Characterizing Sleep Issues Using Twitter

Inferring sleep disturbance from text messages of suicide attempt survivors: A pilot study

OBJECTIVE

Identifying digital markers of sleep disturbance-a known suicide risk factor-may aid in the detection of imminent suicide risk. This study examined sleep-related communication and texting patterns in personal text messages (N = 86,705) of suicide attempt survivors.

METHOD

Twenty-six participants provided dates of past suicide attempts and 2-week periods of positive mood, depressed mood, or suicidal ideation. Linguistic Inquiry Word Count was used to identify sleep-related texts via a custom dictionary. Mixed effect models were fitted to test the association between suicide/mood episode type (e.g., attempt versus ideation) and three outcomes: likelihood of a text including sleep-related content, nightly count of texts sent from midnight to 5:00 AM, and sum of unique hour bins from midnight to 5:00 AM with outgoing texts.

RESULTS

Analyses with a sleep dictionary that was manually revised to be more accurate (but not the original unedited dictionary) showed sleep-related communication was more likely during depressed mood episodes than positive mood episodes. Otherwise, there were no significant differences in sleep-related communication or objective texting patterns across episode type.

CONCLUSIONS

Although we did not detect differences in sleep-related communication tied to suicidal thoughts or behaviors, sleep-related communication may differ as a function of within-person mood level.

Estimation of Bedtimes of Reddit Users: Integrated Analysis of Time Stamps and Surveys

BACKGROUND

Individuals with later bedtimes have an increased risk of difficulties with mood and substances. To investigate the causes and consequences of late bedtimes and other sleep patterns, researchers are exploring social media as a data source. Pioneering studies inferred sleep patterns directly from social media data. While innovative, these efforts are variously unscalable, context dependent, confined to specific sleep parameters, or rest on untested assumptions, and none of the reviewed studies apply to the popular Reddit platform or release software to the research community.

OBJECTIVE

This study builds on this prior work. We estimate the bedtimes of Reddit users from the times tamps of their posts, test inference validity against survey data, and release our model as an R package (The R Foundation).

METHODS

We included 159 sufficiently active Reddit users with known time zones and known, nonanomalous bedtimes, together with the time stamps of their 2.1 million posts. The model's form was chosen by visualizing the aggregate distribution of the timing of users' posts relative to their reported bedtimes. The chosen model represents a user's frequency of Reddit posting by time of day, with a flat portion before bedtime and a quadratic depletion that begins near the user's bedtime, with parameters fitted to the data. This model estimates the bedtimes of individual Reddit users from the time stamps of their posts. Model performance is assessed through k-fold cross-validation. We then apply the model to estimate the bedtimes of 51,372 sufficiently active, nonbot Reddit users with known time zones from the time stamps of their 140 million posts.

RESULTS

The Pearson correlation between expected and observed Reddit posting frequencies in our model was 0.997 on aggregate data. On average, posting starts declining 45 minutes before bedtime, reaches a nadir 4.75 hours after bedtime that is 87% lower than the daytime rate, and returns to baseline 10.25 hours after bedtime. The Pearson correlation between inferred and reported bedtimes for individual users was 0.61 (P<.001). In 90 of 159 cases (56.6%), our estimate was within 1 hour of the reported bedtime; 128 cases (80.5%) were within 2 hours. There was equivalent accuracy in hold-out sets versus training sets of k-fold cross-validation, arguing against overfitting. The model was more accurate than a random forest approach.

CONCLUSIONS

We uncovered a simple, reproducible relationship between Reddit users' reported bedtimes and the time of day when high daytime posting rates transition to low nighttime posting rates. We captured this relationship in a model that estimates users' bedtimes from the time stamps of their posts. Limitations include applicability only to users who post frequently, the requirement for time zone data, and limits on generalizability. Nonetheless, it is a step forward for inferring the sleep parameters of social media users passively at scale. Our model and precomputed estimated bedtimes of 50,000 Reddit users are freely available.

Identifying Sleep Disorders From Search Engine Activity: Combining User-Generated Data With a Clinically Validated Questionnaire

Background

Sleep disorders are experienced by up to 40% of the population but their diagnosis is often delayed by the availability of specialists.

Objective

We propose the use of search engine activity in conjunction with a validated web-based sleep questionnaire to facilitate wide-scale screening of prevalent sleep disorders.

Methods

Search advertisements offering a web-based sleep disorder screening questionnaire were shown on the Bing search engine to individuals who indicated an interest in sleep disorders. People who clicked on the advertisements and completed the sleep questionnaire were identified as being at risk for 1 of 4 common sleep disorders. A machine learning algorithm was applied to previous search engine queries to predict their suspected sleep disorder, as identified by the questionnaire.

Results

A total of 397 users consented to participate in the study and completed the questionnaire. Of them, 132 had sufficient past query data for analysis. Our findings show that diurnal patterns of people with sleep disorders were shifted by 2-3 hours compared to those of the controls. Past query activity was predictive of sleep disorders, approaching an area under the receiver operating characteristic curve of 0.62-0.69, depending on the sleep disorder.

Conclusions

Targeted advertisements can be used as an initial screening tool for people with sleep disorders. However, search engine data are seemingly insufficient as a sole method for screening. Nevertheless, we believe that evaluable web-based information, easily collected and processed with little effort on part of the physician and with low burden on the individual, can assist in the diagnostic process and possibly drive people to seek sleep assessment and diagnosis earlier than they currently do.

Sentiment analysis of tweets on alopecia areata, hidradenitis suppurativa, and psoriasis: Revealing the patient experience

Background

Chronic dermatologic disorders can cause significant emotional distress. Sentiment analysis of disease-related tweets helps identify patients' experiences of skin disease.

Objective

To analyze the expressed sentiments in tweets related to alopecia areata (AA), hidradenitis suppurativa (HS), and psoriasis (PsO) in comparison to fibromyalgia (FM).

Methods

This is a cross-sectional analysis of Twitter users' expressed sentiment on AA, HS, PsO, and FM. Tweets related to the diseases of interest were identified with keywords and hashtags for one month (April, 2022) using the Twitter standard application programming interface (API). Text, account types, and numbers of retweets and likes were collected. The sentiment analysis was performed by the R "tidytext" package using the AFINN lexicon.

