination, physical activity, weight control and maintenance, alcohol consumption, and tobacco use. These health habits are often formed in young adulthood, a life stage which currently intersects with the growing population of digital natives whose childhood occurred in the internet era. Social media is a critical communication medium to reach this population of digital natives. Using a life course perspective, the purpose of this viewpoint paper is to describe the current landscape of nascent research using social media to target cancer prevention efforts in young adults and propose future directions to strengthen the scientific knowledge supporting social media strategies to promote cancer prevention behaviors. Leveraging social media as a health promotion tool is a promising strategy to impact modifiable behavioral risk factors for cancer and warrants further research on developing effective communication strategies in young adults to prevent cancer in the future generations.

Trends of infodemiology studies: a scoping review

INTRODUCTION

The health care industry is rich in data and information. Web technologies, such as search engines and social media, have provided an opportunity for the management of user generated data in real time in the form of infodemiology studies. The aim of this study was to investigate infodemiology studies conducted during 2002-2016, and compare them based on developed, developing and in transition countries.

METHODS

This scoping review was conducted in 2017 with the help of the PRISMA guidelines. PubMed, Scopus, Science Direct, Web of Knowledge, Google Scholar, Wiley and Springer databases were searched between the years 2002 and 2016. Finally, 56 articles were included in the review and analysed.

RESULTS

The initial infodemiology studies pertain to the quality assessment of the hospital's websites. Most of the studies were on developed countries, based on flu, and published in the Journal of Medical Internet Research.

CONCLUSION

The infodemiology approach provides unmatched opportunities for the management of health data and information generated by the users. Using this potential will provide unique opportunities for the health information need assessment in real time by health librarians and thereby provide evidence based health information to the people.

Sentiment Analysis of Health Care Tweets: Review of the Methods Used

Background

Twitter is a microblogging service where users can send and read short 140-character messages called “tweets.” There are several unstructured, free-text tweets relating to health care being shared on Twitter, which is becoming a popular area for health care research. Sentiment is a metric commonly used to investigate the positive or negative opinion within these messages. Exploring the methods used for sentiment analysis in Twitter health care research may allow us to better understand the options available for future research in this growing field.

Objective

The first objective of this study was to understand which tools would be available for sentiment analysis of Twitter health care research, by reviewing existing studies in this area and the methods they used. The second objective was to determine which method would work best in the health care settings, by analyzing how the methods were used to answer specific health care questions, their production, and how their accuracy was analyzed.

Methods

A review of the literature was conducted pertaining to Twitter and health care research, which used a quantitative method of sentiment analysis for the free-text messages (tweets). The study compared the types of tools used in each case and examined methods for tool production, tool training, and analysis of accuracy.

Results

A total of 12 papers studying the quantitative measurement of sentiment in the health care setting were found. More than half of these studies produced tools specifically for their research, 4 used open source tools available freely, and 2 used commercially available software. Moreover, 4 out of the 12 tools were trained using a smaller sample of the study’s final data. The sentiment method was trained against, on an average, 0.45% (2816/627,024) of the total sample data. One of the 12 papers commented on the analysis of accuracy of the tool used.

Conclusions

Multiple methods are used for sentiment analysis of tweets in the health care setting. These range from self-produced basic categorizations to more complex and expensive commercial software. The open source and commercial methods are developed on product reviews and generic social media messages. None of these methods have been extensively tested against a corpus of health care messages to check their accuracy. This study suggests that there is a need for an accurate and tested tool for sentiment analysis of tweets trained using a health care setting–specific corpus of manually annotated tweets first.

Representations of Codeine Misuse on Instagram: Content Analysis

BACKGROUND

Prescription opioid misuse has doubled over the past 10 years and is now a public health epidemic. Analysis of social media data may provide additional insights into opioid misuse to supplement the traditional approaches of data collection (eg, self-report on surveys).

OBJECTIVE

The aim of this study was to characterize representations of codeine misuse through analysis of public posts on Instagram to understand text phrases related to misuse.

METHODS

We identified hashtags and searchable text phrases associated with codeine misuse by analyzing 1156 sequential Instagram posts over the course of 2 weeks from May 2016 to July 2016. Content analysis of posts associated with these hashtags identified the most common themes arising in images, as well as culture around misuse, including how misuse is happening and being perpetuated through social media.

RESULTS

A majority of images (50/100; 50.0%) depicted codeine in its commonly misused form, combined with soda (lean). Codeine misuse was commonly represented with the ingestion of alcohol, cannabis, and benzodiazepines. Some images highlighted the previously noted affinity between codeine misuse and hip-hop culture or mainstream popular culture images.

