What Are People Tweeting About Zika? An Exploratory Study Concerning Its Symptoms, Treatment, Transmission, and Prevention

Patient Experiences and Insights on Chronic Ocular Pain: Social Media Listening Study

BACKGROUND

Ocular pain has multifactorial etiologies that affect activities of daily life, psychological well-being, and health-related quality of life (QoL). Chronic ocular surface pain (COSP) is a persistent eye pain symptom lasting for a period longer than 3 months.

OBJECTIVE

The objective of this social media listening study was to better understand COSP and related symptoms and identify its perceived causes, comorbidities, and impact on QoL from social media posts.

METHODS

A search from February 2020 to February 2021 was performed on social media platforms (Twitter, Facebook, blogs, and forums) for English-language content posted on the web. Social media platforms that did not provide public access to information or posts were excluded. Social media posts from Australia, Canada, the United Kingdom, and the United States were retrieved using the Social Studio platform-a web-based aggregator tool.

RESULTS

Of the 25,590 posts identified initially, 464 posts about COSP were considered relevant; the majority of conversations (98.3%, n=456) were posted by adults (aged >18 years). Work status was mentioned in 52 conversations. Patients' or caregivers' discussions across social media platforms were centered around the symptoms (61.9%, n=287) and causes (58%, n=269) of ocular pain. Patients mentioned having symptoms associated with COSP, including headache or head pressure, dry or gritty eyes, light sensitivity, etc. Patients posted that their COSP impacts day-to-day activities such as reading, driving, sleeping, and their social, mental, and functional well-being.

CONCLUSIONS

Insights from this study reported patients' experiences, concerns, and the adverse impact on overall QoL. COSP imposes a significant burden on patients, which spans multiple aspects of daily life.

Multiple-Perspective Data-Driven Analysis of Online Health Communities

The growth of online health communities and socially generated health-related content has the potential to provide considerable value for patients and healthcare providers alike. For example, members of the public can acquire medical knowledge and interact with others online. However, the volume of information-and the consequent 'noise' associated with large data volumes-can create difficulties for users. In this paper, we present a data-driven approach to better understand these data from multiple stakeholder perspectives. We utilise three techniques-sentiment analysis, content analysis, and topic analysis-to analyse user-generated medical content related to Lyme disease. We use a supervised feature-based model to identify sentiments, content analysis to identify concepts that predominate, and latent Dirichlet allocation strategy as an unsupervised generative model to identify topics represented in the discourse. We validate that applying three different analytic methods highlights differing aspects of the information different stakeholders will be interested in based on the goals of different stakeholders, expert opinion, and comparison with patient information leaflets.

Content and sentiment surveillance (CSI): A critical component for modeling modern epidemics

Comprehensive surveillance systems are the key to provide accurate data for effective modeling. Traditional symptom-based case surveillance has been joined with recent genomic, serologic, and environment surveillance to provide more integrated disease surveillance systems. A major gap in comprehensive disease surveillance is to accurately monitor potential population behavioral changes in real-time. Population-wide behaviors such as compliance with various interventions and vaccination acceptance significantly influence and drive the overall epidemic dynamics in the society. Original infoveillance utilizes online query data (e.g., Google and Wikipedia search of a specific content topic such as an epidemic) and later focuses on large volumes of online discourse data about the from social media platforms and further augments epidemic modeling. It mainly uses number of posts to approximate public awareness of the disease, and further compares with observed epidemic dynamics for better projection. The current COVID-19 pandemic shows that there is an urgency to further harness the rich, detailed content and sentiment information, which can provide more accurate and granular information on public awareness and perceptions toward multiple aspects of the disease, especially various interventions. In this perspective paper, we describe a novel conceptual analytical framework of content and sentiment infoveillance (CSI) and integration with epidemic modeling. This CSI framework includes data retrieval and pre-processing; information extraction via natural language processing to identify and quantify detailed time, location, content, and sentiment information; and integrating infoveillance with common epidemic modeling techniques of both mechanistic and data-driven methods. CSI complements and significantly enhances current epidemic models for more informed decision by integrating behavioral aspects from detailed, instantaneous infoveillance from massive social media data.