Results

A total of 1,505 tweets were randomly extracted, of which 243 (16.15%) referred to AA, 186 (12.36%) to HS, 510 (33.89%) to PsO, and 566 (37.61%) to FM. The mean sentiment score was -0.239 ± 2.90. AA, HS, and PsO had similar sentiment scores (p = 0.482). Although all skin conditions were associated with a negative polarity, their average was significantly less negative than FM (p < 0.0001). Tweets from private accounts were more negative, especially for AA (p = 0.0082). Words reflecting patients' psychological states varied in different diseases. "Anxiety" was observed in posts on AA and FM but not posts on HS and PsO, while "crying" was frequently used in posts on HS. There was no definite correlation between the sentiment score and the number of retweets or likes, although negative AA tweets from public accounts received more retweets (p = 0.03511) and likes (p = 0.0228).

Conclusion

The use of Twitter sentiment analysis is a promising method to document patients' experience of skin diseases, which may improve patient care through bridging misconceptions and knowledge gaps between patients and healthcare professionals.

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.

Lessons for science and technology policy? Probing the Linkedin network of an RDI organisation

In this paper, we seek to examine the network of the Greek National Documentation Centre (EKT) as formed by its LinkedIn followers. By applying specific data collection and processing techniques, we explore the network of all the individuals that follow EKT’s LinkedIn page. Significant manual and automatic approaches have been implemented with regard to data extraction, data curation and data homogenization. The aim is to identify the network’s advancement over time, the institutions involved and the countries. The timeframe of the study spans from when the relevant LinkedIn page was constructed in 2015 to 2020. Findings indicate that there is a steady increase in the number of new followers, peaking in 2020. On an international scale, the evolution of the network of followers is imprinted and distributed in worldwide maps. In total, 68 countries have followed EKT over the examined time period. Also, in terms of followers’ institutional sector the Business Sector (BES) stands out (46.5%). Higher Education (HES) and Government Sector (GOV) are associated with 26.4 and 22.2% of the followers, respectively. Lastly, this paper provides a first institutional and country-level mapping of who constitutes the organisation’s interlocutors in the national and global RDI ecosystem.

Identifying Insomnia From Social Media Posts: Psycholinguistic Analyses of User Tweets

Background

Many people suffer from insomnia, a sleep disorder characterized by difficulty falling and staying asleep during the night. As social media have become a ubiquitous platform to share users’ thoughts, opinions, activities, and preferences with their friends and acquaintances, the shared content across these platforms can be used to diagnose different health problems, including insomnia. Only a few recent studies have examined the prediction of insomnia from Twitter data, and we found research gaps in predicting insomnia from word usage patterns and correlations between users’ insomnia and their Big 5 personality traits as derived from social media interactions.

Objective

The purpose of this study is to build an insomnia prediction model from users’ psycholinguistic patterns, including the elements of word usage, semantics, and their Big 5 personality traits as derived from tweets.

Methods

In this paper, we exploited both psycholinguistic and personality traits derived from tweets to identify insomnia patients. First, we built psycholinguistic profiles of the users from their word choices and the semantic relationships between the words of their tweets. We then determined the relationship between a users’ personality traits and insomnia. Finally, we built a double-weighted ensemble classification model to predict insomnia from both psycholinguistic and personality traits as derived from user tweets.

Results

Our classification model showed strong prediction potential (78.8%) to predict insomnia from tweets. As insomniacs are generally ill-tempered and feel more stress and mental exhaustion, we observed significant correlations of certain word usage patterns among them. They tend to use negative words (eg, “no,” “not,” “never”). Some people frequently use swear words (eg, “damn,” “piss,” “fuck”) with strong temperament. They also use anxious (eg, “worried,” “fearful,” “nervous”) and sad (eg, “crying,” “grief,” “sad”) words in their tweets. We also found that the users with high neuroticism and conscientiousness scores for the Big 5 personality traits likely have strong correlations with insomnia. Additionally, we observed that users with high conscientiousness scores have strong correlations with insomnia patterns, while negative correlation between extraversion and insomnia was also found.

Conclusions

Our model can help predict insomnia from users’ social media interactions. Thus, incorporating our model into a software system can help family members detect insomnia problems in individuals before they become worse. The software system can also help doctors to diagnose possible insomnia in patients.

Negative Perception of the COVID-19 Pandemic Is Dropping: Evidence From Twitter Posts

The COVID-19 pandemic hit hard society, strongly affecting the emotions of the people and wellbeing. It is difficult to measure how the pandemic has affected the sentiment of the people, not to mention how people responded to the dramatic events that took place during the pandemic. This study contributes to this discussion by showing that the negative perception of the people of the COVID-19 pandemic is dropping. By negative perception, we mean the number of negative words the users of Twitter, a social media platform, employ in their online posts. Seen as aggregate, Twitter users are using less and less negative words as the pandemic evolves. The conclusion that the negative perception is dropping comes from a careful analysis we made in the contents of the COVID-19 Twitter chatter dataset, a comprehensive database accounting for more than 1 billion posts generated during the pandemic. We explore why the negativity of the people decreases, making connections with psychological traits such as psychophysical numbing, reappraisal, suppression, and resilience. In particular, we show that the negative perception decreased intensively when the vaccination campaign started in the USA, Canada, and the UK and has remained to decrease steadily since then. This finding led us to conclude that vaccination plays a key role in dropping the negativity of the people, thus promoting their psychological wellbeing.

Exploring discussions of health and risk and public sentiment in Massachusetts during COVID-19 pandemic mandate implementation: A Twitter analysis

As policies are adjusted throughout the COVID-19 pandemic according to public health best practices, there is a need to balance the importance of social distancing in preventing viral spread with the strain that these governmental public safety mandates put on public mental health. Thus, there is need for continuous observation of public sentiment and deliberation to inform further adaptation of mandated interventions. In this study, we explore how public response may be reflected in Massachusetts (MA) via social media by specifically exploring temporal patterns in Twitter posts (tweets) regarding sentiment and discussion of topics. We employ interrupted time series centered on (1) Massachusetts State of Emergency declaration (March 10), (2) US State of Emergency declaration (March 13) and (3) Massachusetts public school closure (March 17) to explore changes in tweet sentiment polarity (net negative/positive), expressed anxiety and discussion on risk and health topics on a random subset of all tweets coded within Massachusetts and published from January 1 to May 15, 2020 (n = 2.8 million). We find significant differences between tweets published before and after mandate enforcement for Massachusetts State of Emergency (increased discussion of risk and health, decreased polarity and increased anxiety expression), US State of Emergency (increased discussion of risk and health, and increased anxiety expression) and Massachusetts public school closure (increased discussion of risk and decreased polarity). Our work further validates that Twitter data is a reasonable way to monitor public sentiment and discourse within a crisis, especially in conjunction with other observation data.

