Using social media to monitor mental health discussions - evidence from Twitter

Short-term changes in mental health help-seeking behaviors following exposure to multiple social stressors and a natural disaster

In 2020, unprecedented circumstances led to significant mental health consequences. Individuals faced mental health stressors that extended beyond the devastating impact of the COVID-19 pandemic, including widespread social unrest following the murder of George Floyd, an intense hurricane season in the Atlantic, and the politically divisive 2020 election. The objective of this analysis was to consider changes in help-seeking behavior following exposure to multiple social stressors and a natural disaster. Data from Crisis Text Line (CTL), a national text-based mental health crisis counseling service, was used to determine how help-seeking behavior changed in the wake of each event. Wilcoxon rank sum tests assessed changes in help-seeking behavior for each event in 2020 as compared to the same period in 2019. AutoRegressive Integrated Moving Average (ARIMA) models examined if changes in crisis conversation volumes following each event differed. Higher median conversation volumes noted for the COVID-19 pandemic (+1 to +5 conversations), Hurricane Laura (+1 to +7 conversations) and the 2020 Election (+1 to +26 conversations). ARIMA models show substantial increases in help-seeking behavior following the declaration of a national emergency for the COVID-19 pandemic (+4.3 to +38.2%) and following the 2020 election (+3 to +24.44%). Our analysis found that the mental health response following social stressors may be distinct from natural events, especially when natural disasters occur in the context of multiple social stressors. This analysis adds to the growing body of literature considering the mental health impact of exposure to multiple co-occurring societal stressors, like police violence and a global pandemic.

"What Do I Say? How Do I Say it?" Twitter as a Knowledge Dissemination Tool for Mental Health Research

This study aims to generate evidence-based guidelines for researchers regarding how to effectively disseminate mental health research via Twitter. Three hundred mental health research Tweets posted from September 2018 to September 2019 were sampled from two large Australian organizations. Twenty-seven predictor variables were coded for each Tweet across five thematic categories: messaging; research area; mental health area; external networks; and media features. Regression analyses were conducted to determine associations with engagement outcomes of Favourites, Retweets, and Comments. Less than half (n = 10) of predictor variables passed validity tests. Notably, conclusions could not reliably be drawn on whether a Tweet featured evidence-based information. Tweets were significantly more likely to be Retweeted if they contained a hyperlink or multimedia. Tweets were significantly more likely to receive comments if they focused on a specific population group. These associations remain significant when controlling for organization. These findings indicate that researchers may be able to maximize engagement on Twitter by highlighting the population groups that the research applies to and enriching Tweets with multimedia content. In addition, care should be taken to ensure users can infer which messages are evidence-based. Guidelines and an accompanying resource are proposed.

Does Organizational Messaging Make a Difference? Investigating Themes and Language Style in Twitter Discourse and Engagement by Mental Health Organizations

The present study investigated the latent topics and language styles present in mental health organizational discourse on Twitter. The researchers sought to analyze identifying the prevalence of and language used in social support messaging in tweets about mental health care, the overarching topics regarding mental health care, and predicted that tweets with higher engagement will have increased frequency of words with positively valenced emotion and cognitive processing. A GSDMM was run to uncover latent themes that emerged in a data set of 326.9k tweets and 7.2 m words about organizational discussions of mental health. A generalized linear model using the Poisson distribution was used to assess the role of engagement, positive emotion, and cognitive processing. The study found support for both positive emotion and cognitive processing as statistically significant predictors of engagement. Directions for research include the development of health message strategies, policy needs, and online interventions.

Changes in suicide-related tweets before and during the COVID-19 pandemic in France: The importance of social media monitoring in public health prediction

INTRODUCTION

The COVID-19 pandemic impacted mental health, as demonstrated by numerous studies. In recent years, especially during the pandemic, the use of social networks, including Twitter, increased. This suggests that this media could help with mental health monitoring, as attested by previous studies.

METHOD

We conducted a multidisciplinary study on French tweets that were posted between January 1, 2019, and December 31, 2021. We selected the tweets via the Twitter API (Application Programming Interface) using five keywords relating to suicide: want to die, suicidal ideation, commit suicide, suicidal, and suicide attempt. A word frequency analysis was performed, and the data were compared with the number of emergency visits for suicidal ideation before and during the COVID-19 pandemic as recorded by the French national suicide observatory.

RESULTS

We observed that 189,005 tweets were related to suicide in 2019, 261,993 in 2020 (+38.62% of that observed in 2019), and 301,177 in 2021 (+59.35% of that observed in 2019). We also observed an increase in the number of tweets containing control words in 2020 (+30.07% of that observed in 2019), but in 2021, the number almost fell back to the level of that in 2019 (+5.96% of that observed in 2019). Furthermore, the difference between both ratios (of suicide-related tweets and of tweets containing control words) was most significant during the third lockdown. The change in the number of suicide-related tweets followed a curve that overlapped with the change in the number of emergency visits following suicidal ideations, as reported by the French national suicide observatory. In conclusion, Twitter can be an adequate and reliable tool for screening for suicidal ideation in the general population.

An Evaluation of the Boys Do Cry Suicide Prevention Media Campaign on Twitter: Mixed Methods Approach

BACKGROUND

In most countries, men are more likely to die by suicide than women. Adherence to dominant masculine norms, such as being self-reliant, is linked to suicide in men in Western cultures. We created a suicide prevention media campaign, "Boys Do Cry," designed to challenge the "self-reliance" norm and encourage help-seeking in men. A music video was at the core of the campaign, which was an adapted version of the "Boys Don't Cry" song from "The Cure." There is evidence that suicide prevention media campaigns can encourage help-seeking for mental health difficulties.

OBJECTIVE

We aimed to explore the reach, engagement, and themes of discussion prompted by the Boys Do Cry campaign on Twitter.

METHODS

We used Twitter analytics data to investigate the reach and engagement of the Boys Do Cry campaign, including analyzing the characteristics of tweets posted by the campaign's hosts. Throughout the campaign and immediately after, we also used Twitter data derived from the Twitter Application Programming Interface to analyze the tweeting patterns of users related to the campaign. In addition, we qualitatively analyzed the content of Boys Do Cry-related tweets during the campaign period.

RESULTS

During the campaign, Twitter users saw the tweets posted by the hosts of the campaign a total of 140,650 times and engaged with its content a total of 4477 times. The 10 highest-performing tweets by the campaign hosts involved either a video or an image. Among the 10 highest-performing tweets, the first was one that included the campaign's core video; the second was a screenshot of the tweet posted by Robert Smith, the lead singer of The Cure, sharing the Boys Do Cry campaign's video and tagging the campaign's hosts. In addition, the pattern of Twitter activity for the campaign-related tweets was considerably higher during the campaign than in the immediate postcampaign period, with half of the activity occurring during the first week of the campaign when Robert Smith promoted the campaign. Some of the key topics of discussions prompted by the Boys Do Cry campaign on Twitter involved users supporting the campaign; referencing the original song, band, or lead singer; reiterating the campaign's messages; and having emotional responses to the campaign.

CONCLUSIONS

This study demonstrates that a brief media campaign such as Boys Do Cry can achieve good reach and engagement and can prompt discussions on Twitter about masculinity and suicide. Such discussions may lead to greater awareness about the importance of seeking help and providing support to those with mental health difficulties. However, this study suggests that longer, more intensive campaigns may be needed in order to amplify and sustain these results.

Analysis of Fluoride-Free Content on Twitter: Topic Modeling Study

BACKGROUND

Although social media has the potential to spread misinformation, it can also be a valuable tool for elucidating the social factors that contribute to the onset of negative beliefs. As a result, data mining has become a widely used technique in infodemiology and infoveillance research to combat misinformation effects. On the other hand, there is a lack of studies that specifically aim to investigate misinformation about fluoride on Twitter. Web-based individual concerns on the side effects of fluoridated oral care products and tap water stimulate the emergence and propagation of convictions that boost antifluoridation activism. In this sense, a previous content analysis-driven study demonstrated that the term fluoride-free was frequently associated with antifluoridation interests.

OBJECTIVE

This study aimed to analyze "fluoride-free" tweets regarding their topics and frequency of publication over time.

METHODS

A total of 21,169 tweets published in English between May 2016 and May 2022 that included the keyword "fluoride-free" were retrieved by the Twitter application programming interface. Latent Dirichlet allocation (LDA) topic modeling was applied to identify the salient terms and topics. The similarity between topics was calculated through an intertopic distance map. Moreover, an investigator manually assessed a sample of tweets depicting each of the most representative word groups that determined specific issues. Lastly, additional data visualization was performed regarding the total count of each topic of fluoride-free record and its relevance over time, using Elastic Stack software.