Entomological Surveillance of the Invasive Aedes Species at Higher-Priority Entry Points in Northern Iran: Exploratory Report on a Field Study

Background

Arboviral diseases such as dengue, Zika, and chikungunya are transmitted by Aedes aegypti and Ae albopictus and are emerging global public health concerns.

Objective

This study aimed to provide up-to-date data on the occurrence of the invasive Aedes species in a given area as this is essential for planning and implementing timely control strategies.

Methods

Entomological surveillance was planned and carried out monthly from May 2018 to December 2019 at higher-priority entry points in Guilan Province, Northern Iran, using ovitraps, larval collection, and human-baited traps. Species richness (R), Simpson (D), evenness (E), and Shannon-Wiener indexes (H̕) were measured to better understand the diversity of the Aedes species. The Spearman correlation coefficient and regression models were used for data analysis.

Results

We collected a total of 3964 mosquito samples including 17.20% (682/3964) belonging to the Aedes species, from 3 genera and 13 species, and morphologically identified them from May 2018 to December 2019. Ae vexans and Ae geniculatus, which showed a peak in activity levels and population in October (226/564, 40.07% and 26/103, 25.2%), were the eudominant species (D=75.7%; D=21.2%) with constant (C=100) and frequent (C=66.7%) distributions, respectively. The population of Ae vexans had a significant positive correlation with precipitation (r=0.521; P=.009) and relative humidity (r=0.510; P=.01), whereas it was inversely associated with temperature (r=−0.432; P=.04). The Shannon-Wiener Index was up to 0.84 and 1.04 in the city of Rasht and in July, respectively. The rarefaction curve showed sufficiency in sampling efforts by reaching the asymptotic line at all spatial and temporal scales, except in Rasht and in October.

Conclusions

Although no specimens of the Ae aegypti and Ae albopictus species were collected, this surveillance provides a better understanding of the native Aedes species in the northern regions of Iran. These data will assist the health system in future arbovirus research, and in the implementation of effective vector control and prevention strategies, should Ae aegypti and Ae albopictus be found in the province.

Smartphone clip-on instrument and microfluidic processor for rapid sample-to-answer detection of Zika virus in whole blood using spatial RT-LAMP

Rapid, simple, inexpensive, accurate, and sensitive point-of-care (POC) detection of viral pathogens in bodily fluids is a vital component of controlling the spread of infectious diseases. The predominant laboratory-based methods for sample processing and nucleic acid detection face limitations that prevent them from gaining wide adoption for POC applications in low-resource settings and self-testing scenarios. Here, we report the design and characterization of an integrated system for rapid sample-to-answer detection of a viral pathogen in a droplet of whole blood comprised of a 2-stage microfluidic cartridge for sample processing and nucleic acid amplification, and a clip-on detection instrument that interfaces with the image sensor of a smartphone. The cartridge is designed to release viral RNA from Zika virus in whole blood using chemical lysis, followed by mixing with the assay buffer for performing reverse-transcriptase loop-mediated isothermal amplification (RT-LAMP) reactions in six parallel microfluidic compartments. The battery-powered handheld detection instrument uniformly heats the compartments from below, and an array of LEDs illuminates from above, while the generation of fluorescent reporters in the compartments is kinetically monitored by collecting a series of smartphone images. We characterize the assay time and detection limits for detecting Zika RNA and gamma ray-deactivated Zika virus spiked into buffer and whole blood and compare the performance of the same assay when conducted in conventional PCR tubes. Our approach for kinetic monitoring of the fluorescence-generating process in the microfluidic compartments enables spatial analysis of early fluorescent "bloom" events for positive samples, in an approach called "Spatial LAMP" (S-LAMP). We show that S-LAMP image analysis reduces the time required to designate an assay as a positive test, compared to conventional analysis of the average fluorescent intensity of the entire compartment. S-LAMP enables the RT-LAMP process to be as short as 22 minutes, resulting in a total sample-to-answer time in the range of 17-32 minutes to distinguish positive from negative samples, while demonstrating a viral RNA detection as low as 2.70 × 102 copies per μl, and a gamma-irradiated virus of 103 virus particles in a single 12.5 μl droplet blood sample.

