Social media interventions for precision public health: promises and risks

Measuring the burden of infodemics with a research toolkit for connecting information exposure, trust, and health behaviours

BACKGROUND

During a public health emergency, accurate and useful information can be drowned out by questions, concerns, information voids, conflicting information, and misinformation. Very few studies connect information exposure and trust to health behaviours, which limits available evidence to inform when and where to act to mitigate the burden of infodemics, especially in low resource settings. This research describes the features of a toolkit that can support studies linking information exposure to health behaviours at the individual level.

METHODS

To meet the needs of the research community, we determined the functional and non-functional requirements of a research toolkit that can be used in studies measuring topic-specific information exposure and health behaviours. Most data-driven infodemiology research is designed to characterise content rather than measure associations between information exposure and health behaviours. Studies also tend to be limited to specific social media platforms, are unable to capture the breadth of individual information exposure that occur online and offline, and cannot measure differences in trust by information source or content. Studies are also designed very differently, limiting synthesis of results.

RESULTS

We demonstrate a way to address these requirements via a web-based study platform that includes an app that participants use to record topic-specific information exposure, a browser plugin for tracking access to relevant webpages, questionnaires that can be delivered at any time during a study, and app-based incentives for participation such as visual analytics to compare trust levels with other participants. Other features of the platform include the ability to tailor studies to local contexts, ease of use for participants, and frictionless sharing of de-identified data for aggregating individual participant data in international meta-analyses.

CONCLUSIONS

Our proposed solution will be able to capture detailed data about information exposure and health behaviour data, standardise study design while simultaneously supporting localisation, and make it easy to synthesise individual participant data across studies. Future research will need to evaluate the toolkit in realistic scenarios to understand the usability of the toolkit for both participants and investigators.

Utility and usability evaluation of an information diary tool to measure health information access and exposure among patients with high cardiovascular risk

Background

Online health misinformation about statins potentially affects health decision-making on statin use and adherence. We developed an information diary platform (IDP) to measure topic-specific health information exposure where participants record what information they encounter. We evaluated the utility and usability of the smartphone diary from the participants' perspective.

Methods

We used a mixed-method design to evaluate how participants used the smartphone diary tool and their perspectives on usability. Participants were high cardiovascular-risk patients recruited from a primary care clinic and used the tool for a week. We measured usability with the System Usability Scale (SUS) questionnaire and interviewed participants to explore utility and usability issues.

Results

The information diary was available in three languages and tested with 24 participants. The mean SUS score was 69.8 ± 12.9. Five themes related to utility were: IDP functions as a health information diary; supporting discussion of health information with doctors; wanting a feedback function about credible information; increasing awareness of the need to appraise information; and wanting to compare levels of trust with other participants or experts. Four themes related to usability were: ease of learning and use; confusion about selecting the category of information source; capturing offline information by uploading photos; and recording their level of trust.

Conclusion

We found that the smartphone diary can be used as a research instrument to record relevant examples of information exposure. It potentially modifies how people seek and appraise topic-specific health information.

[Influencer Marketing in Healthcare: A Review]

BACKGROUND

Social media influencers play an important role in the (digital) life of millions of Germans. In the health sector, these protagonists and their channels are used not only with commercial intentions, but also increasingly for the communication of health messages. We want to investigate if influencers can play an important role in promoting health. We summarize the current scientific evidence on the use of influencer marketing in health communication.

METHODS

Selective literature search on PubMed with the search terms "Influencer marketing" and "health" and summary of the results.

RESULTS

We identified 173 publications, from which four fulfilled the inclusion criteria. For the classification of health influencers, we propose 5 categories.

CONCLUSION

So far, there is only weak evidence for health-promoting effects through the communication of influencers. Both in practice and in science, more knowledge about the health-promoting use of influencers needs to be gained.

Evaluating the impact of a linguistically and culturally tailored social media ad campaign on COVID-19 vaccine uptake among indigenous populations in Guatemala: a pre/post design intervention study

OBJECTIVES

To evaluate the impact of culturally and linguistically tailored informational videos delivered via social media campaigns on COVID-19 vaccine uptake in Indigenous Maya communities in Guatemala.