RESULTS

We identified 3 issues by applying the LDA topic modeling: "healthy lifestyle" (topic 1), "consumption of natural/organic oral care products" (topic 2), and "recommendations for using fluoride-free products/measures" (topic 3). Topic 1 was related to users' concerns about leading a healthier lifestyle and the potential impacts of fluoride consumption, including its hypothetical toxicity. Complementarily, topic 2 was associated with users' personal interests and perceptions of consuming natural and organic fluoride-free oral care products, whereas topic 3 was linked to users' recommendations for using fluoride-free products (eg, switching from fluoridated toothpaste to fluoride-free alternatives) and measures (eg, consuming unfluoridated bottled water instead of fluoridated tap water), comprising the propaganda of dental products. Additionally, the count of tweets on fluoride-free content decreased between 2016 and 2019 but increased again from 2020 onward.

CONCLUSIONS

Public concerns toward a healthy lifestyle, including the adoption of natural and organic cosmetics, seem to be the main motivation of the recent increase of "fluoride-free" tweets, which can be boosted by the propagation of fluoride falsehoods on the web. Therefore, public health authorities, health professionals, and legislators should be aware of the spread of fluoride-free content on social media to create and implement strategies against their potential health damage for the population.

Modeling information diffusion in social media: data-driven observations

Accurately modeling information diffusion within and across social media platforms has many practical applications, such as estimating the size of the audience exposed to a particular narrative or testing intervention techniques for addressing misinformation. However, it turns out that real data reveal phenomena that pose significant challenges to modeling: events in the physical world affect in varying ways conversations on different social media platforms; coordinated influence campaigns may swing discussions in unexpected directions; a platform's algorithms direct who sees which message, which affects in opaque ways how information spreads. This article describes our research efforts in the SocialSim program of the Defense Advanced Research Projects Agency. As formulated by DARPA, the intent of the SocialSim research program was "to develop innovative technologies for high-fidelity computational simulation of online social behavior ... [focused] specifically on information spread and evolution." In this article we document lessons we learned over the 4+ years of the recently concluded project. Our hope is that an accounting of our experience may prove useful to other researchers should they attempt a related project.

Using Twitter Data to Estimate the Prevalence of Symptoms of Mental Disorders in the United States During the COVID-19 Pandemic: Ecological Cohort Study

BACKGROUND

Existing research and national surveillance data suggest an increase of the prevalence of mental disorders during the COVID-19 pandemic. Social media platforms, such as Twitter, could be a source of data for estimation owing to its real-time nature, high availability, and large geographical coverage. However, there is a dearth of studies validating the accuracy of the prevalence of mental disorders on Twitter compared to that reported by the Centers for Disease Control and Prevention (CDC).

OBJECTIVE

This study aims to verify the feasibility of Twitter-based prevalence of mental disorders symptoms being an instrument for prevalence estimation, where feasibility is gauged via correlations between Twitter-based prevalence of mental disorder symptoms (ie, anxiety and depressive symptoms) and that based on national surveillance data. In addition, this study aims to identify how the correlations changed over time (ie, the temporal trend).

METHODS

State-level prevalence of anxiety and depressive symptoms was retrieved from the national Household Pulse Survey (HPS) of the CDC from April 2020 to July 2021. Tweets were retrieved from the Twitter streaming application programming interface during the same period and were used to estimate the prevalence of symptoms of mental disorders for each state using keyword analysis. Stratified linear mixed models were used to evaluate the correlations between the Twitter-based prevalence of symptoms of mental disorders and those reported by the CDC. The magnitude and significance of model parameters were considered to evaluate the correlations. Temporal trends of correlations were tested after adding the time variable to the model. Geospatial differences were compared on the basis of random effects.

RESULTS

Pearson correlation coefficients between the overall prevalence reported by the CDC and that on Twitter for anxiety and depressive symptoms were 0.587 (P<.001) and 0.368 (P<.001), respectively. Stratified by 4 phases (ie, April 2020, August 2020, October 2020, and April 2021) defined by the HPS, linear mixed models showed that Twitter-based prevalence for anxiety symptoms had a positive and significant correlation with CDC-reported prevalence in phases 2 and 3, while a significant correlation for depressive symptoms was identified in phases 1 and 3.

CONCLUSIONS

Positive correlations were identified between Twitter-based and CDC-reported prevalence, and temporal trends of these correlations were found. Geospatial differences in the prevalence of symptoms of mental disorders were found between the northern and southern United States. Findings from this study could inform future investigation on leveraging social media platforms to estimate symptoms of mental disorders and the provision of immediate prevention measures to improve health outcomes.

Social Service Workers' Use of Social Media to Obtain Client Information: Current Practices and Perspectives on a Potential Informatics Platform

To gain insight into current use of social-media platforms in human services delivery, we systematically surveyed 172 social-service workers from six agencies in a Midwest US city to gather data about social-media usage among social-service providers, potential challenges and benefits of using social media, and whether a social-media-based informatics platform could be valuable. Quantitative analyses showed that approximately half of participants have used social media to collect client-related information; nearly one-quarter indicated "often" or "nearly daily" use. Adjusting for the effects of worker characteristics, social-media use was associated with the type of agency involved and with increased tenure in social services. Adjusted results also showed that participants' comfort with using the potential application was greater in those agencies substantially involved with investigative/legal work. However, trust in the information collected by the potential application was a stronger, independent predictor of comfort using the tool. Qualitative analyses identified numerous challenges and ethical concerns, and positive and negative aspects of a social-media-based informatics platform. If the platform is to be created, work must be done carefully, fully considering ethical issues rightly raised by social service workers, existing agency policies, and professional standards. Future research should investigate ways to negotiate these complex challenges.

A Hybrid Deep Learning Model Using Grid Search and Cross-Validation for Effective Classification and Prediction of Suicidal Ideation from Social Network Data

Suicide deaths due to depression and mental stress are growing rapidly at an alarming rate. People freely express their feelings and emotions on social network sites while they feel hesitant to express such feelings during face-to-face interactions with their dear ones. In this study, a dataset comprising 20,000 posts was taken from Reddit and preprocessed into tokens using a variety of effective word2vec techniques. A new hybrid approach is proposed by combining the attention model in a convolutional neural network and long-short-term- memory. The objective of this research is to develop an effective learning model to evaluate the data on social media for the efficient and accurate identification of people with suicidal ideation. The proposed attention convolution long short-term memory (ACL) model uses hyperparameter tuning using a grid search to select optimized hyperparameters. From the experimental evaluation, it is shown that the proposed model, that is, ACL with Glove embedding after hyperparameter tuning gives the highest Accuracy of 88.48%, Precision of 87.36%, F1 score of 90.82% and specificity of 79.23% and ACL with Random embedding gives the highest Recall of 94.94% when compared to the state-of-the-art algorithms.

A Theoretically Based Analysis of Twitter Conversations about Trauma and Mental Health: Examining Responses to Storylines on the Television Show Queen Sugar

Entertainment programming in the United States has long addressed major public health issues. In the present study, we used a culture-centric approach to systematically investigate the role of television storylines in facilitating health-related conversations on social media. In particular, we examined Twitter conversations about sexual and police-involved trauma prompted by portrayals on the fictional television drama Queen Sugar. Guided by the culture-centric model of narratives in health promotion, we classified the tweets (N = 1,671) into four main thematic categories: identification, social proliferation, emotions, and intentions. The analysis also revealed several subthemes, including identification with characters and cultural elements, expressions of pain and joy, information seeking and sharing, and the need to address intergenerational trauma and promote intergenerational conversations. The data suggests that Twitter may provide a vehicle for engaging in difficult conversations. We discuss the theoretical and practical implications of the study for mental health communication with Black Americans.