Seeing Should Probably not be Believing: The Role of Deceptive Support in COVID-19 Misinformation on Twitter

With the spread of the SARS-CoV-2, enormous amounts of information about the pandemic are disseminated through social media platforms such as Twitter. Social media posts often leverage the trust readers have in prestigious news agencies and cite news articles as a way of gaining credibility. Nevertheless, it is not always the case that the cited article supports the claim made in the social media post. We present a cross-genre ad hoc pipeline to identify whether the information in a Twitter post (i.e., a “Tweet”) is indeed supported by the cited news article. Our approach is empirically based on a corpus of over 46.86 million Tweets and is divided into two tasks: (i) development of models to detect Tweets containing claim and worth to be fact-checked and (ii) verifying whether the claims made in a Tweet are supported by the newswire article it cites. Unlike previous studies that detect unsubstantiated information by post hoc analysis of the patterns of propagation, we seek to identify reliable support (or the lack of it) before the misinformation begins to spread. We discover that nearly half of the Tweets (43.4%) are not factual and hence not worth checking – a significant filter, given the sheer volume of social media posts on a platform such as Twitter. Moreover, we find that among the Tweets that contain a seemingly factual claim while citing a news article as supporting evidence, at least 1% are not actually supported by the cited news, and are hence misleading.

The double-edged sword of social media usage during the COVID-19 pandemic: demographical and cultural analyses

Purpose

This exploratory research aims to (1) investigate the bright and dark sides of social media use during the COVID-19 pandemic; (2) explore the impact of demographic factors on social media usage; and (3) assess the effects of cultural dimensions on social media usage.

Design/methodology/approach

The data are collected through an online survey. Factors derived from grounded theories and models such as affordance theory and Hofstede's cultural framework were considered. Spearman correlation and nonparametric analysis were used to test the hypotheses.

Findings

The results revealed that social media usage was positively associated with healing and affiliation, and negatively associated with self-control. There are also positive associations between social media usage and sharing information related to COVID-19 without verification, perceived reliability of COVID-19 information on social media and relapse. The impact of demographic and cultural factors indicated significant effects of gender, age, marital status, educational level, power distance and collectivism on social media usage, sharing information, perceived information reliability, healing and affiliation.

Originality/value

This study contributes to technology affordances by examining social media's positive and negative affordances in a new context (COVID-19 pandemic). From the positive side, this study explores the use of social media for healing and affiliation. As for the negative impact of social media during the pandemic, this study assesses the user's addiction to social media use (relapse) and perception of the social media information reliability and information sharing without verification. It is among few research endeavors conducted in a non-Western country. This study also examines the influence of demographic and cultural factors on social media users. The results provide insights for both researchers and policymakers regarding social media usage.

Network Dynamics of COVID-19 Fake and True News Diffusion Networks

Social media platforms have become an integral source to spread and consume information. Twitter has emerged as the fastest medium to disseminate any information. This blind trust on social media has raised the concern to quantify the truth or fakeness of what we are consuming. During COVID-19, the usage of social platforms has dramatically increased in everyone’s life. It is high time to distinguish between the type of users involved in spreading fake and true news content. Our study aims to answer two questions. First, what is the complex network structure of users involved in spreading any news? How two types (i.e. Fake and True) of networks are different in terms of network topology. Second, what is the role of influential users in spreading both types of news? To answer these, the fake and true news of COVID-19 are collected which have been classified by fact-checking websites. Diffusion networks have been created to perform the experiments. Network topological analysis revealed that despite having differences, most properties show similar behaviour. Though, it can be stated that during COVID-19, behaviour of users remained the same in spreading fake or true content. Resilience analysis discovered that fake networks were more densely connected than true ones. There were more centric nodes or influential users were present in Fake news networks than True news networks.