METHODS

Our team designed a series of videos utilising community input and evaluated the impact using a pre-post intervention design. In-person preintervention surveys were collected from a sample of respondents in four rural municipalities in Guatemala in March 2022. Facebook, Instagram and browser ads were flooded with COVID-19 vaccine informational videos in Spanish, Kaqchikel and Kiche for 3 weeks. Postintervention surveys were conducted by telephone among the same participants in April 2022. Logistic regression models were used to estimate the OR of COVID-19 vaccine uptake following exposure to the intervention videos.

RESULTS

Preintervention and postintervention surveys were collected from 1572 participants. The median age was 28 years; 63% (N=998) identified as women, and 36% spoke an Indigenous Mayan language. Twenty-one per cent of participants (N=327) reported watching the intervention content on social media. At baseline, 89% (N=1402) of participants reported having at least one COVID-19 vaccine, compared with 97% (N=1507) in the follow-up. Those who reported watching the videos had 1.78 times the odds (95% CI 1.14 to 2.77) of getting vaccinated after watching the videos compared with those who did not see the videos when adjusted by age, community, sex and language.

CONCLUSION

Our findings suggest that culturally and linguistically tailored videos addressing COVID-19 vaccine misinformation deployed over social media can increase vaccinations in a rural, indigenous population in Guatemala, implying that social media content can influence vaccination uptake. Providing accurate, culturally sensitive information in local languages from trusted sources may help increase vaccine uptake in historically marginalised populations.

Bibliometric and Altmetric Analysis of Retracted Articles on COVID-19

BACKGROUND

With greater use of social media platforms for promotions of research articles, retracted articles tend to receive approximately the same attention. We systematically analyzed retracted articles from retractionwatch.com to look at the Altmetric Attention Scores (AAS) garnered over a period of time in order to highlight the role of social media and other platforms in advertising retracted articles and its effect on the spread of misinformation.

METHODS

Retractionwatch.com was searched for coronavirus disease 2019 related retracted papers on November 6th, 2021. Articles were excluded based on lack of digital object identifier (DOI), if they were preprint articles, absent AAS, and incomplete AAS of pre retraction, post retraction, or both scores.

RESULTS

A total of 196 articles were found on the Retraction Watch website of which 189 were retracted papers and 7 were expression of concern (EOC). We then identified 175 articles after excluding those that did not have a DOI and 30 preprint articles were also excluded giving 145 articles. Further exclusion of articles with absent AAS and incomplete AAS resulted in a total of 22 articles.

CONCLUSION

Retracted articles receive significant online attention. Twitter and Mendeley were the most popular medium for publicizing retracted articles, therefore more focus should be given by journals and their Twitter accounts to discredit all their retracted articles. Preprints should be reconsidered as a whole by journals due to the huge risk they carry in disseminating false information.

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.

A Public Health Research Agenda for Managing Infodemics: Methods and Results of the First WHO Infodemiology Conference

Background

An infodemic is an overflow of information of varying quality that surges across digital and physical environments during an acute public health event. It leads to confusion, risk-taking, and behaviors that can harm health and lead to erosion of trust in health authorities and public health responses. Owing to the global scale and high stakes of the health emergency, responding to the infodemic related to the pandemic is particularly urgent. Building on diverse research disciplines and expanding the discipline of infodemiology, more evidence-based interventions are needed to design infodemic management interventions and tools and implement them by health emergency responders.

Objective

The World Health Organization organized the first global infodemiology conference, entirely online, during June and July 2020, with a follow-up process from August to October 2020, to review current multidisciplinary evidence, interventions, and practices that can be applied to the COVID-19 infodemic response. This resulted in the creation of a public health research agenda for managing infodemics.

Methods

As part of the conference, a structured expert judgment synthesis method was used to formulate a public health research agenda. A total of 110 participants represented diverse scientific disciplines from over 35 countries and global public health implementing partners. The conference used a laddered discussion sprint methodology by rotating participant teams, and a managed follow-up process was used to assemble a research agenda based on the discussion and structured expert feedback. This resulted in a five-workstream frame of the research agenda for infodemic management and 166 suggested research questions. The participants then ranked the questions for feasibility and expected public health impact. The expert consensus was summarized in a public health research agenda that included a list of priority research questions.