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Twitter can be used to track the emotions of the public during times of crises.

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During COVID-19 shelter-in-place an increase in discussions about risk and health, and anxiety levels was seen on Twitter.

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Real-time information from Twitter may be used to make quick evidence-based decisions based on public reactions.

Consumer Views on Health Applications of Consumer Digital Data and Health Privacy Among US Adults: Qualitative Interview Study

BACKGROUND

In 2020, the number of internet users surpassed 4.6 billion. Individuals who create and share digital data can leave a trail of information about their habits and preferences that collectively generate a digital footprint. Studies have shown that digital footprints can reveal important information regarding an individual's health status, ranging from diet and exercise to depression. Uses of digital applications have accelerated during the COVID-19 pandemic where public health organizations have utilized technology to reduce the burden of transmission, ultimately leading to policy discussions about digital health privacy. Though US consumers report feeling concerned about the way their personal data is used, they continue to use digital technologies.

OBJECTIVE

This study aimed to understand the extent to which consumers recognize possible health applications of their digital data and identify their most salient concerns around digital health privacy.

METHODS

We conducted semistructured interviews with a diverse national sample of US adults from November 2018 to January 2019. Participants were recruited from the Ipsos KnowledgePanel, a nationally representative panel. Participants were asked to reflect on their own use of digital technology, rate various sources of digital information, and consider several hypothetical scenarios with varying sources and health-related applications of personal digital information.

RESULTS

The final cohort included a diverse national sample of 45 US consumers. Participants were generally unaware what consumer digital data might reveal about their health. They also revealed limited knowledge of current data collection and aggregation practices. When responding to specific scenarios with health-related applications of data, they had difficulty weighing the benefits and harms but expressed a desire for privacy protection. They saw benefits in using digital data to improve health, but wanted limits to health programs' use of consumer digital data.

CONCLUSIONS

Current privacy restrictions on health-related data are premised on the notion that these data are derived only from medical encounters. Given that an increasing amount of health-related data is derived from digital footprints in consumer settings, our findings suggest the need for greater transparency of data collection and uses, and broader health privacy protections.

Psychosocial phenotyping as a personalization strategy for chronic disease self-management interventions

BACKGROUND

As the U.S. population grows older and more diverse, self-management needs are increasingly complicated. In order to deliver effective personalized interventions to those suffer from chronic conditions social determinants of health must be considered. Therefore, psychosocial phenotyping holds strong promise as a tool for tailoring interventions based on precision health principles.

PURPOSE

To define psychosocial phenotyping and develop a research agenda that promotes its integration into chronic disease management as a tool for precision self-management interventions.

METHODS

Since psychosocial phenotyping is not yet used in interventions for self-management support, we conducted a literature review to identify potential phenotypes for chronic disease self-management. We also reviewed policy intervention case reports from the Centers for Medicare and Medicaid Services to examine factors related to social determinants of health in people with chronic illnesses. Finally, we reviewed methodological approaches for identifying patient profiles or phenotypes.

RESULTS

The literature review revealed areas within which to collect data for psychosocial phenotyping that can inform personalized interventions. The findings of our exemplar cases revealed that several environmental or key SDOH such as factors realted with economic stability and neighborhood environment have been closely linked with the success of chronic disease management interventions. We elucidated theory, definitions, and pragmatic conceptual boundaries related to psychosocial phenotyping for precision health.

CONCLUSIONS

Our literature review with case example analysis demonstrates the potential usefulness of psychosocial phenotyping as a tool to enhance personalized self-management interventions for people with chronic diseases, with implications for future research.

Twitter-Derived Social Neighborhood Characteristics and Individual-Level Cardiometabolic Outcomes: Cross-Sectional Study in a Nationally Representative Sample

BACKGROUND

Social media platforms such as Twitter can serve as a potential data source for public health research to characterize the social neighborhood environment. Few studies have linked Twitter-derived characteristics to individual-level health outcomes.

OBJECTIVE

This study aims to assess the association between Twitter-derived social neighborhood characteristics, including happiness, food, and physical activity mentions, with individual cardiometabolic outcomes using a nationally representative sample.

METHODS

We collected a random 1% of the geotagged tweets from April 2015 to March 2016 using Twitter's Streaming Application Interface (API). Twitter-derived zip code characteristics on happiness, food, and physical activity were merged to individual outcomes from restricted-use National Health and Nutrition Examination Survey (NHANES) with residential zip codes. Separate regression analyses were performed for each of the neighborhood characteristics using NHANES 2011-2016 and 2007-2016.

RESULTS

Individuals living in the zip codes with the two highest tertiles of happy tweets reported BMI of 0.65 (95% CI -1.10 to -0.20) and 0.85 kg/m2 (95% CI -1.48 to -0.21) lower than those living in zip codes with the lowest frequency of happy tweets. Happy tweets were also associated with a 6%-8% lower prevalence of hypertension. A higher prevalence of healthy food tweets was linked with an 11% (95% CI 2% to 21%) lower prevalence of obesity. Those living in areas with the highest and medium tertiles of physical activity tweets were associated with a lower prevalence of hypertension by 10% (95% CI 4% to 15%) and 8% (95% CI 2% to 14%), respectively.

CONCLUSIONS

Twitter-derived social neighborhood characteristics were associated with individual-level obesity and hypertension in a nationally representative sample of US adults. Twitter data could be used for capturing neighborhood sociocultural influences on chronic conditions and may be used as a platform for chronic outcomes prevention.

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.