SceneFND: Multimodal fake news detection by modelling scene context information

Fake news is a threat for the society and can create a lot of confusion to people regarding what is true and what not. Fake news usually contain manipulated content, such as text or images that attract the interest of the readers with the aim to convince them on their truthfulness. In this article, we propose SceneFND (Scene Fake News Detection), a system that combines textual, contextual scene and visual representation to address the problem of multimodal fake news detection. The textual representation is based on word embeddings that are passed into a bidirectional long short-term memory network. Both the contextual scene and the visual representations are based on the images contained in the news post. The place, weather and season scenes are extracted from the image. Our statistical analysis on the scenes showed that there are statistically significant differences regarding their frequency in fake and real news. In addition, our experimental results on two real world datasets show that the integration of the contextual scenes is effective for fake news detection. In particular, SceneFND improved the performance of the textual baseline by 3.48% in PolitiFact and by 3.32% in GossipCop datasets. Finally, we show the suitability of the scene information for the task and present some examples to explain its effectiveness in capturing the relevance between images and text.

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.

An evaluation of the psychological impact of COVID-19 and the precautionary measure of social isolation on adults in the Asir region, Saudi Arabia

BACKGROUND

The COVID-19 outbreak is worrying for people and society. The aim of this study is to evaluate the psychological impact of the COVID-19 pandemic and the precautionary measure of social isolation on adults in the Asir region of Saudi Arabia.

METHODS

A descriptive cross-sectional survey design was carried out in the Asir region for a period of 5 months from May 2020 to September 2020 to assess the psychological response of the adult population during the COVID-19 pandemic using an anonymous online questionnaire. The questionnaire was adapted from previous research and involved three sections, namely sociodemographic data, Patient Health Questionnaire-9 (PHQ-9) and the Generalised Anxiety Disorder Scale (GAD-7 Scale). A total score of ≥10 indicates depression and anxiety. Data were analysed using SPSS V.25.

RESULTS

Females had higher rates of COVID-19 depression than males. There was a significant correlation between age and home setting and anxiety, and a significant association between marital status and the level of education and depression. There was a significant association between gender and depression and anxiety, while there was no significant association between occupation and income, and depression and anxiety.

CONCLUSION

The findings of the study clearly show that depression and anxiety are highly prevalent among adults. Females had higher rates of COVID-19 depression and anxiety than males. The findings from this study show that implementing a strategy for the prevention and management of depression and anxiety is highly recommended to minimise the impact of these disorders.

Topic modelling and opinion mining of user generated content on the internet using machine learning: An analysis of postpartum care centres in Shanghai

In order to reach a compromise between adhering to the traditional culture and embracing the modern lifestyle, more and more Asian moms are heading towards postpartum care centres for postpartum recovery. However, research regarding the quality of care of these postpartum care centres is nearly missing from the literature. This paper investigated the status quo of the postpartum care centres in Shanghai, China from mothers’ perspectives by means of analysing the 34280 pairs of ratings and reviews posted by postpartum care centre customers on the internet with machine learning and text mining. Results show that the mothers are generally satisfied with the studied care centres. Meanwhile, the 13 major topics in the customer online reviews were identified, which provide an overview of the interaction between a mother and a care centre. In addition, weight of topic analysis suggests that the studied care centres can further improve in the areas of support team, environment, and facility.

Public Discussion of Anthrax on Twitter: Using Machine Learning to Identify Relevant Topics and Events

Background

Social media allows researchers to study opinions and reactions to events in real time. One area needing more study is anthrax-related events. A computational framework that utilizes machine learning techniques was created to collect tweets discussing anthrax, further categorize them as relevant by the month of data collection, and detect discussions on anthrax-related events.

Objective

The objective of this study was to detect discussions on anthrax-related events and to determine the relevance of the tweets and topics of discussion over 12 months of data collection.