Language-agnostic deep learning framework for automatic monitoring of population-level mental health from social networks

In many countries, mental health issues are among the most serious public health concerns. National mental health statistics are frequently collected from reported patient cases or government-sponsored surveys, which have restricted coverage, frequency, and timeliness. Many domains of study, including public healthcare and biomedical informatics, have recently adopted social media data as a feasible real-time alternative to traditional methods of gathering representative information at the population level in a variety of contexts. However, because of the limits of fundamental natural language processing tools and labeled corpora in countries with limited natural language resources, such as Thailand, implementing social media systems to monitor mental health signals could be challenging. This paper presents LAPoMM, a novel framework for monitoring real-time mental health indicators from social media data without using labeled datasets in low-resource languages. Specifically, we use cross-lingual methods to train language-agnostic models and validate our framework by examining cross-correlations between the aggregate predicted mental signals and real-world administrative data from Thailand's Department of Mental Health, which includes monthly depression patients and reported cases of suicidal attempts. A combination of a language-agnostic representation and a deep learning classification model outperforms all other cross-lingual techniques for recognizing various mental signals in Tweets, such as emotions, sentiments, and suicidal tendencies. The correlation analyses discover a strong positive relationship between actual depression cases and the predicted negative sentiment signals as well as suicide attempts and negative signals (e.g., fear, sadness, and disgust) and suicidal tendency. These findings establish the effectiveness of our proposed framework and its potential applications in monitoring population-level mental health using large-scale social media data. Furthermore, because the language-agnostic model utilized in the methodology is capable of supporting a wide range of languages, the proposed LAPoMM framework can be easily generalized for analogous applications in other countries with limited language resources.

Synergy between Public and Private Healthcare Organizations during COVID-19 on Twitter

BACKGROUND

Social media platforms (SMPs) are frequently used by various pharmaceutical companies, public health agencies, and NGOs for communicating health concerns, new advancements, and potential outbreaks. While the benefits of using them as a tool have been extensively discussed, the online activity of various healthcare organizations on SMPs during COVID-19 in terms of engagement and sentiment forecasting has not been thoroughly investigated.

OBJECTIVE

The purpose of this research is to analyze the nature of information shared on Twitter, understand the public engagement generated on it, and forecast the sentiment score for various organizations.

METHODS

Data was collected from the Twitter handles of five pharmaceutical companies, ten U.S. and Canadian public health agencies, and World Health Organization (WHO) between January 01, 2017 - December 31, 2021. A total of 181,469 tweets were divided into two phases for the analysis: before COVID-19 and during COVID-19, based on the confirmation of the first COVID-19 community transmission case in North America on February 26, 2020. We conducted content analysis to generate health-related topics using Natural Language Processing (NLP) based topic modeling techniques, analyzed public engagement on Twitter, and performed sentiment forecasting using 16 univariate moving-average and machine learning (ML) models to understand the correlation between public opinion and tweet contents.

RESULTS

We utilized the topics modeled from the tweets authored by the health organizations chosen for our analysis using Non-Negative Matrix Factorization (NMF) ('c_umass' scores: -3.6530 and -3.7944, before COVID-19 and during COVID-19 respectively). The topics are - 'Chronic Diseases', 'Health Research', 'Community Healthcare', 'Medical Trials', 'COVID-19', 'Vaccination', 'Nutrition and Well-being', and 'Mental Health'. In terms of user impact, WHO (user impact: 4171.24) had the highest impact overall, followed by the public health agencies, CDC (user impact: 2895.87), and NIH (user impact: 891.06). Among pharmaceutical companies, Pfizer's user impact was the highest at 97.79. Furthermore, for sentiment forecasting, ARIMA and SARIMAX models performed best on the majority of the subsets of data (divided as per the health organization and time-period), with Mean Absolute Error (MAE) between 0.027 - 0.084, Mean Squared Error (MSE) between 0.001 - 0.011, and Root Mean Squared Error (RMSE) between 0.031 - 0.105.

CONCLUSIONS

Our findings indicate that people engage more on topics like 'COVID-19' than 'Medical Trials', 'Customer Experience'. Also, there are notable differences in the user engagement levels across organizations. Global organizations, like WHO, show wide variations in engagement levels over time. The sentiment forecasting method discussed presents a way for organizations to structure their future content to ensure maximum user engagement.

CLINICALTRIAL

Exploring Public Perceptions of Dental Care Affordability in the United States: A Mixed Method Analysis via Twitter

BACKGROUND

Dental care expenses are reported to present higher financial barriers than any other type of health care service in the United States. Social media platforms such as Twitter have become a source of public health communication and surveillance. Previous studies have demonstrated the usefulness of Twitter in exploring public opinion on aspects of dental care. To date, no studies have leveraged Twitter to examine public sentiments regarding dental care affordability in the U.S.

OBJECTIVE

The aim of this study was to understand public perceptions of dental care affordability in the U.S. on the social media site, Twitter.

METHODS

Tweets posted between September 1, 2017 and September 30, 2021 were collected using the Snscrape application. Query terms were selected a priori to represent dentistry and financial aspects associated with dental treatment. Data were analyzed qualitatively using both deductive and inductive approaches. Ten percent of all included tweets were coded to identify prominent themes and subthemes. The entire sample of included tweets were then independently coded into the thematic categories. Quantitative data analyses included: geographic distribution of tweets by state; volume analysis of tweets over time; distribution of tweets by content theme.

RESULTS

A final sample of 5,314 tweets were included in the study. Thematic analysis identified the following prominent themes: 1) general sentiments (1614 tweets, 30.4%); 2) delaying or forgoing dental care (1190 tweets, 22.4%); 3) payment strategies (1019 tweets, 19.2%); 4) insurance (767 tweets, 14.4%); and 5) policy statements (724 tweets, 13.6%). Geographic distributions of tweets established California, Texas, Florida, New York as the states with the most tweets. A word cloud revealed that "insurance", "need", and "work" were the most frequently used words. Qualitative analysis revealed barriers faced by individuals to accessing dental care, strategies taken to cope with dental pain, and public perceptions on aspects of dental care policy. The volume and thematic trends of tweets corresponded to relevant societal events: The Coronavirus disease 2019 (COVID-19) pandemic and debates on healthcare policy resulting from the election of President Joseph R. Biden.

CONCLUSIONS

Findings illustrate the real-time sentiment of social media users toward the cost of dental treatment and suggest shortcomings in funding that may be representative of greater systemic failures in the provision of dental care. Thus, this study provides insights for policy makers and dental professionals who strive to increase access to dental care.

Consumer perceptions of telehealth for mental health or substance abuse: a Twitter-based topic modeling analysis

Objective

The objective of this study is to understand the primary topics of consumer discussion on Twitter associated with telehealth for mental health or substance abuse for prepandemic versus during-pandemic time-periods, using a state-of-the-art machine learning (ML) natural language processing (NLP) method.

Materials and Methods

The primary methodological phases of this project were: (1) collecting, cleaning, and filtering data (tweets) from January 2014 to June 2021, (2) describing the final corpus, (3) running and optimizing Bidirectional Encoder Representations from Transformers (BERT; using BERTopic in Python) models, and (4) human refinement of topic model results and thematic classification of topics.

Results

The number of tweets in this context increased by 4 times during the pandemic (2017 tweets prepandemic vs 8672 tweets during the pandemic). During the pandemic topics were more frequently mental health related than substance abuse related. Top during-pandemic topics were therapy, suicide, pain (associated with burnout and drinking), and mental health diagnoses such as ADHD and autism. Anxiety was a key topic of discussion both pre- and during the pandemic.

Discussion

Telehealth for mental health and substance abuse is being discussed more frequently online, which implies growing demand. Given the topics extracted as proxies for demand, the most demand is currently for telehealth for mental health primarily, especially for children, parents, and therapy for those with anxiety or depression, and substance abuse secondarily.

Conclusions

Scarce telehealth resources can be allocated more efficiently if topics of consumer discussion are included in resource allocation decision- and policy-making processes.

Telehealth for mental health and substance abuse is being discussed more frequently online. To determine what aspects of telehealth for mental health and/or substance abuse were being discussed most on Twitter, both before the pandemic and during the pandemic, we downloaded relevant tweets and ran a specialized machine learning model that extracts the most popular keywords from tweets as well as combines similar keywords into overall topics. We find 33 relevant topics prepandemic and 32 relevant topics during the pandemic to be relevant in this context. Given the topics extracted as proxies for demand, the most demand is currently for telehealth for mental health primarily, especially for children, parents, and therapy for those with anxiety or depression, and substance abuse secondarily. We also find that therapy and therapists were the top areas of discussion in regard to telehealth for mental health and/or substance abuse during the pandemic. These results can be applied to telehealth decision-making processes. In particular, scarce telehealth resources can be allocated more efficiently, particularly to those who currently need or want them most, if topics of consumer discussion are included in resource allocation decision- and policy-making processes.

Deep neural networks for simultaneously capturing public topics and sentiments during a pandemic. Application to a COVID-19 tweet dataset

Background

Public engagement is a key element for mitigating pandemics, and a good understanding of public opinion could help to encourage the successful adoption of public health measures by the population. In past years, deep learning has been increasingly applied to the analysis of text from social networks. However, most of the developed approaches can only capture topics or sentiments alone but not both together.