Results

The public health research agenda for infodemic management has five workstreams: (1) measuring and continuously monitoring the impact of infodemics during health emergencies; (2) detecting signals and understanding the spread and risk of infodemics; (3) responding and deploying interventions that mitigate and protect against infodemics and their harmful effects; (4) evaluating infodemic interventions and strengthening the resilience of individuals and communities to infodemics; and (5) promoting the development, adaptation, and application of interventions and toolkits for infodemic management. Each workstream identifies research questions and highlights 49 high priority research questions.

Conclusions

Public health authorities need to develop, validate, implement, and adapt tools and interventions for managing infodemics in acute public health events in ways that are appropriate for their countries and contexts. Infodemiology provides a scientific foundation to make this possible. This research agenda proposes a structured framework for targeted investment for the scientific community, policy makers, implementing organizations, and other stakeholders to consider.

Precision Public Health Campaign: Delivering Persuasive Messages to Relevant Segments Through Targeted Advertisements on Social Media

Although established marketing techniques have been applied to design more effective health campaigns, more often than not, the same message is broadcasted to large populations, irrespective of unique characteristics. As individual digital device use has increased, so have individual digital footprints, creating potential opportunities for targeted digital health interventions. We propose a novel precision public health campaign framework to structure and standardize the process of designing and delivering tailored health messages to target particular population segments using social media–targeted advertising tools. Our framework consists of five stages: defining a campaign goal, priority audience, and evaluation metrics; splitting the target audience into smaller segments; tailoring the message for each segment and conducting a pilot test; running the health campaign formally; and evaluating the performance of the campaigns. We have demonstrated how the framework works through 2 case studies. The precision public health campaign framework has the potential to support higher population uptake and engagement rates by encouraging a more standardized, concise, efficient, and targeted approach to public health campaign development.

Development of Reporting Guidelines for Social Media Research (RESOME) using a modified Delphi Method: Study protocol (Preprint)

Background

Objective

Methods

Results

Conclusions

International Registered Report Identifier (IRRID)

Predictors of Health Information-Seeking Behavior: Systematic Literature Review and Network Analysis

Background

People engage in health information–seeking behavior to support health outcomes, and being able to predict such behavior can inform the development of interventions to guide effective health information seeking. Obtaining a comprehensive list of the predictors of health information–seeking behavior through a systematic search of the literature and exploring the interrelationship of these predictors are critical first steps in this process.

Objective

This study aims to identify significant predictors of health information–seeking behavior in the primary literature, develop a common taxonomy for these predictors, and identify the evolution of the concerned research field.

Methods

A systematic search of PsycINFO, Scopus, and PubMed was conducted for all years up to and including December 10, 2019. Quantitative studies identifying significant predictors of health information–seeking behavior were included. Information seeking was broadly defined and not restricted to any source of health information. Data extraction of significant predictors was performed by 2 authors, and network analysis was conducted to observe the relationships between predictors with time.

Results

A total of 9549 articles were retrieved, and after the screening, 344 studies were retained for analysis. A total of 1595 significant predictors were identified. These predictors were categorized into 67 predictor categories, with the most central predictors being age, education, gender, health condition, and financial income. With time, the interrelationship of predictors in the network became denser, with the growth of new predictor grouping reaching saturation (1 new predictor identified) in the past 7 years, despite increasing publication rates.

Conclusions

A common taxonomy was developed to classify 67 significant predictors of health information–seeking behavior. A time-aggregated network method was developed to track the evolution of the research field, showing the maturation of new predictor terms and an increase in primary studies reporting multiple significant predictors of health information–seeking behavior. The literature has evolved with a decreased characterization of novel predictors of health information–seeking behavior. In contrast, we identified a parallel increase in the complexity of predicting health information–seeking behavior, with an increase in the literature describing multiple significant predictors.

Analyzing Twitter Data to Evaluate People's Attitudes towards Public Health Policies and Events in the Era of COVID-19

Policymakers and relevant public health authorities can analyze people’s attitudes towards public health policies and events using sentiment analysis. Sentiment analysis focuses on classifying and analyzing text sentiments. A Twitter sentiment analysis has the potential to monitor people’s attitudes towards public health policies and events. Here, we explore the feasibility of using Twitter data to build a surveillance system for monitoring people’s attitudes towards public health policies and events since the beginning of the COVID-19 pandemic. In this study, we conducted a sentiment analysis of Twitter data. We analyzed the relationship between the sentiment changes in COVID-19-related tweets and public health policies and events. Furthermore, to improve the performance of the early trained model, we developed a data preprocessing approach by using the pre-trained model and early Twitter data, which were available at the beginning of the pandemic. Our study identified a strong correlation between the sentiment changes in COVID-19-related Twitter data and public health policies and events. Additionally, the experimental results suggested that the data preprocessing approach improved the performance of the early trained model. This study verified the feasibility of developing a fast and low-human-effort surveillance system for monitoring people’s attitudes towards public health policies and events during a pandemic by analyzing Twitter data. Based on the pre-trained model and early Twitter data, we can quickly build a model for the surveillance system.