CONCLUSIONS

The prevalence of codeine misuse images, glamorizing of ingestion with soda and alcohol, and their integration with mainstream, popular culture imagery holds the potential to normalize and increase codeine misuse and overdose. To reduce harm and prevent misuse, immediate public health efforts are needed to better understand the relationship between the potential normalization, ritualization, and commercialization of codeine misuse.

A Concept Analysis of Substance Misuse to Inform Contemporary Terminology

Previous diagnostic categories of substance abuse and dependence have given way to the current view that substance use disorders occur on a continuum with a broad range of severity. This current view is featured in the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM V). In recognizing the role of stigma in preventing persons from seeking substance use treatment, advocates have called attention that particular terminology can fuel such stigma. To mitigate the negative effects of such stigmatizing language, the International Society of Addiction Journal Editors (ISAJE) recommends against using previously-used and possibly pejorative terminology for substance abuse and dependence, unless a particular scientific justification exists. The purpose of this paper is to: (1) present a concept analysis of the term substance misuse and (2) recommend an alternate term for substance misuse that is neither pejorative nor inadvertently stigmatizing: at-risk substance use.

National substance use patterns on Twitter

Purpose

We examined openly shared substance-related tweets to estimate prevalent sentiment around substance use and identify popular substance use activities. Additionally, we investigated associations between substance-related tweets and business characteristics and demographics at the zip code level.

Methods

A total of 79,848,992 tweets were collected from 48 states in the continental United States from April 2015-March 2016 through the Twitter API, of which 688,757 were identified as being related to substance use. We implemented a machine learning algorithm (maximum entropy text classifier) to estimate sentiment score for each tweet. Zip code level summaries of substance use tweets were created and merged with the 2013 Zip Code Business Patterns and 2010 US Census Data.

Results

Quality control analyses with a random subset of tweets yielded excellent agreement rates between computer generated and manually generated labels: 97%, 88%, 86%, 75% for underage engagement in substance use, alcohol, drug, and smoking tweets, respectively. Overall, 34.1% of all substance-related tweets were classified as happy. Alcohol was the most frequently tweeted substance, followed by marijuana. Regression results suggested more convenience stores in a zip code were associated with higher percentages of tweets about alcohol. Larger zip code population size and higher percentages of African Americans and Hispanics were associated with fewer tweets about substance use and underage engagement. Zip code economic disadvantage was associated with fewer alcohol tweets but more drug tweets.

Conclusions

The patterns in substance use mentions on Twitter differ by zip code economic and demographic characteristics. Online discussions have great potential to glorify and normalize risky behaviors. Health promotion and underage substance prevention efforts may include interactive social media campaigns to counter the social modeling of risky behaviors.

Capturing the Patient's Perspective: a Review of Advances in Natural Language Processing of Health-Related Text

Background: Natural Language Processing (NLP) methods are increasingly being utilized to mine knowledge from unstructured health-related texts. Recent advances in noisy text processing techniques are enabling researchers and medical domain experts to go beyond the information encapsulated in published texts (e.g., clinical trials and systematic reviews) and structured questionnaires, and obtain perspectives from other unstructured sources such as Electronic Health Records (EHRs) and social media posts. Objectives: To review the recently published literature discussing the application of NLP techniques for mining health-related information from EHRs and social media posts. Methods: Literature review included the research published over the last five years based on searches of PubMed, conference proceedings, and the ACM Digital Library, as well as on relevant publications referenced in papers. We particularly focused on the techniques employed on EHRs and social media data. Results: A set of 62 studies involving EHRs and 87 studies involving social media matched our criteria and were included in this paper. We present the purposes of these studies, outline the key NLP contributions, and discuss the general trends observed in the field, the current state of research, and important outstanding problems. Conclusions: Over the recent years, there has been a continuing transition from lexical and rule-based systems to learning-based approaches, because of the growth of annotated data sets and advances in data science. For EHRs, publicly available annotated data is still scarce and this acts as an obstacle to research progress. On the contrary, research on social media mining has seen a rapid growth, particularly because the large amount of unlabeled data available via this resource compensates for the uncertainty inherent to the data. Effective mechanisms to filter out noise and for mapping social media expressions to standard medical concepts are crucial and latent research problems. Shared tasks and other competitive challenges have been driving factors behind the implementation of open systems, and they are likely to play an imperative role in the development of future systems.

Use of syndromic surveillance data to monitor poisonings and drug overdoses in state and local public health agencies

BACKGROUND

The incidence of poisoning and drug overdose has risen rapidly in the USA over the last 16 years. To inform local intervention approaches, local health departments (LHDs) in North Carolina (NC) are using a statewide syndromic surveillance system that provides timely, local emergency department (ED) and Emergency Medical Services (EMS) data on medication and drug overdoses.