Objective

Here, we aimed to develop a new approach, based on deep neural networks, for simultaneously capturing public topics and sentiments and applied it to tweets sent just after the announcement of the COVID-19 pandemic by the World Health Organization (WHO).

Methods

A total of 1,386,496 tweets were collected, preprocessed, and split with a ratio of 80:20 into training and validation sets, respectively. We combined lexicons and convolutional neural networks to improve sentiment prediction. The trained model achieved an overall accuracy of 81% and a precision of 82% and was able to capture simultaneously the weighted words associated with a predicted sentiment intensity score. These outputs were then visualized via an interactive and customizable web interface based on a word cloud representation. Using word cloud analysis, we captured the main topics for extreme positive and negative sentiment intensity scores.

Results

In reaction to the announcement of the pandemic by the WHO, 6 negative and 5 positive topics were discussed on Twitter. Twitter users seemed to be worried about the international situation, economic consequences, and medical situation. Conversely, they seemed to be satisfied with the commitment of medical and social workers and with the collaboration between people.

Conclusions

We propose a new method based on deep neural networks for simultaneously extracting public topics and sentiments from tweets. This method could be helpful for monitoring public opinion during crises such as pandemics.

Using social media listening and data mining to understand travellers' perspectives on travel disease risks and vaccine-related attitudes and behaviours

BACKGROUND

Travellers can access online information to research and plan their expeditions/excursions, and seek travel-related health information. We explored German travellers' attitude and behaviour toward vaccination, and their travel-related health information seeking activities.

METHODS

We used two approaches: web 'scraping' of comments on German travel-related sites and an online survey. 'Scraping' of travel-related sites was undertaken using keywords/synonyms to identify vaccine- and disease-related posts. The raw unstructured text extracted from online comments was converted to a structured dataset using Natural Language Processing Techniques. Traveller personas were defined using K-means based on the online survey results, with cluster (i.e. persona) descriptions made from the most discriminant features in a distinguished set of observations. The web-scraped profiles were mapped to the personas identified. Travel and vaccine-related behaviours were described for each persona.

RESULTS

We identified ~2.6 million comments; ~880 k were unique and mentioned ~280 k unique trips by ~65 k unique profiles. Most comments were on destinations in Europe (37%), Africa (21%), Southeast Asia (12%) and the Middle East (11%). Eight personas were identified: 'middle-class family woman', 'young woman travelling with partner', 'female globe-trotter', 'upper-class active man', 'single male traveller', 'retired traveller', 'young backpacker', and 'visiting friends and relatives'. Purpose of travel was leisure in 82-94% of profiles, except the 'visiting friends and relatives' persona. Malaria and rabies were the most commented diseases with 12.7 k and 6.6 k comments, respectively. The 'middle-class family woman' and the 'upper-class active man' personas were the most active in online conversations regarding endemic disease and vaccine-related topics, representing 40% and 19% of comments, respectively. Vaccination rates were 54%-71% across the traveller personas in the online survey. Reasons for vaccination reluctance included perception of low risk to disease exposure (21%), price (14%), fear of side effects (12%) and number of vaccines (11%).

CONCLUSIONS

The information collated on German traveller personas and behaviours toward vaccinations should help guide counselling by healthcare professionals.

The COVID-19 Pandemic and Mental Health Concerns on Twitter in the United States

Background

During the COVID-19 pandemic, mental health concerns (such as fear and loneliness) have been actively discussed on social media. We aim to examine mental health discussions on Twitter during the COVID-19 pandemic in the US and infer the demographic composition of Twitter users who had mental health concerns.

Methods

COVID-19-related tweets from March 5th, 2020, to January 31st, 2021, were collected through Twitter streaming API using keywords (i.e., "corona," "covid19," and "covid"). By further filtering using keywords (i.e., "depress," "failure," and "hopeless"), we extracted mental health-related tweets from the US. Topic modeling using the Latent Dirichlet Allocation model was conducted to monitor users' discussions surrounding mental health concerns. Deep learning algorithms were performed to infer the demographic composition of Twitter users who had mental health concerns during the pandemic.

Results

We observed a positive correlation between mental health concerns on Twitter and the COVID-19 pandemic in the US. Topic modeling showed that "stay-at-home," "death poll," and "politics and policy" were the most popular topics in COVID-19 mental health tweets. Among Twitter users who had mental health concerns during the pandemic, Males, White, and 30-49 age group people were more likely to express mental health concerns. In addition, Twitter users from the east and west coast had more mental health concerns.

Conclusions

The COVID-19 pandemic has a significant impact on mental health concerns on Twitter in the US. Certain groups of people (such as Males and White) were more likely to have mental health concerns during the COVID-19 pandemic.

Harnessing Twitter data to survey public attention and attitudes towards COVID-19 vaccines in the UK

Attitudes to COVID-19 vaccination vary considerably within and between countries. Although the contribution of socio-demographic factors to these attitudes has been studied, the role of social media and how it interacts with news about vaccine development and efficacy is uncertain. We examined around 2 million tweets from 522,893 persons in the UK from November 2020 to January 2021 to evaluate links between Twitter content about vaccines and major scientific news announcements about vaccines. The proportion of tweets with negative vaccine content varied, with reductions of 20-24% on the same day as major news announcement. However, the proportion of negative tweets reverted back to an average of around 40% within a few days. Engagement rates were higher for negative tweets. Public health messaging could consider the dynamics of Twitter-related traffic and the potential contribution of more targeted social media campaigns to address vaccine hesitancy.

Artificial intelligence applications in social media for depression screening: A systematic review protocol for content validity processes

BACKGROUND

The popularization of social media has led to the coalescing of user groups around mental health conditions; in particular, depression. Social media offers a rich environment for contextualizing and predicting users' self-reported burden of depression. Modern artificial intelligence (AI) methods are commonly employed in analyzing user-generated sentiment on social media. In the forthcoming systematic review, we will examine the content validity of these computer-based health surveillance models with respect to standard diagnostic frameworks. Drawing from a clinical perspective, we will attempt to establish a normative judgment about the strengths of these modern AI applications in the detection of depression.

METHODS

We will perform a systematic review of English and German language publications from 2010 to 2020 in PubMed, APA PsychInfo, Science Direct, EMBASE Psych, Google Scholar, and Web of Science. The inclusion criteria span cohort, case-control, cross-sectional studies, randomized controlled studies, in addition to reports on conference proceedings. The systematic review will exclude some gray source materials, specifically editorials, newspaper articles, and blog posts. Our primary outcome is self-reported depression, as expressed on social media. Secondary outcomes will be the types of AI methods used for social media depression screen, and the clinical validation procedures accompanying these methods. In a second step, we will utilize the evidence-strengthening Population, Intervention, Comparison, Outcomes, Study type (PICOS) tool to refine our inclusion and exclusion criteria. Following the independent assessment of the evidence sources by two authors for the risk of bias, the data extraction process will culminate in a thematic synthesis of reviewed studies.

DISCUSSION

We present the protocol for a systematic review which will consider all existing literature from peer reviewed publication sources relevant to the primary and secondary outcomes. The completed review will discuss depression as a self-reported health outcome in social media material. We will examine the computational methods, including AI and machine learning techniques which are commonly used for online depression surveillance. Furthermore, we will focus on standard clinical assessments, as indicating content validity, in the design of the algorithms. The methodological quality of the clinical construct of the algorithms will be evaluated with the COnsensus-based Standards for the selection of health status Measurement Instruments (COSMIN) framework. We conclude the study with a normative judgment about the current application of AI to screen for depression on social media.

SYSTEMATIC REVIEW REGISTRATION

International Prospective Register of Systematic Reviews PROSPERO (registration number CRD42020187874).

Using a mixed methods approach to identify public perception of vaping risks and overall health outcomes on Twitter during the 2019 EVALI outbreak

INTRODUCTION

Vaping product use (i.e., e-cigarettes) has been rising since 2000 in the United States. Negative health outcomes associated with vaping products have created public uncertainty and debates on social media platforms. This study explores the feasibility of using social media as a surveillance tool to identify relevant posts and at-risk vaping users.

METHODS

Using an interdisciplinary method that leverages natural language processing and manual content analysis, we extracted and analyzed 794,620 vaping-related tweets on Twitter. After observing significant increases in vaping-related tweets in July, August, and September 2019, additional human coding was completed on a subset of these tweets to better understand primary themes of vaping-related discussions on Twitter during this time frame.