Addressing Military Sexual Violence by Proposing a Social Media Influencer Model

Military sexual violence (MSV) is a prevalent issue that uniquely affects mission readiness. Although research on MSV and social media is growing, examinations of possible interventions like those employing social media in this population are scant. Given the growing interest in targeting MSV, the present systematic review was conducted. The PRISMA framework was used to conduct a systematic review of MSV and social media (N = 71). Queries were limited to articles published between 2010 and 2020. SAGE Journals, PubMed, and JSTOR were utilized. Terms and potential combinations were entered into the databases in varying Boolean combinations. Additional recorders were identified for inclusion via the reference sections of relevant records. After removing duplicates from the query results, we selected records of suspected relevance by title and screened abstracts. Finally, articles with relevant abstracts were reviewed thoroughly to determine whether they met inclusion criteria for the review. The employments of military leaders in a social media intervention puts into practice the military's central values and development of its leadership core. This intervention promotes group solidarity while maximizing conversations around meaningful messages. Findings in this review suggest military leaders could feasibly employ a cost-effective global intervention using social media, as a tool to help actively address MSV.

Real-time Twitter interactions during World Breastfeeding Week: A case study and social network analysis

Using Twitter to implement public health awareness campaigns is on the rise, but campaign monitoring and evaluation are largely dependent on basic Twitter Analytics. To establish the potential of social network theory-based metrics in better understanding public health campaigns, we analyzed real-time user interactions on Twitter during the 2020 World Breastfeeding Week (WBW) as an exemplar case. Social network analysis (SNA), including community and influencer identification, as well as topic modeling were used to compare the activity of n = 29,958 campaign participants and n = 10,694 reference users from the six-months pre-campaign period. Users formed more inter-connected relationships during the campaign, retweeting and mentioning each other 46,161 times compared to 10,662 times in the prior six months. Campaign participants formed identifiable communities that were not only based on their geolocation, but also based on interests and professional background. While influencers who dominated the WBW conversations were disproportionally members of the scientific community, the campaign did mobilize influencers from the general public who seemed to play a "bridging" role between the public and the scientific community. Users communicated about the campaign beyond its original themes to also discuss breastfeeding within the context of social and racial inequities. Applying SNA allowed understanding of the breastfeeding campaign's messaging and engagement dynamics across communities and influencers. Moving forward, WBW could benefit from improving targeting to enhance geographic coverage and user interactions. As this exemplar case indicates, social network theory and analysis can be used to inform other public health campaigns with data on user interactions that go beyond traditional metrics.

Limited Role of Bots in Spreading Vaccine-Critical Information Among Active Twitter Users in the United States: 2017-2019

Objectives. To examine the role that bots play in spreading vaccine information on Twitter by measuring exposure and engagement among active users from the United States.Methods. We sampled 53 188 US Twitter users and examined who they follow and retweet across 21 million vaccine-related tweets (January 12, 2017-December 3, 2019). Our analyses compared bots to human-operated accounts and vaccine-critical tweets to other vaccine-related tweets.Results. The median number of potential exposures to vaccine-related tweets per user was 757 (interquartile range [IQR] = 168-4435), of which 27 (IQR = 6-169) were vaccine critical, and 0 (IQR = 0-12) originated from bots. We found that 36.7% of users retweeted vaccine-related content, 4.5% retweeted vaccine-critical content, and 2.1% retweeted vaccine content from bots. Compared with other users, the 5.8% for whom vaccine-critical tweets made up most exposures more often retweeted vaccine content (62.9%; odds ratio [OR] = 2.9; 95% confidence interval [CI] = 2.7, 3.1), vaccine-critical content (35.0%; OR = 19.0; 95% CI = 17.3, 20.9), and bots (8.8%; OR = 5.4; 95% CI = 4.7, 6.3).Conclusions. A small proportion of vaccine-critical information that reaches active US Twitter users comes from bots.