Methods

This is an infoveillance study, using tweets in English containing the keyword “Anthrax” and “Bacillus anthracis”, collected from September 25, 2017, through August 15, 2018. Machine learning techniques were used to determine what people were tweeting about anthrax. Data over time was plotted to determine whether an event was detected (a 3-fold spike in tweets). A machine learning classifier was created to categorize tweets by relevance to anthrax. Relevant tweets by month were examined using a topic modeling approach to determine the topics of discussion over time and how these events influence that discussion.

Results

Over the 12 months of data collection, a total of 204,008 tweets were collected. Logistic regression analysis revealed the best performance for relevance (precision=0.81; recall=0.81; F₁-score=0.80). In total, 26 topics were associated with anthrax-related events, tweets that were highly retweeted, natural outbreaks, and news stories.

Conclusions

This study shows that tweets related to anthrax can be collected and analyzed over time to determine what people are discussing and to detect key anthrax-related events. Future studies are required to focus only on opinion tweets, use the methodology to study other terrorism events, or to monitor for terrorism threats.

Categorising patient concerns using natural language processing techniques

OBJECTIVES

Patient feedback is critical to identify and resolve patient safety and experience issues in healthcare systems. However, large volumes of unstructured text data can pose problems for manual (human) analysis. This study reports the results of using a semiautomated, computational topic-modelling approach to analyse a corpus of patient feedback.

METHODS

Patient concerns were received by Alberta Health Services between 2011 and 2018 (n=76 163), regarding 806 care facilities in 163 municipalities, including hospitals, clinics, community care centres and retirement homes, in a province of 4.4 million. Their existing framework requires manual labelling of pre-defined categories. We applied an automated latent Dirichlet allocation (LDA)-based topic modelling algorithm to identify the topics present in these concerns, and thereby produce a framework-free categorisation.

RESULTS

The LDA model produced 40 topics which, following manual interpretation by researchers, were reduced to 28 coherent topics. The most frequent topics identified were communication issues causing delays (frequency: 10.58%), community care for elderly patients (8.82%), interactions with nurses (8.80%) and emergency department care (7.52%). Many patient concerns were categorised into multiple topics. Some were more specific versions of categories from the existing framework (eg, communication issues causing delays), while others were novel (eg, smoking in inappropriate settings).

DISCUSSION

LDA-generated topics were more nuanced than the manually labelled categories. For example, LDA found that concerns with community care were related to concerns about nursing for seniors, providing opportunities for insight and action.

CONCLUSION

Our findings outline the range of concerns patients share in a large health system and demonstrate the usefulness of using LDA to identify categories of patient concerns.

Understanding misinformation infodemic during public health emergencies due to large-scale disease outbreaks: a rapid review

AIM

The coronavirus disease 2019 (COVID-19) has caused hundreds of thousands of deaths, impacted the flow of life and resulted in an immeasurable amount of socio-economic damage. However, not all of this damage is attributable to the disease itself; much of it has occurred due to the prevailing misinformation around COVID-19. This rapid integrative review will draw on knowledge from the literature about misinformation during previous abrupt large-scale infectious disease outbreaks to enable policymakers, governments and health institutions to proactively mitigate the spread and effect of misinformation.

SUBJECT AND METHODS

For this rapid integrative review, we systematically searched MEDLINE and Google Scholar and extracted the literature on misinformation during abrupt large-scale infectious disease outbreaks since 2000. We screened articles using predetermined inclusion criteria. We followed an updated methodology for integrated reviews and adjusted it for our rapid review approach.

RESULTS

We found widespread misinformation in all aspects of large-scale infectious disease outbreaks since 2000, including prevention, treatment, risk factor, transmission mode, complications and vaccines. Conspiracy theories also prevailed, particularly involving vaccines. Misinformation most frequently has been reported regarding Ebola, and women and youth are particularly vulnerable to misinformation. A lack of scientific knowledge by individuals and a lack of trust in the government increased the consumption of misinformation, which is disseminated quickly by the unregulated media, particularly social media.

CONCLUSION

This review identified the nature and pattern of misinformation during large-scale infectious disease outbreaks, which could potentially be used to address misinformation during the ongoing COVID-19 or any future pandemic.