RESULTS

We found significant increases in tweets related to negative health outcomes such as acute lung injury and respiratory issues during the outbreak of e-cigarette/vaping associated lung injury (EVALI) in the fall of 2019. Positive sentiment toward vaping remained high, even across the peak of this outbreak in July, August, and September. Tweets mentioning the public perceptions of youth risk were concerning, as were increases in marketing and marijuana-related tweets during this time.

DISCUSSION

The preliminary results of this study suggest the feasibility of using Twitter as a means of surveillance for public health crises, and themes found in this research could aid in specifying those groups or populations at risk on Twitter. As such, we plan to build automatic detection algorithms to identify these unique vaping users to connect them with a digital intervention in the future.

The Application of the Principles of Responsible AI on Social Media Marketing for Digital Health

Social media enables medical professionals and authorities to share, disseminate, monitor, and manage health-related information digitally through online communities such as Twitter and Facebook. Simultaneously, artificial intelligence (AI) powered social media offers digital capabilities for organizations to select, screen, detect and predict problems with possible solutions through digital health data. Both the patients and healthcare professionals have benefited from such improvements. However, arising ethical concerns related to the use of AI raised by stakeholders need scrutiny which could help organizations obtain trust, minimize privacy invasion, and eventually facilitate the responsible success of AI-enabled social media operations. This paper examines the impact of responsible AI on businesses using insights from analysis of 25 in-depth interviews of health care professionals. The exploratory analysis conducted revealed that abiding by the responsible AI principles can allow healthcare businesses to better take advantage of the improved effectiveness of their social media marketing initiatives with their users. The analysis is further used to offer research propositions and conclusions, and the contributions and limitations of the study have been discussed.

Indications of Depressive Symptoms During the COVID-19 Pandemic in Germany: Comparison of National Survey and Twitter Data

BACKGROUND

The current COVID-19 pandemic is associated with extensive individual and societal challenges, including challenges to both physical and mental health. To date, the development of mental health problems such as depressive symptoms accompanying population-based federal distancing measures is largely unknown, and opportunities for rapid, effective, and valid monitoring are currently a relevant matter of investigation.

OBJECTIVE

In this study, we aim to investigate, first, the temporal progression of depressive symptoms during the COVID-19 pandemic and, second, the consistency of the results from tweets and survey-based self-reports of depressive symptoms within the same time period.

METHODS

Based on a cross-sectional population survey of 9011 German adolescents and adults (n=4659, 51.7% female; age groups from 15 to 50 years and older) and a sample of 88,900 tweets (n=74,587, 83.9% female; age groups from 10 to 50 years and older), we investigated five depressive symptoms (eg, depressed mood and energy loss) using items from the Patient Health Questionnaire (PHQ-8) before, during, and after relaxation of the first German social contact ban from January to July 2020.

RESULTS

On average, feelings of worthlessness were the least frequently reported symptom (survey: n=1011, 13.9%; Twitter: n=5103, 5.7%) and fatigue or loss of energy was the most frequently reported depressive symptom (survey: n=4472, 51.6%; Twitter: n=31,005, 34.9%) among both the survey and Twitter respondents. Young adult women and people living in federal districts with high COVID-19 infection rates were at an increased risk for depressive symptoms. The comparison of the survey and Twitter data before and after the first contact ban showed that German adolescents and adults had a significant decrease in feelings of fatigue and energy loss over time. The temporal progression of depressive symptoms showed high correspondence between both data sources (ρ=0.76-0.93; P<.001), except for diminished interest and depressed mood, which showed a steady increase even after the relaxation of the contact ban among the Twitter respondents but not among the survey respondents.

CONCLUSIONS

Overall, the results indicate relatively small differences in depressive symptoms associated with social distancing measures during the COVID-19 pandemic and highlight the need to differentiate between positive (eg, energy level) and negative (eg, depressed mood) associations and variations over time. The results also underscore previous suggestions of Twitter data's potential to help identify hot spots of declining and improving public mental health and thereby help provide early intervention measures, especially for young and middle-aged adults. Further efforts are needed to investigate the long-term consequences of recurring lockdown phases and to address the limitations of social media data such as Twitter data to establish real-time public mental surveillance approaches.

Analysis and visualization of COVID-19 discourse on Twitter using data science: a case study of the USA, the UK and India

Purpose

This paper aims to understand, examine and interpret the main concerns and emotions of the people regarding COVID-19 pandemic in the UK, the USA and India using Data Science measures.

Design/methodology/approach

This study implements unsupervised and supervised machine learning methods, i.e. topic modeling and sentiment analysis on Twitter data for extracting the topics of discussion and calculating public sentiment.

Findings

Governments and policymakers remained the focus of public discussion on Twitter during the first three months of the pandemic. Overall, public sentiment toward the pandemic remained neutral except for the USA.

Originality/value

This paper proposes a Data Science-based approach to better understand the public topics of concern during the COVID-19 pandemic.

Mining and Validating Social Media Data for COVID-19-Related Human Behaviors Between January and July 2020: Infodemiology Study

Background

Health authorities can minimize the impact of an emergent infectious disease outbreak through effective and timely risk communication, which can build trust and adherence to subsequent behavioral messaging. Monitoring the psychological impacts of an outbreak, as well as public adherence to such messaging, is also important for minimizing long-term effects of an outbreak.

Objective

We used social media data from Twitter to identify human behaviors relevant to COVID-19 transmission, as well as the perceived impacts of COVID-19 on individuals, as a first step toward real-time monitoring of public perceptions to inform public health communications.

Methods

We developed a coding schema for 6 categories and 11 subcategories, which included both a wide number of behaviors as well codes focused on the impacts of the pandemic (eg, economic and mental health impacts). We used this to develop training data and develop supervised learning classifiers for classes with sufficient labels. Classifiers that performed adequately were applied to our remaining corpus, and temporal and geospatial trends were assessed. We compared the classified patterns to ground truth mobility data and actual COVID-19 confirmed cases to assess the signal achieved here.

Results

We applied our labeling schema to approximately 7200 tweets. The worst-performing classifiers had F1 scores of only 0.18 to 0.28 when trying to identify tweets about monitoring symptoms and testing. Classifiers about social distancing, however, were much stronger, with F1 scores of 0.64 to 0.66. We applied the social distancing classifiers to over 228 million tweets. We showed temporal patterns consistent with real-world events, and we showed correlations of up to –0.5 between social distancing signals on Twitter and ground truth mobility throughout the United States.

Conclusions

Behaviors discussed on Twitter are exceptionally varied. Twitter can provide useful information for parameterizing models that incorporate human behavior, as well as for informing public health communication strategies by describing awareness of and compliance with suggested behaviors.

Mental Health Discourses on Twitter during Mental Health Awareness Week

Promoting health-related campaigns on Twitter has increasingly become a world-wide choice to raise awareness and disseminate health information. Data retrieved from Twitter are now being used to explore how users express their views, attitudes and personal experiences of health-related issues. We focused on Twitter discourse reproduced during Mental Health Awareness Week 2017 by examining 1,200 tweets containing the keywords 'mental health', 'mental illness', 'mental disorders' and '#MHAW'. The analysis revealed 'awareness and advocacy', 'stigmatization', and 'personal experience of mental health/illness' as the central discourses within the sample. The article concludes with some recommendations for future research on digitally-mediated health communication.

"Twitter is Really Therapeutic at Times": Examination of Black Men's Twitter Conversations Following Hip-Hop Artist Kid Cudi's Depression Disclosure

Mental illness affects a considerable number of African Americans, and Black men bare a heavy burden. Over the past few years, more and more Black male celebrities have publicly discussed their struggles with mental illness, aiming to raise awareness, educate the public, and reduce stigma around mental health in the Black community. In this exploratory study, I investigated Twitter conversations following hip-hop artist Scott "Kid Cudi" Mescudi's October 2016 depression disclosure. Following the disclosure, the hashtag #YouGoodMan was created to engage Black men on Twitter in conversations about mental health. I used thematic analysis to analyze a sample of 1,482 tweets from the hashtag. Three distinct themes emerged from this study, with implications for mental health communication. The three themes are (a) advocating for mental health disclosure, (b) providing online and offline support, and (c) acknowledging the role and impact of culture and society. The findings are discussed relevant to social representations theory, celebrity influence, and health campaigns.