Use of Social Media to Promote Cancer Screening and Early Diagnosis: Scoping Review

BACKGROUND

Social media is commonly used in public health interventions to promote cancer screening and early diagnosis, as it can rapidly deliver targeted public health messages to large numbers of people. However, there is currently little understanding of the breadth of social media interventions and evaluations, whether they are effective, and how they might improve outcomes.

OBJECTIVE

This scoping review aimed to map the evidence for social media interventions to improve cancer screening and early diagnosis, including their impact on behavior change and how they facilitate behavior change.

METHODS

Five databases and the grey literature were searched to identify qualitative and quantitative evaluations of social media interventions targeting cancer screening and early diagnosis. Two reviewers independently reviewed each abstract. Data extraction was carried out by one author and verified by a second author. Data on engagement was extracted using an adapted version of the key performance indicators and metrics related to social media use in health promotion. Insights, exposure, reach, and differing levels of engagement, including behavior change, were measured. The behavior change technique taxonomy was used to identify how interventions facilitated behavior change.

RESULTS

Of the 23 publications and reports included, the majority (16/23, 70%) evaluated national cancer awareness campaigns (eg, breast cancer awareness month). Most interventions delivered information via Twitter (13/23, 57%), targeted breast cancer (12/23, 52%), and measured exposure, reach, and low- to medium-level user engagement, such as number of likes (9/23, 39%). There were fewer articles about colorectal and lung cancer than about breast and prostate cancer campaigns. One study found that interventions had less reach and engagement from ethnic minority groups. A small number of articles (5/23, 22%) suggested that some types of social media interventions might improve high-level engagement, such as intended and actual uptake of screening. Behavior change techniques, such as providing social support and emphasizing the consequences of cancer, were used to engage users. Many national campaigns delivered fundraising messages rather than actionable health messages.

CONCLUSIONS

The limited evidence suggests that social media interventions may improve cancer screening and early diagnosis. Use of evaluation frameworks for social media interventions could help researchers plan more robust evaluations that measure behavior change. We need a greater understanding of who engages with these interventions to know whether social media can be used to reduce some health inequalities in cancer screening and early diagnosis.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

RR2-10.1136/bmjopen-2019-033592.

Diffusing science through social networks: The case of breastfeeding communication on Twitter

Breastfeeding is one of many health practices known to support the survival and health of mother and infant, yet low breastfeeding rates persist globally. These rates may be influenced by limited diffusion of evidence-based research and guidelines from the scientific community (SC). As recently highlighted by the National Academy of Sciences, there is a need for the SC to diffuse its findings to the public more effectively online, as means to counteract the spread of misinformation. In response to this call, we gathered data from Twitter for one month from major breastfeeding hashtags resulting in an interconnected social network (n = 3,798 users). We then identified 59 influencers who disproportionately influenced information flow using social network analysis. These influencers were from the SC (e.g. academics, researchers, health care practitioners), as well as interested citizens (IC) and companies. We then conducted an ego-network analysis of influencer networks, developed ego maps, and compared diffusion metrics across the SC, IC and company influencers. We also qualitatively analyzed their tweets (n = 711) to understand the type of information being diffused. SC influencers were the least efficient communicators. Although having the highest tweeting activity (80% of tweets), they did not reach more individuals compared to IC and companies (two-step ego size: 220± 99, 188 ± 124, 169 ± 97 respectively, P = 0.28). Content analysis of tweets suggest IC are more active than the SC in diffusing evidence-based breastfeeding knowledge, with 35% of their tweets around recent research findings compared to only 12% by the SC. Nonetheless, in terms of outreach to the general public, the two-step networks of SC influences were more heterogenous than ICs (55.7 ± 5.07, 50.9 ± 12.0, respectively, P<0.001). Collectively, these findings suggest SC influencers may possess latent potential to diffuse research and evidence- based practices. However, the research suggests specific ways to enhance diffusion.