Mining topic and sentiment dynamics in physician rating websites during the early wave of the COVID-19 pandemic: Machine learning approach

INTRODUCTION

An increasing number of patients are voicing their opinions and expectations about the quality of care in online forums and on physician rating websites (PRWs). This paper analyzes patient online reviews (PORs) to identify emerging and fading topics and sentiment trends in PRWs during the early stage of the COVID-19 outbreak.

METHODS

Text data were collected, including 55,612 PORs of 3430 doctors from three popular PRWs in the United States (RateMDs, HealthGrades, and Vitals) from March 01 to June 27, 2020. An improved latent Dirichlet allocation (LDA)-based topic modeling (topic coherence-based LDA [TCLDA]), manual annotation, and sentiment analysis tool were applied to extract a suitable number of topics, generate corresponding keywords, assign topic names, and determine trends in the extracted topics and specific emotions.

RESULTS

According to the coherence value and manual annotation, the identified taxonomy includes 30 topics across high-rank and low-rank disease categories. The emerging topics in PRWs focus mainly on themes such as treatment experience, policy implementation regarding epidemic control measures, individuals' attitudes toward the pandemic, and mental health across high-rank diseases. In contrast, the treatment process and experience during COVID-19, awareness and COVID-19 control measures, and COVID-19 deaths, fear, and stress were the most popular themes for low-rank diseases. Panic buying and daily life impact, treatment processes, and bedside manner were the fading themes across high-rank diseases. In contrast, provider attitude toward patients during the pandemic, detection at public transportation, passenger, travel bans and warnings, and materials supplies and society support during COVID-19 were the most fading themes across low-rank diseases. Regarding sentiment analysis, negative emotions (fear, anger, and sadness) prevail during the early wave of the COVID-19.

CONCLUSION

Mining topic dynamics and sentiment trends in PRWs may provide valuable knowledge of patients' opinions during the COVID-19 crisis. Policymakers should consider these PORs and develop global healthcare policies and surveillance systems through monitoring PRWs. The findings of this study identify research gaps in the areas of e-health and text mining and offer future research directions.

Analyzing Restaurant Customers’ Evolution of Dining Patterns and Satisfaction during COVID-19 for Sustainable Business Insights

Observing and interpreting restaurant customers’ evolution of dining patterns and satisfaction during COVID-19 is of critical importance in terms of developing sustainable business insights. This study describes and analyzes customers’ dining behavior before and after the pandemic outbreak by means of statistically aggregating and empirically correlating 651,703 restaurant-user-generated contents posted by diners during 2019–2020. Twenty review topics, mostly food, were identified by latent Dirichlet allocation, whereas analysis of variation and rating-review regression were performed to explore whether and why customers became less satisfied. Results suggest that customers have been paying fewer visits to restaurants since the outbreak, assigning lower ratings, and showing limited evidence of spending more. Interestingly, queuing, the most annoying factor for restaurant customers during normal periods, turns out to receive much less complaint during COVID-19. This study contributes by discovering business knowledge in the context of COVID-19 based on big data that features accessibility, relevance, volume, and information richness, which is transferable to future studies and can benefit additional population and business. Meanwhile, this study also provides practical suggestions to managers regarding the framework of self-evaluation, business mode, and operational optimization.

Detecting Topic and Sentiment Trends in Physician Rating Websites: Analysis of Online Reviews Using 3-Wave Datasets