Estimating geographic subjective well-being from Twitter: A comparison of dictionary and data-driven language methods

Researchers and policy makers worldwide are interested in measuring the subjective well-being of populations. When users post on social media, they leave behind digital traces that reflect their thoughts and feelings. Aggregation of such digital traces may make it possible to monitor well-being at large scale. However, social media-based methods need to be robust to regional effects if they are to produce reliable estimates. Using a sample of 1.53 billion geotagged English tweets, we provide a systematic evaluation of word-level and data-driven methods for text analysis for generating well-being estimates for 1,208 US counties. We compared Twitter-based county-level estimates with well-being measurements provided by the Gallup-Sharecare Well-Being Index survey through 1.73 million phone surveys. We find that word-level methods (e.g., Linguistic Inquiry and Word Count [LIWC] 2015 and Language Assessment by Mechanical Turk [LabMT]) yielded inconsistent county-level well-being measurements due to regional, cultural, and socioeconomic differences in language use. However, removing as few as three of the most frequent words led to notable improvements in well-being prediction. Data-driven methods provided robust estimates, approximating the Gallup data at up to r = 0.64. We show that the findings generalized to county socioeconomic and health outcomes and were robust when poststratifying the samples to be more representative of the general US population. Regional well-being estimation from social media data seems to be robust when supervised data-driven methods are used.

Are Aspects of Twitter Use Associated with Reduced Depressive Symptoms? The Moderating Role of In-Person Social Support

In a two-wave, 4-month longitudinal study of 308 adults, two hypotheses were tested regarding the relation of Twitter-based measures of online social media use and in-person social support with depressive thoughts and symptoms. For four of five measures, Twitter use by in-person social support interactions predicted residualized change in depression-related outcomes over time; these results supported a corollary of the social compensation hypothesis that social media use is associated with greater benefits for people with lower in-person social support. In particular, having a larger Twitter social network (i.e., following and being followed by more people) and being more active in that network (i.e., sending and receiving more tweets) are especially helpful to people who have lower levels of in-person social support. For the fifth measure (the sentiment of Tweets), no interaction emerged; however, a beneficial main effect offset the adverse main effect of low in-person social support.

Twitter-based measures of neighborhood sentiment as predictors of residential population health

Several studies have recently applied sentiment-based lexicons to Twitter to gauge local sentiment to understand health behaviors and outcomes for local areas. While this research has demonstrated the vast potential of this approach, lingering questions remain regarding the validity of Twitter mining and surveillance in local health research. First, how well does this approach predict health outcomes at very local scales, such as neighborhoods? Second, how robust are the findings garnered from sentiment signals when accounting for spatial effects? To evaluate these questions, we link 2,076,025 tweets from 66,219 distinct users in the city of San Diego over the period of 2014-12-06 to 2017-05-24 to the 500 Cities Project data and 2010-2014 American Community Survey data. We determine how well sentiment predicts self-rated mental health, sleep quality, and heart disease at a census tract level, controlling for neighborhood characteristics and spatial autocorrelation. We find that sentiment is related to some outcomes on its own, but these relationships are not present when controlling for other neighborhood factors. Evaluating our encoding strategy more closely, we discuss the limitations of existing measures of neighborhood sentiment, calling for more attention to how race/ethnicity and socio-economic status play into inferences drawn from such measures.

A systematic literature review of machine learning in online personal health data

OBJECTIVE

User-generated content (UGC) in online environments provides opportunities to learn an individual's health status outside of clinical settings. However, the nature of UGC brings challenges in both data collecting and processing. The purpose of this study is to systematically review the effectiveness of applying machine learning (ML) methodologies to UGC for personal health investigations.

MATERIALS AND METHODS

We searched PubMed, Web of Science, IEEE Library, ACM library, AAAI library, and the ACL anthology. We focused on research articles that were published in English and in peer-reviewed journals or conference proceedings between 2010 and 2018. Publications that applied ML to UGC with a focus on personal health were identified for further systematic review.

RESULTS

We identified 103 eligible studies which we summarized with respect to 5 research categories, 3 data collection strategies, 3 gold standard dataset creation methods, and 4 types of features applied in ML models. Popular off-the-shelf ML models were logistic regression (n = 22), support vector machines (n = 18), naive Bayes (n = 17), ensemble learning (n = 12), and deep learning (n = 11). The most investigated problems were mental health (n = 39) and cancer (n = 15). Common health-related aspects extracted from UGC were treatment experience, sentiments and emotions, coping strategies, and social support.

CONCLUSIONS

The systematic review indicated that ML can be effectively applied to UGC in facilitating the description and inference of personal health. Future research needs to focus on mitigating bias introduced when building study cohorts, creating features from free text, improving clinical creditability of UGC, and model interpretability.

Extracting health-related causality from twitter messages using natural language processing

BACKGROUND

Twitter messages (tweets) contain various types of topics in our daily life, which include health-related topics. Analysis of health-related tweets would help us understand health conditions and concerns encountered in our daily lives. In this paper we evaluate an approach to extracting causalities from tweets using natural language processing (NLP) techniques.

METHODS

Lexico-syntactic patterns based on dependency parser outputs are used for causality extraction. We focused on three health-related topics: "stress", "insomnia", and "headache." A large dataset consisting of 24 million tweets are used.

RESULTS

The results show the proposed approach achieved an average precision between 74.59 to 92.27% in comparisons with human annotations.

CONCLUSIONS

Manual analysis on extracted causalities in tweets reveals interesting findings about expressions on health-related topic posted by Twitter users.

Feasibility of using social media to monitor outdoor air pollution in London, England

Air pollution is a serious public health concern. Innovative and scalable methods for detecting harmful air pollutants such as PM2.5 are necessary. This study assessed the feasibility of using social media to monitor outdoor air pollution in an urban area by comparing data from Twitter and validating it against established air monitoring stations. Data were collected from London, England from July 29, 2016 to March 17, 2017. Daily mean PM2.5 data was downloaded from the LondonAir platform consisting of 26 air pollution monitoring sites throughout Greater London. Publicly available tweets geo-located to Greater London containing air pollution terms were captured from the Twitter platform. Tweets with media URL links were excluded to minimize influence of news stories. Sentiment of the tweets was examined as negative, positive, or neutral. Cross-correlation analyses were used to compare the relationship between trends of tweets about air pollution and levels of PM2.5 over time. There were 16,448 tweets without a media URL link, with a mean of 498.42 (SD = 491.08) tweets per week. A significant cross-correlation coefficient of 0.803 was observed between PM2.5 data and the non-media air pollution tweets (p < 0.001). The cross-correlation coefficient was highest between PM2.5 data and air pollution tweets with negative sentiment at 0.816 (p < 0.001). Discussions about air pollution on Twitter reflect particle PM2.5 pollution levels in Greater London. This study highlights that social media may offer a supplemental source to support the detection and monitoring of air pollution in a densely populated urban area.