Monitoring People’s Emotions and Symptoms from Arabic Tweets during the COVID-19 Pandemic

Coronavirus-19 (COVID-19) started from Wuhan, China, in late December 2019. It swept most of the world’s countries with confirmed cases and deaths. The World Health Organization (WHO) declared the virus a pandemic on 11 March 2020 due to its widespread transmission. A public health crisis was declared in specific regions and nation-wide by governments all around the world. Citizens have gone through a wide range of emotions, such as fear of shortage of food, anger at the performance of governments and health authorities in facing the virus, sadness over the deaths of friends or relatives, etc. We present a monitoring system of citizens’ concerns using emotion detection in Twitter data. We also track public emotions and link these emotions with COVID-19 symptoms. We aim to show the effect of emotion monitoring on improving people’s daily health behavior and reduce the spread of negative emotions that affect the mental health of citizens. We collected and annotated 5.5 million tweets in the period from January to August 2020. A hybrid approach combined rule-based and neural network techniques to annotate the collected tweets. The rule-based technique was used to classify 300,000 tweets relying on Arabic emotion and COVID-19 symptom lexicons while the neural network was used to expand the sample tweets that were annotated using the rule-based technique. We used long short-term memory (LSTM) deep learning to classify all of the tweets into six emotion classes and two types (symptom and non-symptom tweets). The monitoring system shows that most of the tweets were posted in March 2020. The anger and fear emotions have the highest number of tweets and user interactions after the joy emotion. The results of user interaction monitoring show that people use likes and replies to interact with non-symptom tweets while they use re-tweets to propagate tweets that mention any of COVID-19 symptoms. Our study should help governments and decision-makers to dispel people’s fears and discover new symptoms associated with the symptoms that were declared by the WHO. It can also help in the understanding of people’s mental and emotional issues to address them before the impact of disease anxiety becomes harmful in itself.

Social Media Use for Health Purposes by Chronic Disease Patients in the United States

Background

Social media can be a cost-effective instant tool for exchanging health information among those with chronic diseases. However, few studies have analyzed the nexus between chronic disease and patients' use of the internet for health-related purposes.

Objective

The objective of this study is to determine if chronic disease patients in the United States use social media platforms to share health information and/or join groups of similar condition.

Materials and Methods

This cross-sectional study conducted a secondary analysis of the Health Information Trends Survey dataset 5 (cycle 1 of 2017 and cycle 2 of 2018) (N = 6650), which is nationally representative of American adults. A series of chi-square tests was carried to examine the association between using social media by chronic disease patients and (a) sharing health information and (b) participating in relevant health groups. Logistic regression analysis was used to determine significant findings.

Results

In terms of sharing health information on social media sites, those who were aged 18-49 years (P < 0.0001) and underweight (P = 0.04) were more likely to share health information on social media, while males were less likely to do so (P < 0.0001). In terms of joining relevant health groups on social media, predictors were being aged 35-49 years (P = 0.008), having a Bachelor's or postbaccalaureate degree (P < 0.02) and having depression or anxiety disorder (P = 0.004); males were less likely to join such groups (P = 0.0004).

Conclusion

Individuals with chronic conditions, except depression or anxiety disorder, were not likely to participate in social media support groups. Future studies should explore how social media can be used to effectively engage those with chronic diseases, which may assist in disease management.

Anxiety and Panic Buying Behaviour during COVID-19 Pandemic-A Qualitative Analysis of Toilet Paper Hoarding Contents on Twitter

Background: The coronavirus disease 2019 (COVID-19) pandemic had increased population-level anxiety and had elicited panic buying behaviour across the world. The over-hoarding of toilet paper has received a lot of negative public attention. In this work, we used Twitter data to qualitatively analyse tweets related to panic buying of toilet paper during the crisis. Methods: A total of 255,171 tweets were collected. Of these 4081 met our inclusion criteria and 100 tweets were randomly selected to develop a coding scheme in the initial phase. Random samples of tweets in folds of 100 were then qualitatively analysed in the focused coding phase until saturation was met at 500 tweets analysed. Results: Five key themes emerged: (1) humour or sarcasm, (2) marketing or profiteering, (3) opinion and emotions, (4) personal experience, and (5) support or information. About half of the tweets carried negative sentiments, expressing anger or frustration towards the deficiency of toilet paper and the frantic situation of toilet paper hoarding, which were among the most influential tweets. Discussion: Panic buying of toilet paper was seen during the 2020 pandemic period with a mass amount of related content spread across social media. The spontaneous contagion of fear and panic through social media could fuel psychological reactions in midst of crises. The high level of negative social media posts regarding the toilet paper crisis acts as an emotional trigger of public anxiety and panic. Conclusions: Social media data can provide rapid infodemiology of public mental health. In a pandemic or crisis situation, real-time data could be monitored and content-analysed for authorities to promptly address public concerns.

Forecasting the future of library and information science and its sub-fields

Forecasting is one of the methods applied in many studies in the library and information science (LIS) field for numerous purposes, from making predictions of the next Nobel laureates to potential technological developments. This study sought to draw a picture for the future of the LIS field and its sub-fields by analysing 97 years of publication and citation patterns. The core Web of Science indexes were used as the data source, and 123,742 articles were examined in-depth for time series analysis. The social network analysis method was used for sub-field classification. The field was divided into four sub-fields: (1) librarianship and law librarianship, (2) health information in LIS, (3) scientometrics and information retrieval and (4) management and information systems. The results of the study show that the LIS sub-fields are completely different from each other in terms of their publication and citation patterns, and all the sub-fields have different dynamics. Furthermore, the number of publications, references and citations will increase significantly in the future. It is expected that more scholars will work together. The future subjects of the LIS field show astonishing diversity from fake news to predatory journals, open government, e-learning and electronic health records. However, the findings prove that publish or perish culture will shape the field. Therefore, it is important to go beyond numbers. It can only be achieved by understanding publication and citation patterns of the field and developing research policies accordingly.

Recommendations From the Twitter Hashtag #DoctorsAreDickheads: Qualitative Analysis

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Machine Learning Classifiers for Twitter Surveillance of Vaping: Comparative Machine Learning Study

BACKGROUND

Twitter presents a valuable and relevant social media platform to study the prevalence of information and sentiment on vaping that may be useful for public health surveillance. Machine learning classifiers that identify vaping-relevant tweets and characterize sentiments in them can underpin a Twitter-based vaping surveillance system. Compared with traditional machine learning classifiers that are reliant on annotations that are expensive to obtain, deep learning classifiers offer the advantage of requiring fewer annotated tweets by leveraging the large numbers of readily available unannotated tweets.

OBJECTIVE

This study aims to derive and evaluate traditional and deep learning classifiers that can identify tweets relevant to vaping, tweets of a commercial nature, and tweets with provape sentiments.

METHODS

We continuously collected tweets that matched vaping-related keywords over 2 months from August 2018 to October 2018. From this data set of tweets, a set of 4000 tweets was selected, and each tweet was manually annotated for relevance (vape relevant or not), commercial nature (commercial or not), and sentiment (provape or not). Using the annotated data, we derived traditional classifiers that included logistic regression, random forest, linear support vector machine, and multinomial naive Bayes. In addition, using the annotated data set and a larger unannotated data set of tweets, we derived deep learning classifiers that included a convolutional neural network (CNN), long short-term memory (LSTM) network, LSTM-CNN network, and bidirectional LSTM (BiLSTM) network. The unannotated tweet data were used to derive word vectors that deep learning classifiers can leverage to improve performance.

RESULTS

LSTM-CNN performed the best with the highest area under the receiver operating characteristic curve (AUC) of 0.96 (95% CI 0.93-0.98) for relevance, all deep learning classifiers including LSTM-CNN performed better than the traditional classifiers with an AUC of 0.99 (95% CI 0.98-0.99) for distinguishing commercial from noncommercial tweets, and BiLSTM performed the best with an AUC of 0.83 (95% CI 0.78-0.89) for provape sentiment. Overall, LSTM-CNN performed the best across all 3 classification tasks.

CONCLUSIONS

We derived and evaluated traditional machine learning and deep learning classifiers to identify vaping-related relevant, commercial, and provape tweets. Overall, deep learning classifiers such as LSTM-CNN had superior performance and had the added advantage of requiring no preprocessing. The performance of these classifiers supports the development of a vaping surveillance system.

Belief in a COVID-19 Conspiracy Theory as a Predictor of Mental Health and Well-Being of Health Care Workers in Ecuador: Cross-Sectional Survey Study

BACKGROUND

During the coronavirus disease (COVID-19) pandemic, social media platforms have become active sites for the dissemination of conspiracy theories that provide alternative explanations of the cause of the pandemic, such as secret plots by powerful and malicious groups. However, the association of individuals' beliefs in conspiracy theories about COVID-19 with mental health and well-being issues has not been investigated. This association creates an assessable channel to identify and provide assistance to people with mental health and well-being issues during the pandemic.