Breastfeeding promotion on Twitter: A social network and content analysis approach

The importance of breastfeeding for maternal and infant health is well‐established, yet complex and intertwined sociocultural barriers contribute to suboptimal breastfeeding rates in most countries. Large‐scale campaigns for evidence dissemination and promotion through targeted interventions on social media may help overcome some of these barriers. To date, most breastfeeding research on social media only focuses on content analysis, and there remains limited knowledge about the social networks of online communities (who interacts with whom), influencers in the breastfeeding space and the diffusion of evidence‐based knowledge. This study, grounded in social network theory, aims to better understand the breastfeeding communication landscape on Twitter including determining the presence of a breastfeeding network, communities and key influencers. Further, we characterize influencer interactions, roles and the content being shared. The study revealed an overall breastfeeding social network of 3,798 unique individuals (users) and 3,972 tweets with commonly used hashtags (e.g., #breastfeeding and #normalizebreastfeeding). Around one third of users (n = 1,324, 34%) exchanged pornographic content (PC) that sexualized breastfeeding. The non‐PC network (n = 2,474 users) formed 144 unique communities, and content flowing within the network was disproportionately influenced by 59 key influencers. However, these influencers had mostly inward‐oriented interaction (% composition, E‐I index: 47% professionals, −0.18; 41% interested citizens, −0.67; 12% companies, −0.18), limiting opportunities for evidence‐based dissemination to the lay public. Although more tweets about peer‐reviewed research findings were sent compared with tweets about nonevidence‐based lay recommendations, our findings suggest that it is the lay public who often communicated findings, which may be overcome through a targeted social network‐based intervention.

Leveraging risk maps of malaria vector abundance to guide control efforts reduces malaria incidence in Eastern Province, Zambia

Although transmission of malaria and other mosquito-borne diseases is geographically heterogeneous, in sub-Saharan Africa risk maps are rarely used to determine which communities receive vector control interventions. We compared outcomes in areas receiving different indoor residual spray (IRS) strategies in Eastern Province, Zambia: (1) concentrating IRS interventions within a geographical area, (2) prioritizing communities to receive IRS based on predicted probabilities of Anopheles funestus, and (3) prioritizing communities to receive IRS based on observed malaria incidence at nearby health centers. Here we show that the use of predicted probabilities of An. funestus to guide IRS implementation saw the largest decrease in malaria incidence at health centers, a 13% reduction (95% confidence interval = 5-21%) compared to concentrating IRS geographically and a 37% reduction (95% confidence interval = 30-44%) compared to targeting IRS based on health facility incidence. These results suggest that vector control programs could produce better outcomes by prioritizing IRS according to malaria-vector risk maps.

How do social media users process cancer prevention messages on Facebook? An eye-tracking study

OBJECTIVE

The quality of cancer-related information on social media (SM) is mixed, and exposure to inaccurate information may negatively affect knowledge, attitudes, and behaviors. This study examines SM users' attention to simulated Facebook posts related to cancer and identifies message features associated with increased attention.

METHODS

SM users (N = 53) participated in a mixed methods experimental study using eye-tracking technology, whereby participants' dwell time on message components was measured. Stimuli conditions included message format (narrative/non-narrative), information veracity, source (organization/individual), and cancer topic (HPV vaccine and sunscreen safety).

RESULTS

Pixel-size adjusted analyses revealed that average dwell time was longer on posts attributed to individuals and on narrative-based posts. The source of the message received nearly the same amount of dwell time as the text. Dwell time on other message components did not significantly differ by condition.

CONCLUSION

This study found that the source of a message attracted substantial attention, whereas other features were not associated with attention. The study illustrates how communication research can help us understand the processing of ubiquitous cancer-related messages on SM.

PRACTICAL IMPLICATIONS

Health communication practitioners should consider message features that garner attention when developing efforts to facilitate the exchange of evidence-based information and to mitigate the harms of misinformation.

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

PURPOSE OF REVIEW

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

RECENT FINDINGS

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

SUMMARY

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

Use of social media for cancer prevention and early diagnosis: scoping review protocol

INTRODUCTION

Social media platforms offer unique opportunities for health promotion messages focusing on cancer prevention and early diagnosis. However, there has been very little synthesis of the evaluation of such campaigns, limiting the ability to apply learning to the design of future social media campaigns. We aimed to provide a broad overview of the current research base on social media interventions for cancer prevention and early diagnosis, to identify knowledge gaps and to inform policy, practice and future research questions.