Using Twitter to Detect Psychological Characteristics of Self-Identified Persons With Autism Spectrum Disorder: A Feasibility Study

Background

More than 3.5 million Americans live with autism spectrum disorder (ASD). Major challenges persist in diagnosing ASD as no medical test exists to diagnose this disorder. Digital phenotyping holds promise to guide in the clinical diagnoses and screening of ASD.

Objective

This study aims to explore the feasibility of using the Web-based social media platform Twitter to detect psychological and behavioral characteristics of self-identified persons with ASD.

Methods

Data from Twitter were retrieved from 152 self-identified users with ASD and 182 randomly selected control users from March 22, 2012 to July 20, 2017. We conducted a between-group comparative textual analysis of tweets about repetitive and obsessive-compulsive behavioral characteristics typically associated with ASD. In addition, common emotional characteristics of persons with ASD, such as fear, paranoia, and anxiety, were examined between groups through textual analysis. Furthermore, we compared the timing of tweets between users with ASD and control users to identify patterns in communication.

Results

Users with ASD posted a significantly higher frequency of tweets related to the specific repetitive behavior of counting compared with control users (P<.001). The textual analysis of obsessive-compulsive behavioral characteristics, such as fixate, excessive, and concern, were significantly higher among users with ASD compared with the control group (P<.001). In addition, emotional terms related to fear, paranoia, and anxiety were tweeted at a significantly higher rate among users with ASD compared with control users (P<.001). Users with ASD posted a smaller proportion of tweets during time intervals of 00:00-05:59 (P<.001), 06:00-11:59 (P<.001), and 18:00-23.59 (P<.001), as well as a greater proportion of tweets from 12:00 to 17:59 (P<.001) compared with control users.

Conclusions

Social media may be a valuable resource for observing unique psychological characteristics of self-identified persons with ASD. Collecting and analyzing data from these digital platforms may afford opportunities to identify the characteristics of ASD and assist in the diagnosis or verification of ASD. This study highlights the feasibility of leveraging digital data for gaining new insights into various health conditions.

Monitoring Online Discussions About Suicide Among Twitter Users With Schizophrenia: Exploratory Study

BACKGROUND

People with schizophrenia experience elevated risk of suicide. Mental health symptoms, including depression and anxiety, contribute to increased risk of suicide. Digital technology could support efforts to detect suicide risk and inform suicide prevention efforts.

OBJECTIVE

This exploratory study examined the feasibility of monitoring online discussions about suicide among Twitter users who self-identify as having schizophrenia.

METHODS

Posts containing the terms suicide or suicidal were collected from a sample of Twitter users who self-identify as having schizophrenia (N=203) and a random sample of control users (N=173) over a 200-day period. Frequency and timing of posts about suicide were compared between groups. The associations between posting about suicide and common mental health symptoms were examined.

RESULTS

Twitter users who self-identify as having schizophrenia posted more tweets about suicide (mean 7.10, SD 15.98) compared to control users (mean 1.89, SD 4.79; t₃₇₄=-4.13, P<.001). Twitter users who self-identify as having schizophrenia showed greater odds of tweeting about suicide compared to control users (odds ratio 2.15, 95% CI 1.42-3.28). Among all users, tweets about suicide were associated with tweets about depression (r=0.62, P<.001) and anxiety (r=0.45, P<.001).

CONCLUSIONS

Twitter users who self-identify as having schizophrenia appear to commonly discuss suicide on social media, which is associated with greater discussion about other mental health symptoms. These findings should be interpreted cautiously, as it is not possible to determine whether online discussions about suicide correlate with suicide risk. However, these patterns of online discussion may be indicative of elevated risk of suicide observed in this patient group. There may be opportunities to leverage social media for supporting suicide prevention among individuals with schizophrenia.

Twitter-derived measures of sentiment towards minorities (2015-2016) and associations with low birth weight and preterm birth in the United States

INTRODUCTION

The objective of this study was to investigate the association between state-level publicly expressed sentiment towards racial and ethnic minorities and birth outcomes for mothers who gave birth in that state.

METHODS

We utilized Twitter's Streaming Application Programming Interface (API) to collect 1,249,653 tweets containing at least one relevant keyword pertaining to a racial or ethnic minority group. State-level derived sentiment towards racial and ethnic minorities were merged with data on all 2015 U.S. births (N=3.99 million singleton births).

RESULTS

Mothers living in states in the lowest tertile of positive sentiment towards racial/ethnic minorities had greater prevalences of low birth weight (+6%), very low birth weight (+9%), and preterm birth (+10%) compared to mothers living in states in the highest tertile of positive sentiment, controlling for individual-level maternal characteristics and state demographic characteristics. Sentiment towards specific racial/ethnic groups showed a similar pattern. Mothers living in states in the lowest tertile of positive sentiment towards blacks had an 8% greater prevalence of low birth weight and very low birth weight, and a 16% greater prevalence of preterm birth, compared to mothers living in states in the highest tertile. Lower state-level positive sentiment towards Middle Eastern groups was also associated with a 4-13% greater prevalence of adverse birth outcomes. Results from subgroup analyses restricted to racial/ethnic minority mothers did not differ substantially from those seen for the full population of mothers.

CONCLUSIONS

More negative area-level sentiment towards blacks and Middle Eastern groups was related to worse individual birth outcomes, and this is true for the full population and minorities.