OBJECTIVE

Our aim was to provide the first evidence that belief in conspiracy theories regarding the COVID-19 pandemic is a predictor of the mental health and well-being of health care workers.

METHODS

We conducted a survey of 252 health care workers in Ecuador from April 10 to May 2, 2020. We analyzed the data regarding distress and anxiety caseness with logistic regression and the data regarding life and job satisfaction with linear regression.

RESULTS

Among the 252 sampled health care workers in Ecuador, 61 (24.2%) believed that the virus was developed intentionally in a lab; 82 (32.5%) experienced psychological distress, and 71 (28.2%) had anxiety disorder. Compared to health care workers who were not sure where the virus originated, those who believed the virus was developed intentionally in a lab were more likely to report psychological distress and anxiety disorder and to have lower levels of job satisfaction and life satisfaction.

CONCLUSIONS

This paper identifies belief in COVID-19 conspiracy theories as an important predictor of distress, anxiety, and job and life satisfaction among health care workers. This finding will enable mental health services to better target and provide help to mentally vulnerable health care workers during the ongoing COVID-19 pandemic.

Belief in a COVID-19 Conspiracy Theory as a Predictor of Mental Health and Well-Being of Health Care Workers in Ecuador: Cross-Sectional Survey Study

Background

During the coronavirus disease (COVID-19) pandemic, social media platforms have become active sites for the dissemination of conspiracy theories that provide alternative explanations of the cause of the pandemic, such as secret plots by powerful and malicious groups. However, the association of individuals’ beliefs in conspiracy theories about COVID-19 with mental health and well-being issues has not been investigated. This association creates an assessable channel to identify and provide assistance to people with mental health and well-being issues during the pandemic.

Objective

Our aim was to provide the first evidence that belief in conspiracy theories regarding the COVID-19 pandemic is a predictor of the mental health and well-being of health care workers.

Methods

We conducted a survey of 252 health care workers in Ecuador from April 10 to May 2, 2020. We analyzed the data regarding distress and anxiety caseness with logistic regression and the data regarding life and job satisfaction with linear regression.

Results

Among the 252 sampled health care workers in Ecuador, 61 (24.2%) believed that the virus was developed intentionally in a lab; 82 (32.5%) experienced psychological distress, and 71 (28.2%) had anxiety disorder. Compared to health care workers who were not sure where the virus originated, those who believed the virus was developed intentionally in a lab were more likely to report psychological distress and anxiety disorder and to have lower levels of job satisfaction and life satisfaction.

Conclusions

This paper identifies belief in COVID-19 conspiracy theories as an important predictor of distress, anxiety, and job and life satisfaction among health care workers. This finding will enable mental health services to better target and provide help to mentally vulnerable health care workers during the ongoing COVID-19 pandemic.

A scoping review of the use of Twitter for public health research

Public health practitioners and researchers have used traditional medical databases to study and understand public health for a long time. Recently, social media data, particularly Twitter, has seen some use for public health purposes. Every large technological development in history has had an impact on the behaviour of society. The advent of the internet and social media is no different. Social media creates public streams of communication, and scientists are starting to understand that such data can provide some level of access into the people's opinions and situations. As such, this paper aims to review and synthesize the literature on Twitter applications for public health, highlighting current research and products in practice. A scoping review methodology was employed and four leading health, computer science and cross-disciplinary databases were searched. A total of 755 articles were retreived, 92 of which met the criteria for review. From the reviewed literature, six domains for the application of Twitter to public health were identified: (i) Surveillance; (ii) Event Detection; (iii) Pharmacovigilance; (iv) Forecasting; (v) Disease Tracking; and (vi) Geographic Identification. From our review, we were able to obtain a clear picture of the use of Twitter for public health. We gained insights into interesting observations such as how the popularity of different domains changed with time, the diseases and conditions studied and the different approaches to understanding each disease, which algorithms and techniques were popular with each domain, and more.

A machine learning approach predicts future risk to suicidal ideation from social media data

Machine learning analysis of social media data represents a promising way to capture longitudinal environmental influences contributing to individual risk for suicidal thoughts and behaviors. Our objective was to generate an algorithm termed "Suicide Artificial Intelligence Prediction Heuristic (SAIPH)" capable of predicting future risk to suicidal thought by analyzing publicly available Twitter data. We trained a series of neural networks on Twitter data queried against suicide associated psychological constructs including burden, stress, loneliness, hopelessness, insomnia, depression, and anxiety. Using 512,526 tweets from N = 283 suicidal ideation (SI) cases and 3,518,494 tweets from 2655 controls, we then trained a random forest model using neural network outputs to predict binary SI status. The model predicted N = 830 SI events derived from an independent set of 277 suicidal ideators relative to N = 3159 control events in all non-SI individuals with an AUC of 0.88 (95% CI 0.86-0.90). Using an alternative approach, our model generates temporal prediction of risk such that peak occurrences above an individual specific threshold denote a ~7 fold increased risk for SI within the following 10 days (OR = 6.7 ± 1.1, P = 9 × 10-71). We validated our model using regionally obtained Twitter data and observed significant associations of algorithm SI scores with county-wide suicide death rates across 16 days in August and in October, 2019, most significantly in younger individuals. Algorithmic approaches like SAIPH have the potential to identify individual future SI risk and could be easily adapted as clinical decision tools aiding suicide screening and risk monitoring using available technologies.

A Data-Driven Social Network Intervention for Improving Organ Donation Awareness Among Minorities: Analysis and Optimization of a Cross-Sectional Study

Background

Increasing the number of organ donors may enhance organ transplantation, and past health interventions have shown the potential to generate both large-scale and sustainable changes, particularly among minorities.

Objective

This study aimed to propose a conceptual data-driven framework that tracks digital markers of public organ donation awareness using Twitter and delivers an optimized social network intervention (SNI) to targeted audiences using Facebook.

Methods

We monitored digital markers of organ donation awareness across the United States over a 1-year period using Twitter and examined their association with organ donation registration. We delivered this SNI on Facebook with and without optimized awareness content (ie, educational content with a weblink to an online donor registration website) to low-income Hispanics in Los Angeles over a 1-month period and measured the daily number of impressions (ie, exposure to information) and clicks (ie, engagement) among the target audience.

Results

Digital markers of organ donation awareness on Twitter are associated with donation registration (beta=.0032; P<.001) such that 10 additional organ-related tweets are associated with a 3.20% (33,933/1,060,403) increase in the number of organ donor registrations at the city level. In addition, our SNI on Facebook effectively reached 1 million users, and the use of optimization significantly increased the rate of clicks per impression (beta=.0213; P<.004).

Conclusions

Our framework can provide a real-time characterization of organ donation awareness while effectively delivering tailored interventions to minority communities. It can complement past approaches to create large-scale, sustainable interventions that are capable of raising awareness and effectively mitigate disparities in organ donation.

Syndromic surveillance using web data: a systematic review

During the recent years, a lot of debate is taken place about the evolution of Smart Healthcare systems. Particularly, how these systems can help people improve human conditions of health, by taking advantages of the new Information and Communication Technologies (ICT), regarding early prediction and efficient treatment. The purpose of this study is to provide a systematic review of the current literature available that focuses on information systems on syndromic surveillance using web data. All published items concern articles, books, reviews, reports, conference announcements, and dissertations. We used a variation of PRISMA Statements methodology to conduct a systematic review. The review identifies the relevant published papers from the year 2004 to 2018, systematically includes and explores them to extract similarities, gaps, and conclusions on the research that has been done so far. The results presented concern the year, the examined disease, the web data source, the geographic location/country, and the data analysis method used. The results show that influenza is the most examined infectious disease. The internet tools most used are Twitter and Google. Regarding the geographical areas explored in the published papers, the most examined country is the United States, since many scientists come from this country. There is a significant growth of articles since 2009. There are also various statistical methods used to correlate the data retrieved from the internet to the data from national authorities. The conclusion of all researches is that the Web can be a useful tool for the detection of serious epidemics and for a creation of a syndromic surveillance system using the Web, since we can predict epidemics from web data before they are officially detected in population. With the advance of ICT, Smart Healthcare can benefit from the monitoring of epidemics and the early prediction of such a system, improving national or international health strategies and policy decision. This can be achieved through the provision of new technology tools to enhance health monitoring systems toward the new innovations of Smart Health or eHealth, even with the emerging technologies of Internet of Things. The challenges and impacts of an electronic system based on internet data include the social, medical, and technological disciplines. These can be further extended to Smart Healthcare, as the data streaming can provide with real-time information, awareness on epidemics and alerts for both patients or medical scientists. Finally, these new systems can help improve the standards of human life.