OBJECTIVE

The purpose of this article is to describe the development and use of a variety of case definitions for poisoning and overdose implemented in NC's syndromic surveillance system and the impact of the system on local surveillance initiatives.

DESIGN, SETTING, PARTICIPANTS

Thirteen new poisoning and overdose-related case definitions were added to NC's syndromic surveillance system and LHDs were trained on their use for surveillance purposes. Twenty-one LHDs were surveyed on the utility and impact of these new case definitions.

RESULTS/CONCLUSIONS

Ninety-one per cent of survey respondents (n = 29) agreed or strongly agreed that their ability to access timely ED data was vital to inform community-level overdose prevention work. Providing LHDs with access to local, timely data to identify pockets of need and engage stakeholders facilitates the practice of informed injury prevention and contributes to the reduction of injury incidence in their communities.

A pattern-matched Twitter analysis of US cancer-patient sentiments

BACKGROUND

Twitter has been recognized as an important source of organic sentiment and opinion. This study aimed to (1) characterize the content of tweets authored by the United States cancer patients; and (2) use patient tweets to compute the average happiness of cancer patients for each cancer diagnosis.

METHODS

A large sample of English tweets from March 2014 through December 2014 was obtained from Twitter. Using regular expression software pattern matching, the tweets were filtered by cancer diagnosis. For each cancer-specific tweetset, individual patients were extracted, and the content of the tweet was categorized. The patients' Twitter identification numbers were used to gather all tweets for each patient, and happiness values for patient tweets were calculated using a quantitative hedonometric analysis.

RESULTS

The most frequently tweeted cancers were breast (n = 15,421, 11% of total cancer tweets), lung (n = 2928, 2.0%), prostate (n = 1036, 0.7%), and colorectal (n = 773, 0.5%). Patient tweets pertained to the treatment course (n = 73, 26%), diagnosis (n = 65, 23%), and then surgery and/or biopsy (n = 42, 15%). Computed happiness values for each cancer diagnosis revealed higher average happiness values for thyroid (h_avg = 6.1625), breast (h_avg = 6.1485), and lymphoma (h_avg = 6.0977) cancers and lower average happiness values for pancreatic (h_avg = 5.8766), lung (h_avg = 5.8733), and kidney (h_avg = 5.8464) cancers.

CONCLUSIONS

The study confirms that patients are expressing themselves openly on social media about their illness and that unique cancer diagnoses are correlated with varying degrees of happiness. Twitter can be employed as a tool to identify patient needs and as a means to gauge the cancer patient experience.

Garbage in, Garbage Out: Data Collection, Quality Assessment and Reporting Standards for Social Media Data Use in Health Research, Infodemiology and Digital Disease Detection

Background

Social media have transformed the communications landscape. People increasingly obtain news and health information online and via social media. Social media platforms also serve as novel sources of rich observational data for health research (including infodemiology, infoveillance, and digital disease detection detection). While the number of studies using social data is growing rapidly, very few of these studies transparently outline their methods for collecting, filtering, and reporting those data. Keywords and search filters applied to social data form the lens through which researchers may observe what and how people communicate about a given topic. Without a properly focused lens, research conclusions may be biased or misleading. Standards of reporting data sources and quality are needed so that data scientists and consumers of social media research can evaluate and compare methods and findings across studies.

Objective

We aimed to develop and apply a framework of social media data collection and quality assessment and to propose a reporting standard, which researchers and reviewers may use to evaluate and compare the quality of social data across studies.

Methods

We propose a conceptual framework consisting of three major steps in collecting social media data: develop, apply, and validate search filters. This framework is based on two criteria: retrieval precision (how much of retrieved data is relevant) and retrieval recall (how much of the relevant data is retrieved). We then discuss two conditions that estimation of retrieval precision and recall rely on—accurate human coding and full data collection—and how to calculate these statistics in cases that deviate from the two ideal conditions. We then apply the framework on a real-world example using approximately 4 million tobacco-related tweets collected from the Twitter firehose.

Results

We developed and applied a search filter to retrieve e-cigarette–related tweets from the archive based on three keyword categories: devices, brands, and behavior. The search filter retrieved 82,205 e-cigarette–related tweets from the archive and was validated. Retrieval precision was calculated above 95% in all cases. Retrieval recall was 86% assuming ideal conditions (no human coding errors and full data collection), 75% when unretrieved messages could not be archived, 86% assuming no false negative errors by coders, and 93% allowing both false negative and false positive errors by human coders.

Conclusions

This paper sets forth a conceptual framework for the filtering and quality evaluation of social data that addresses several common challenges and moves toward establishing a standard of reporting social data. Researchers should clearly delineate data sources, how data were accessed and collected, and the search filter building process and how retrieval precision and recall were calculated. The proposed framework can be adapted to other public social media platforms.