Online Communication about Depression and Anxiety among Twitter Users with Schizophrenia: Preliminary Findings to Inform a Digital Phenotype Using Social Media

Digital technologies hold promise for supporting the detection and management of schizophrenia. This exploratory study aimed to generate an initial understanding of whether patterns of communication about depression and anxiety on popular social media among individuals with schizophrenia are consistent with offline representations of the illness. From January to July 2016, posts on Twitter were collected from a sample of Twitter users who self-identify as having a schizophrenia spectrum disorder (n = 203) and a randomly selected sample of control users (n = 173). Frequency and timing of communication about depression and anxiety were compared between groups. In total, the groups posted n = 1,544,122 tweets and users had similar characteristics. Twitter users with schizophrenia showed significantly greater odds of tweeting about depression compared with control users (OR = 2.69; 95% CI 1.76-4.10), and significantly greater odds of tweeting about anxiety compared with control users (OR = 1.81; 95% CI 1.20-2.73). This study offers preliminary insights that Twitter users with schizophrenia may express elevated symptoms of depression and anxiety in their online posts, which is consistent with clinical characteristics of schizophrenia observed in offline settings. Social media platforms could further our understanding of schizophrenia by informing a digital phenotype and may afford new opportunities to support early illness detection.

Characterizing Depression Issues on Sina Weibo

The prevalence of depression has increased significantly over the past few years both in developed and developing countries. However, many people with symptoms of depression still remain untreated or undiagnosed. Social media may be a tool to help researchers and clinicians to identify and support individuals who experience depression. More than 394,000,000 postings were collected from China’s most popular social media website, Sina Weibo. 1000 randomly selected depression-related postings was coded and analyzed to learn the themes of these postings, and a text classifier was built to identify the postings indicating depression. The identified depressed users were compared with the general population on demographic characteristics, diurnal patterns, and patterns of emoticon usage. We found that disclosure of depression was the most popular theme; depression displayers were more engaged with social media compared to non-depression displayers, the depression postings showed geographical variations, depression displayers tended to be active during periods of leisure and sleep, and depression displayers used negative emoticons more frequently than non-depression displayers. This study offers a broad picture of depression references on China’s social media, which may be cost effectively developed to detect and help individuals who may suffer from depression disorders.

Exploring online communication about cigarette smoking among Twitter users who self-identify as having schizophrenia

Novel approaches are needed to address elevated tobacco use among people with schizophrenia. This exploratory study examined the frequency, timing, and type of communication about tobacco-related content on Twitter among users who self-identify as having schizophrenia compared with users from the general population. Over a 200-day period from January to July 2016, Twitter users who self-identify as having a schizophrenia spectrum disorder (n = 203) and a randomly selected group of general population control users (n = 173) posted 1,544,122 tweets. Communication frequency did not differ between groups. Tweets containing tobacco-related keywords were extracted. Twitter users with schizophrenia posted significantly more tweets containing any tobacco-related terms (mean = 3.74; SD = 16.3) compared with control users (mean = 0.82; SD = 1.8). A significantly greater proportion of Twitter users with schizophrenia (45%; n = 92) posted tweets containing any tobacco terms compared with control users (30%; n = 52). Schizophrenia users showed significantly greater odds of tweeting about tobacco compared with control users (OR = 1.99; 95% CI 1.29-3.07). These findings suggest that online communication about tobacco may parallel real world trends of elevated tobacco use observed among people with schizophrenia. By showing that Twitter users who self-identify as having schizophrenia discuss tobacco-related content online, popular social media could inform smoking cessation efforts targeting this at-risk group.

High-resolution Temporal Representations of Alcohol and Tobacco Behaviors from Social Media Data

Understanding tobacco- and alcohol-related behavioral patterns is critical for uncovering risk factors and potentially designing targeted social computing intervention systems. Given that we make choices multiple times per day, hourly and daily patterns are critical for better understanding behaviors. Here, we combine natural language processing, machine learning and time series analyses to assess Twitter activity specifically related to alcohol and tobacco consumption and their sub-daily, daily and weekly cycles. Twitter self-reports of alcohol and tobacco use are compared to other data streams available at similar temporal resolution. We assess if discussion of drinking by inferred underage versus legal age people or discussion of use of different types of tobacco products can be differentiated using these temporal patterns. We find that time and frequency domain representations of behaviors on social media can provide meaningful and unique insights, and we discuss the types of behaviors for which the approach may be most useful.

Assessing Behavioral Stages From Social Media Data

Important work rooted in psychological theory posits that health behavior change occurs through a series of discrete stages. Our work builds on the field of social computing by identifying how social media data can be used to resolve behavior stages at high resolution (e.g. hourly/daily) for key population subgroups and times. In essence this approach opens new opportunities to advance psychological theories and better understand how our health is shaped based on the real, dynamic, and rapid actions we make every day. To do so, we bring together domain knowledge and machine learning methods to form a hierarchical classification of Twitter data that resolves different stages of behavior. We identify and examine temporal patterns of the identified stages, with alcohol as a use case (planning or looking to drink, currently drinking, and reflecting on drinking). Known seasonal trends are compared with findings from our methods. We discuss the potential health policy implications of detecting high frequency behavior stages.

Adolescent and Young Adult Use of Social Media for Health and Its Implications

PURPOSE

To determine how adolescents and young adults (AYAs) use social media to share health information and to assess attitudes toward using social media to obtain health information and communicate with medical providers.

METHODS

A cross-sectional study of AYAs, 12 years or older, attending a primary care adolescent and young adult clinic. Participants completed an anonymous survey about health-related social media use, personal health, and communication with their health care team.

RESULTS

Of the 244 patients approached, 204 enrolled (83.6% participation rate). Almost all (98%) had used social media within the prior month, but only 51.5% had shared health information in these networks. These participants shared about mood (76.2%), wellness (57.1%), and acute medical conditions (41.9%). Those with self-reported poor health were more likely to share health information than other groups. Privacy was the most important factor determining which platform to use. Only 25% thought that social media could provide them with useful health information. Few AYAs connected with their health care team on social media and most did not want to use this method; texting was preferred.

CONCLUSIONS

AYAs maintain their privacy on social media regarding their health. Those with self-perceived poor health are more likely to share health information, potentially biasing online content and impairing the generalizability of social media research. AYAs do not view social media as a useful source of health information, which may limit the utility of public health messages through these platforms, and it may not be adequate for communication between patients and their health care team.