Identifying Sleep-Deprived Authors of Tweets: Prospective Study

BACKGROUND

Social media data can be explored as a tool to detect sleep deprivation. First-year undergraduate students in their first quarter were invited to wear sleep-tracking devices (Basis; Intel), allow us to follow them on Twitter, and complete weekly surveys regarding their sleep.

OBJECTIVE

This study aimed to determine whether social media data can be used to monitor sleep deprivation.

METHODS

The sleep data obtained from the device were utilized to create a tiredness model that aided in labeling the tweets as sleep deprived or not at the time of posting. Labeled data were used to train and test a gated recurrent unit (GRU) neural network as to whether or not study participants were sleep deprived at the time of posting.

RESULTS

Results from the GRU neural network suggest that it is possible to classify the sleep-deprivation status of a tweet's author with an average area under the curve of 0.68.

CONCLUSIONS

It is feasible to use social media to identify students' sleep deprivation. The results add to the body of research suggesting that social media data should be further explored as a potential source for monitoring health.

A computational study of mental health awareness campaigns on social media

As public discourse continues to progress online, it is important for mental health advocates, public health officials, and other curious parties and stakeholders, ranging from researchers, to those affected by the issue, to be aware of the advancing new mediums in which the public can share content ranging from useful resources and self-help tips to personal struggles with respect to both illness and its stigmatization. A better understanding of this new public discourse on mental health, often framed as social media campaigns, can help perpetuate the allocation of sparse mental health resources, the need for educational awareness, and the usefulness of community, with an opportunity to reach those seeking help at the right moment. The objective of this study was to understand the nature of and engagement around mental health content shared on mental health campaigns, specifically #MyTipsForMentalHealth on Twitter around World Mental Health Awareness Day in 2017. We collected 14,217 Twitter posts from 10,805 unique users between September and October 2017 that contained the hashtag #MyTipsForMentalHealth. With the involvement of domain experts, we hand-labeled 700 posts and categorized them as (a) Fact, (b) Stigmatizing, (c) Inspirational, (d) Medical/Clinical Tip, (e) Resource Related, (f) Lifestyle or Social Tip or Personal View, and (g) Off Topic. After creating a "seed" machine learning classifier, we used both unsupervised and semi supervised methods to classify posts into the various expert identified topical categories. We also performed a content analysis to understand how information on different topics spread through social networks. Our support vector machine classification algorithm achieved a mean cross-validation accuracy of 0.81 and accuracy of 0.64 on unseen data. We found that inspirational Twitter posts were the most spread with a mean of 4.17 retweets, and stigmatizing content was second with a mean of 3.66 retweets. Classification of social media-related mental health interactions offers valuable insights on public sentiment as well as a window into the evolving world of online self-help and the varied resources within. Our results suggest an important role for social media-based peer support to not only guide information seekers to useful content and local resources but also illuminate the socially-insular aspects of stigmatization. However, our results also reflect the challenges of quantifying the heterogeneity of mental health content on social media and the need for novel machine learning methods customized to the challenges of the field.

Crime and social media

Purpose

The purpose of this paper is to complement the scant macroeconomic literature on the development outcomes of social media by examining the relationship between Facebook penetration and violent crime levels in a cross-section of 148 countries for the year 2012.

Design/methodology/approach

The empirical evidence is based on ordinary least squares (OLS), Tobit and quantile regressions. In order to respond to policy concerns on the limited evidence on the consequences of social media in developing countries, the data set is disaggregated into regions and income levels. The decomposition by income levels included: low income, lower middle income, upper middle income and high income. The corresponding regions include: Europe and Central Asia, East Asia and the Pacific, Middle East and North Africa (MENA), Sub-Saharan Africa and Latin America.

Findings

From OLS and Tobit regressions, there is a negative relationship between Facebook penetration and crime. However, quantile regressions reveal that the established negative relationship is noticeable exclusively in the 90th crime quantile. Further, when the data set is decomposed into regions and income levels, the negative relationship is evident in the MENA while a positive relationship is confirmed for Sub-Saharan Africa. Policy implications are discussed.

Originality/value

Studies on the development outcomes of social media are sparse because of a lack of reliable macroeconomic data on social media. This study primarily complemented three existing studies that have leveraged on a newly available data set on Facebook.

Social Media Skills for Professional Development in Psychiatry and Medicine

Social media use is increasing in the United States. Because psychiatrists and physicians are becoming more active online, Twitter is emerging as a leading platform for professional development. Social media can enhance networking, serve as a tool for mentoring trainees and colleagues, introduce journal articles to new readers, and allow psychiatrists and physicians to advocate for health care issues. Psychiatrists and physicians should observe ethical standards for digital citizenship on Twitter and other social media platforms.

#Teeth&Tweets: the reach and reaction of an online social media oral health promotion campaign

Aim The aim of the study was to investigate: i) the geographical reach and reaction of the online participants engaging in an oral health campaign 'National Smile Month' UK 2016 (NSM); and ii) whether dental practices during NSM were using Twitter to help address regional oral health inequalities.Methods Twitter posts, that is 'tweets', were collected using the application programming interface (API) software Mozdeh, for one month. Tweets were classified into high, medium or low engagement. Participants' postcode data of the organisation/practice were obtained via an internet search using Google. The geolocation of tweets was then linked by organisations' postcode to the 2015 Index of Multiple Deprivation and the oral health survey of five-year-olds 2014/15, and subsequently mapped using Google Fusion Tables.Results A total of 23,100 tweets were captured with a final total of 2,968 usable tweets from 763 separate accounts. Two hundred and twelve tweets were from dental practices, with 107 classified as low engagement, 99 medium, and 45 high engagement (39 of those tweets were from organisations allied to oral health). Interactive maps were created to give a visual representation of the relationship between those participants producing 'high' impact tweets and the level of dental decay in five-year-olds and deprivation levels.Conclusion The majority of tweets did not promote any specific preventative behaviour. Dental practices in England were not contributing to National Smile Month via Twitter in a way that would improve regional oral health inequalities. In areas of high-need there is evidence of proactive engagement with NSM via Twitter by local authorities and their healthcare partners.

Temporal and Geographic Patterns of Social Media Posts About an Emerging Suicide Game

PURPOSE

Rates of suicide are increasing rapidly among youth. Social media messages and online games promoting suicide are a concern for parents and clinicians. We examined the timing and location of social media posts about one alleged youth suicide game to better understand the degree to which social media data can provide earlier public health awareness.

METHODS

We conducted a search of all public social media posts and news articles on the Blue Whale Challenge (BWC), an alleged suicide game, from January 1, 2013, through June 30, 2017. Data were retrieved through multiple keyword search; sources included social media platforms Twitter, YouTube, Reddit, Tumblr, as well as blogs, forums, and news articles. Posts were classified into three categories: individual "pro"-BWC posts (support for game), individual "anti"-BWC posts (opposition to game), and media reports. Timing and location of posts were assessed.

RESULTS

Overall, 95,555 social media posts and articles about the BWC were collected. In total, over one-quarter (28.3%) were "pro"-BWC. The first U.S. news article related to the BWC was published approximately 4 months after the first English language U.S. social media post about the BWC and 9 months after the first U.S. social media post in any language. By the close of the study period, "pro"-BWC posts had spread to 127 countries.

CONCLUSIONS

Novel online risks to mental health, such as prosuicide games or messages, can spread rapidly and globally. Better understanding social media and Web data may allow for detection of such threats earlier than is currently possible.

Evaluation of the Safety and Design of Community Internet Resources for Veteran Suicide Prevention

Background: Recent data show many veterans who die by suicide are not currently engaged in mental health care. Veterans frequently use the Internet for health information and may look online for community resources when in distress. However, little is known about their design characteristics. Aim: To evaluate the design and content of community, veteran suicide prevention websites. Method: Community websites focused on veteran suicide prevention were gathered through Internet searches using standardized search terms. Websites that met the inclusion criteria (n = 9) were evaluated for adherence to suicide safe messaging, usability, readability, and credibility heuristics. Interrater reliability was evaluated using kappa statistics. Descriptive statistics were used to describe website features. Results: Community websites tended to provide help-seeking information, safe messaging, and community activities. However, no websites provided information on lethal means safety or references to signal credibility. Limitations: The sample was small and only included English-language websites, and focused on veteran-oriented, community websites. Conclusion: Community suicide prevention websites focused on veterans could be improved through increased readability, credibility, and provision of lethal means safety information.