METHODS

We will use scoping review methodology to explore the available evidence on social media interventions for cancer prevention and early diagnosis, with a focus on methodological approaches. Quantitative and qualitative studies and reports will be identified through searching several research databases, through internet searching for grey literature and by screening the citations of studies included in the review. All identified studies will undergo independent title and abstract screening and full-text screening against inclusion and exclusion criteria. We plan to chart the data from included studies to record the characteristics of the social media interventions, resources, activities, outputs, outcomes and impact. Charted data will be collated and summarised using a narrative synthesis. The interpretation and implications of the findings will be enhanced by consultation with relevant stakeholders such as public health organisations, cancer charities, and patient and public involvement groups when preliminary results are available.

ETHICS AND DISSEMINATION

Ethical approval is not required for this scoping review. The results will be used to identify research questions for future systematic reviews and to inform the development of future social media interventions. We will disseminate findings in peer-reviewed journals and at relevant conferences.

Automatically Appraising the Credibility of Vaccine-Related Web Pages Shared on Social Media: A Twitter Surveillance Study

Background

Tools used to appraise the credibility of health information are time-consuming to apply and require context-specific expertise, limiting their use for quickly identifying and mitigating the spread of misinformation as it emerges.

Objective

The aim of this study was to estimate the proportion of vaccine-related Twitter posts linked to Web pages of low credibility and measure the potential reach of those posts.

Methods

Sampling from 143,003 unique vaccine-related Web pages shared on Twitter between January 2017 and March 2018, we used a 7-point checklist adapted from validated tools and guidelines to manually appraise the credibility of 474 Web pages. These were used to train several classifiers (random forests, support vector machines, and recurrent neural networks) using the text from a Web page to predict whether the information satisfies each of the 7 criteria. Estimating the credibility of all other Web pages, we used the follower network to estimate potential exposures relative to a credibility score defined by the 7-point checklist.

Results

The best-performing classifiers were able to distinguish between low, medium, and high credibility with an accuracy of 78% and labeled low-credibility Web pages with a precision of over 96%. Across the set of unique Web pages, 11.86% (16,961 of 143,003) were estimated as low credibility and they generated 9.34% (1.64 billion of 17.6 billion) of potential exposures. The 100 most popular links to low credibility Web pages were each potentially seen by an estimated 2 million to 80 million Twitter users globally.

Conclusions

The results indicate that although a small minority of low-credibility Web pages reach a large audience, low-credibility Web pages tend to reach fewer users than other Web pages overall and are more commonly shared within certain subpopulations. An automatic credibility appraisal tool may be useful for finding communities of users at higher risk of exposure to low-credibility vaccine communications.

Beyond Public Health Genomics: Can Big Data and Predictive Analytics Deliver Precision Public Health?

The field of public health genomics has matured in the past two decades and is beginning to deliver genomic-based interventions for health and health care. In the past few years, the terms precision medicine and precision public health have been used to include information from multiple fields measuring biomarkers as well as environmental and other variables to provide tailored interventions. In the context of public health, "precision" implies delivering the right intervention to the right population at the right time, with the goal of improving health for all. In addition to genomics, precision public health can be driven by "big data" as identified by volume, variety, and variability in biomedical, sociodemographic, environmental, geographic, and other information. Most current big data applications in health are in elucidating pathobiology and tailored drug discovery. We explore how big data and predictive analytics can contribute to precision public health by improving public health surveillance and assessment, and efforts to promote uptake of evidence-based interventions, by including more extensive information related to place, person, and time. We use selected examples drawn from child health, cardiovascular disease, and cancer to illustrate the promises of precision public health, as well as current methodologic and analytic challenges to big data to fulfill these promises.

Modeling Spatiotemporal Factors Associated With Sentiment on Twitter: Synthesis and Suggestions for Improving the Identification of Localized Deviations

Background

Studies examining how sentiment on social media varies depending on timing and location appear to produce inconsistent results, making it hard to design systems that use sentiment to detect localized events for public health applications.

Objective

The aim of this study was to measure how common timing and location confounders explain variation in sentiment on Twitter.

Methods

Using a dataset of 16.54 million English-language tweets from 100 cities posted between July 13 and November 30, 2017, we estimated the positive and negative sentiment for each of the cities using a dictionary-based sentiment analysis and constructed models to explain the differences in sentiment using time of day, day of week, weather, city, and interaction type (conversations or broadcasting) as factors and found that all factors were independently associated with sentiment.