Driver sleepiness on YouTube: A content analysis

Driver sleepiness is a major contributor to severe crashes and fatalities on our roads. Many people continue to drive despite being aware of feeling tired. Prevention relies heavily on education campaigns as it is difficult to police driver sleepiness. The video sharing social media site YouTube is extremely popular, particularly with at risk driver demographics. Content and popularity of uploaded videos can provide insight into the quality of publicly accessible driver sleepiness information. The purpose of this research was to answer two questions; firstly, how prevalent are driver sleepiness videos on YouTube? And secondly, what are the general characteristics of driver sleepiness videos in terms of (a) outlook on driver sleepiness, (b) tone, (c) countermeasures to driver sleepiness, and, (d) driver demographics. Using a keywords search, 442 relevant videos were found from a five year period (2nd December 2009-2nd December 2014). Tone, outlook, and countermeasure use were thematically coded. Driver demographic and video popularity data also were recorded. The majority of videos portrayed driver sleepiness as dangerous. However, videos that had an outlook towards driver sleepiness being amusing were viewed more often and had more mean per video comments and likes. Humorous videos regardless of outlook, were most popular. Most information regarding countermeasures to deal with driver sleepiness was accurate. Worryingly, 39.8% of videos with countermeasure information contained some kind of ineffective countermeasure. The use of humour to convey messages about the dangers of driver sleepiness may be a useful approach in educational interventions.

Social Media Listening for Routine Post-Marketing Safety Surveillance

INTRODUCTION

Post-marketing safety surveillance primarily relies on data from spontaneous adverse event reports, medical literature, and observational databases. Limitations of these data sources include potential under-reporting, lack of geographic diversity, and time lag between event occurrence and discovery. There is growing interest in exploring the use of social media ('social listening') to supplement established approaches for pharmacovigilance. Although social listening is commonly used for commercial purposes, there are only anecdotal reports of its use in pharmacovigilance. Health information posted online by patients is often publicly available, representing an untapped source of post-marketing safety data that could supplement data from existing sources.

OBJECTIVES

The objective of this paper is to describe one methodology that could help unlock the potential of social media for safety surveillance.

METHODS

A third-party vendor acquired 24 months of publicly available Facebook and Twitter data, then processed the data by standardizing drug names and vernacular symptoms, removing duplicates and noise, masking personally identifiable information, and adding supplemental data to facilitate the review process. The resulting dataset was analyzed for safety and benefit information.

RESULTS

In Twitter, a total of 6,441,679 Medical Dictionary for Regulatory Activities (MedDRA(®)) Preferred Terms (PTs) representing 702 individual PTs were discussed in the same post as a drug compared with 15,650,108 total PTs representing 946 individual PTs in Facebook. Further analysis revealed that 26 % of posts also contained benefit information.

CONCLUSION

Social media listening is an important tool to augment post-marketing safety surveillance. Much work remains to determine best practices for using this rapidly evolving data source.

Sleep Quality Prediction From Wearable Data Using Deep Learning

Background

The importance of sleep is paramount to health. Insufficient sleep can reduce physical, emotional, and mental well-being and can lead to a multitude of health complications among people with chronic conditions. Physical activity and sleep are highly interrelated health behaviors. Our physical activity during the day (ie, awake time) influences our quality of sleep, and vice versa. The current popularity of wearables for tracking physical activity and sleep, including actigraphy devices, can foster the development of new advanced data analytics. This can help to develop new electronic health (eHealth) applications and provide more insights into sleep science.

Objective

The objective of this study was to evaluate the feasibility of predicting sleep quality (ie, poor or adequate sleep efficiency) given the physical activity wearable data during awake time. In this study, we focused on predicting good or poor sleep efficiency as an indicator of sleep quality.

Methods

Actigraphy sensors are wearable medical devices used to study sleep and physical activity patterns. The dataset used in our experiments contained the complete actigraphy data from a subset of 92 adolescents over 1 full week. Physical activity data during awake time was used to create predictive models for sleep quality, in particular, poor or good sleep efficiency. The physical activity data from sleep time was used for the evaluation. We compared the predictive performance of traditional logistic regression with more advanced deep learning methods: multilayer perceptron (MLP), convolutional neural network (CNN), simple Elman-type recurrent neural network (RNN), long short-term memory (LSTM-RNN), and a time-batched version of LSTM-RNN (TB-LSTM).

Results

Deep learning models were able to predict the quality of sleep (ie, poor or good sleep efficiency) based on wearable data from awake periods. More specifically, the deep learning methods performed better than traditional linear regression. CNN had the highest specificity and sensitivity, and an overall area under the receiver operating characteristic (ROC) curve (AUC) of 0.9449, which was 46% better as compared with traditional linear regression (0.6463).

Conclusions

Deep learning methods can predict the quality of sleep based on actigraphy data from awake periods. These predictive models can be an important tool for sleep research and to improve eHealth solutions for sleep.

What Can We Learn About Mental Health Needs From Tweets Mentioning Dementia on World Alzheimer's Day?

BACKGROUND

Twitter can address the mental health challenges of dementia care. The aims of this study is to explore the contents and user interactions of tweets mentioning dementia to gain insights for dementia care.

METHODS

We collected 35,260 tweets mentioning Alzheimer’s or dementia on World Alzheimer’s Day, September 21^st in 2015. Topic modeling and social network analysis were applied to uncover content and structure of user communication.

RESULTS

Global users generated keywords related to mental health and care including #psychology and #mental health. There were similarities and differences between the UK and the US in tweet content. The macro-level analysis uncovered substantial public interest on dementia. The meso-level network analysis revealed that top leaders of communities were spiritual organizations and traditional media.

CONCLUSIONS

The application of topic modeling and multi-level network analysis while incorporating visualization techniques can promote a global level understanding regarding public attention, interests, and insights regarding dementia care and mental health.

Building a National Neighborhood Dataset From Geotagged Twitter Data for Indicators of Happiness, Diet, and Physical Activity

Background

Studies suggest that where people live, play, and work can influence health and well-being. However, the dearth of neighborhood data, especially data that is timely and consistent across geographies, hinders understanding of the effects of neighborhoods on health. Social media data represents a possible new data resource for neighborhood research.

Objective

The aim of this study was to build, from geotagged Twitter data, a national neighborhood database with area-level indicators of well-being and health behaviors.

Methods

We utilized Twitter’s streaming application programming interface to continuously collect a random 1% subset of publicly available geolocated tweets for 1 year (April 2015 to March 2016). We collected 80 million geotagged tweets from 603,363 unique Twitter users across the contiguous United States. We validated our machine learning algorithms for constructing indicators of happiness, food, and physical activity by comparing predicted values to those generated by human labelers. Geotagged tweets were spatially mapped to the 2010 census tract and zip code areas they fall within, which enabled further assessment of the associations between Twitter-derived neighborhood variables and neighborhood demographic, economic, business, and health characteristics.

Results

Machine labeled and manually labeled tweets had a high level of accuracy: 78% for happiness, 83% for food, and 85% for physical activity for dichotomized labels with the F scores 0.54, 0.86, and 0.90, respectively. About 20% of tweets were classified as happy. Relatively few terms (less than 25) were necessary to characterize the majority of tweets on food and physical activity. Data from over 70,000 census tracts from the United States suggest that census tract factors like percentage African American and economic disadvantage were associated with lower census tract happiness. Urbanicity was related to higher frequency of fast food tweets. Greater numbers of fast food restaurants predicted higher frequency of fast food mentions. Surprisingly, fitness centers and nature parks were only modestly associated with higher frequency of physical activity tweets. Greater state-level happiness, positivity toward physical activity, and positivity toward healthy foods, assessed via tweets, were associated with lower all-cause mortality and prevalence of chronic conditions such as obesity and diabetes and lower physical inactivity and smoking, controlling for state median income, median age, and percentage white non-Hispanic.

Conclusions

Machine learning algorithms can be built with relatively high accuracy to characterize sentiment, food, and physical activity mentions on social media. Such data can be utilized to construct neighborhood indicators consistently and cost effectively. Access to neighborhood data, in turn, can be leveraged to better understand neighborhood effects and address social determinants of health. We found that neighborhoods with social and economic disadvantage, high urbanicity, and more fast food restaurants may exhibit lower happiness and fewer healthy behaviors.

The Importance of Computer Science for Public Health Training: An Opportunity and Call to Action

A century ago, the Welch-Rose Report established a public health education system in the United States. Since then, the system has evolved to address emerging health needs and integrate new technologies. Today, personalized health technologies generate large amounts of data. Emerging computer science techniques, such as machine learning, present an opportunity to extract insights from these data that could help identify high-risk individuals and tailor health interventions and recommendations. As these technologies play a larger role in health promotion, collaboration between the public health and technology communities will become the norm. Offering public health trainees coursework in computer science alongside traditional public health disciplines will facilitate this evolution, improving public health's capacity to harness these technologies to improve population health.

Establishing a Link Between Prescription Drug Abuse and Illicit Online Pharmacies: Analysis of Twitter Data

Background

Youth and adolescent non-medical use of prescription medications (NUPM) has become a national epidemic. However, little is known about the association between promotion of NUPM behavior and access via the popular social media microblogging site, Twitter, which is currently used by a third of all teens.

Objective

In order to better assess NUPM behavior online, this study conducts surveillance and analysis of Twitter data to characterize the frequency of NUPM-related tweets and also identifies illegal access to drugs of abuse via online pharmacies.

Methods

Tweets were collected over a 2-week period from April 1-14, 2015, by applying NUPM keyword filters for both generic/chemical and street names associated with drugs of abuse using the Twitter public streaming application programming interface. Tweets were then analyzed for relevance to NUPM and whether they promoted illegal online access to prescription drugs using a protocol of content coding and supervised machine learning.

Results

A total of 2,417,662 tweets were collected and analyzed for this study. Tweets filtered for generic drugs names comprised 232,108 tweets, including 22,174 unique associated uniform resource locators (URLs), and 2,185,554 tweets (376,304 unique URLs) filtered for street names. Applying an iterative process of manual content coding and supervised machine learning, 81.72% of the generic and 12.28% of the street NUPM datasets were predicted as having content relevant to NUPM respectively. By examining hyperlinks associated with NUPM relevant content for the generic Twitter dataset, we discovered that 75.72% of the tweets with URLs included a hyperlink to an online marketing affiliate that directly linked to an illicit online pharmacy advertising the sale of Valium without a prescription.

Conclusions

This study examined the association between Twitter content, NUPM behavior promotion, and online access to drugs using a broad set of prescription drug keywords. Initial results are concerning, as our study found over 45,000 tweets that directly promoted NUPM by providing a URL that actively marketed the illegal online sale of prescription drugs of abuse. Additional research is needed to further establish the link between Twitter content and NUPM, as well as to help inform future technology-based tools, online health promotion activities, and public policy to combat NUPM online.

Agenda Setting for Health Promotion: Exploring an Adapted Model for the Social Media Era

Background

The foundation of best practice in health promotion is a robust theoretical base that informs design, implementation, and evaluation of interventions that promote the public’s health. This study provides a novel contribution to health promotion through the adaptation of the agenda-setting approach in response to the contribution of social media. This exploration and proposed adaptation is derived from a study that examined the effectiveness of Twitter in influencing agenda setting among users in relation to road traffic accidents in Saudi Arabia.

Objective

The proposed adaptations to the agenda-setting model to be explored reflect two levels of engagement: agenda setting within the social media sphere and the position of social media within classic agenda setting. This exploratory research aims to assess the veracity of the proposed adaptations on the basis of the hypotheses developed to test these two levels of engagement.

Methods

To validate the hypotheses, we collected and analyzed data from two primary sources: Twitter activities and Saudi national newspapers. Keyword mentions served as indicators of agenda promotion; for Twitter, interactions were used to measure the process of agenda setting within the platform. The Twitter final dataset comprised 59,046 tweets and 38,066 users who contributed by tweeting, replying, or retweeting. Variables were collected for each tweet and user. In addition, 518 keyword mentions were recorded from six popular Saudi national newspapers.

Results

The results showed significant ratification of the study hypotheses at both levels of engagement that framed the proposed adaptions. The results indicate that social media facilitates the contribution of individuals in influencing agendas (individual users accounted for 76.29%, 67.79%, and 96.16% of retweet impressions, total impressions, and amplification multipliers, respectively), a component missing from traditional constructions of agenda-setting models. The influence of organizations on agenda setting is also highlighted (in the data of user interactions, organizational accounts registered 17% and 14.74% as source and target of interactions, respectively). In addition, 13 striking similarities showed the relationship between newspapers and Twitter on the mentions trends line.

Conclusions

The effective use of social media platforms in health promotion intervention programs requires new strategies that consider the limitations of traditional communication channels. Conducting research is vital to establishing a strong basis for modifying, designing, and developing new health promotion strategies and approaches.