Results

In the full multivariable model of positive (Pearson r in test data 0.236; 95% CI 0.231-0.241) and negative (Pearson r in test data 0.306; 95% CI 0.301-0.310) sentiment, the city and time of day explained more of the variance than weather and day of week. Models that account for these confounders produce a different distribution and ranking of important events compared with models that do not account for these confounders.

Conclusions

In public health applications that aim to detect localized events by aggregating sentiment across populations of Twitter users, it is worthwhile accounting for baseline differences before looking for unexpected changes.

A Software Tool Aimed at Automating the Generation, Distribution, and Assessment of Social Media Messages for Health Promotion and Education Research

Background

Social media offers promise for communicating the risks and health effects of harmful products and behaviors to larger and hard-to-reach segments of the population. Nearly 70% of US adults use some social media. However, rigorous research across different social media is vital to establish successful evidence-based health communication strategies that meet the requirements of the evolving digital landscape and the needs of diverse populations.

Objective

The aim of this study was to expand and test a software tool (Trial Promoter) to support health promotion and education research by automating aspects of the generation, distribution, and assessment of large numbers of social media health messages and user comments.

Methods

The tool supports 6 functions (1) data import, (2) message generation deploying randomization techniques, (3) message distribution, (4) import and analysis of message comments, (5) collection and display of message performance data, and (6) reporting based on a predetermined data dictionary. The tool was built using 3 open-source software products: PostgreSQL, Ruby on Rails, and Semantic UI. To test the tool’s utility and reliability, we developed parameterized message templates (N=102) based upon 2 government-sponsored health education campaigns, extracted images from these campaigns and a free stock photo platform (N=315), and topic-related hashtags (N=4) from Twitter. We conducted a functional correctness analysis of the generated social media messages to assess the algorithm’s ability to produce the expected output for each input. We defined 100% correctness as use of the message template text and substitution of 3 message parameters (ie, image, hashtag, and destination URL) without any error. The percent correct was calculated to determine the probability with which the tool generates accurate messages.

Results

The tool generated, distributed, and assessed 1275 social media health messages over 85 days (April 19 to July 12, 2017). It correctly used the message template text and substituted the message parameters 100% (1275/1275) of the time as verified by human reviewers and a custom algorithm using text search and attribute-matching techniques.

Conclusions

A software tool can effectively support the generation, distribution, and assessment of hundreds of health promotion messages and user comments across different social media with the highest degree of functional correctness and minimal human interaction. The tool has the potential to support social media–enabled health promotion research and practice: first, by enabling the assessment of large numbers of messages to develop evidence-based health communication, and second, by providing public health organizations with a tool to increase their output of health education messages and manage user comments. We call on readers to use and develop the tool and to contribute to evidence-based communication methods in the digital age.

HPV vaccine coverage in Australia and associations with HPV vaccine information exposure among Australian Twitter users

Introduction: Human papillomavirus (HPV) vaccine coverage in Australia is 80% for females and 76% for males. Attitudes may influence coverage but surveys measuring attitudes are resource-intensive. The aim of this study was to determine whether Twitter-derived estimates of HPV vaccine information exposure were associated with differences in coverage across regions in Australia.

Methods: Regional differences in information exposure were estimated from 1,103,448 Australian Twitter users and 655,690 HPV vaccine related tweets posted between 6 September 2013 and 1 September 2017. Tweets about HPV vaccines were grouped using topic modelling; an algorithm for clustering text-based data. Proportional exposure to topics across 25 regions in Australia were used as factors to model HPV vaccine coverage in females and males, and compared to models using employment and education as factors.

Results: Models using topic exposure measures were more closely correlated with HPV vaccine coverage (female: Pearson’s R = 0.75 [0.49 to 0.88]; male: R = 0.76 [0.51 to 0.89]) than models using employment and education as factors (female: 0.39 [−0.02 to 0.68]; male: 0.36 [−0.04 to 0.66]). In Australia, positively-framed news tended to reach more Twitter users overall, but vaccine-critical information made up higher proportions of exposures among Twitter users in low coverage regions, where distorted characterisations of safety research and vaccine-critical blogs were popular.

Conclusions: Twitter-derived models of information exposure were correlated with HPV vaccine coverage in Australia. Topic exposure measures may be useful for providing timely and localised reports of the information people access and share to inform the design of targeted vaccine promotion interventions.