(1) Background: Physician rating websites (PRWs) are a rich resource of information where individuals learn other people response to various health problems. The current study aims to investigate and analyze the people top concerns and sentiment dynamics expressed in physician online reviews (PORs). (2) Methods: Text data were collected from four U.S.-based PRWs during the three time periods of 2018, 2019 and 2020. Based on the dynamic topic modeling, hot topics related to different aspects of healthcare were identified. Following the hybrid approach of aspect-based sentiment analysis, the social network of prevailing topics was also analyzed whether people expressed positive, neutral or negative sentiments in PORs. (3) Results: The study identified 30 dominant topics across three different stages which lead toward four key findings. First, topics discussed in Stage III were quite different from the earlier two stages due to the COVID-19 outbreak. Second, based on the keyword co-occurrence analysis, the most prevalent keywords in all three stages were related to the treatment, questions asked by patients, communication problem, patients' feelings toward the hospital environment, disease symptoms, time spend with patients and different issues related to the COVID-19 (i.e., pneumonia, death, spread and cases). Third, topics related to the provider service quality, hospital servicescape and treatment cost were the most dominant topics in Stages I and II, while the quality of online information regarding COVID-19 and government countermeasures were the most dominant topics in Stage III. Fourth, when zooming into the topic-based sentiments analysis, hot topics in Stage I were mostly positive (joy be the dominant emotion), then negative (disgust be the dominant emotion) in Stage II. Furthermore, sentiments in the initial period of Stage III (COVID-19) were negative (anger be the dominant emotion), then transformed into positive (trust be the dominant emotion) later. The findings also revealed that the proposed method outperformed the conventional machine learning models in analyzing topic and sentiment dynamics expressed in PRWs. (4) Conclusions: Methodologically, this research demonstrates the ability and importance of computational techniques for analyzing large corpora of text and complementing conventional social science approaches.

Determinants of Infodemics During Disease Outbreaks: A Systematic Review

Background: The widespread use of social media represents an unprecedented opportunity for health promotion. We have more information and evidence-based health related knowledge, for instance about healthy habits or possible risk behaviors. However, these tools also carry some disadvantages since they also open the door to new social and health risks, in particular during health emergencies. This systematic review aims to study the determinants of infodemics during disease outbreaks, drawing on both quantitative and qualitative methods. Methods: We searched research articles in PubMed, Scopus, Medline, Embase, CINAHL, Sociological abstracts, Cochrane Library, and Web of Science. Additional research works were included by searching bibliographies of electronically retrieved review articles. Results: Finally, 42 studies were included in the review. Five determinants of infodemics were identified: (1) information sources; (2) online communities' structure and consensus; (3) communication channels (i.e., mass media, social media, forums, and websites); (4) messages content (i.e., quality of information, sensationalism, etc.,); and (5) context (e.g., social consensus, health emergencies, public opinion, etc.). Studied selected in this systematic review identified different measures to combat misinformation during outbreaks. Conclusion: The clarity of the health promotion messages has been proven essential to prevent the spread of a particular disease and to avoid potential risks, but it is also fundamental to understand the network structure of social media platforms and the emergency context where misinformation might dynamically evolve. Therefore, in order to prevent future infodemics, special attention will need to be paid both to increase the visibility of evidence-based knowledge generated by health organizations and academia, and to detect the possible sources of mis/disinformation.

COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication

BACKGROUND

COVID-19, caused by SARS-CoV-2, has led to a global pandemic. The World Health Organization has also declared an infodemic (ie, a plethora of information regarding COVID-19 containing both false and accurate information circulated on the internet). Hence, it has become critical to test the veracity of information shared online and analyze the evolution of discussed topics among citizens related to the pandemic.

OBJECTIVE

This research analyzes the public discourse on COVID-19. It characterizes risk communication patterns in four Asian countries with outbreaks at varying degrees of severity: South Korea, Iran, Vietnam, and India.

METHODS

We collected tweets on COVID-19 from four Asian countries in the early phase of the disease outbreak from January to March 2020. The data set was collected by relevant keywords in each language, as suggested by locals. We present a method to automatically extract a time-topic cohesive relationship in an unsupervised fashion based on natural language processing. The extracted topics were evaluated qualitatively based on their semantic meanings.

RESULTS

This research found that each government's official phases of the epidemic were not well aligned with the degree of public attention represented by the daily tweet counts. Inspired by the issue-attention cycle theory, the presented natural language processing model can identify meaningful transition phases in the discussed topics among citizens. The analysis revealed an inverse relationship between the tweet count and topic diversity.

CONCLUSIONS

This paper compares similarities and differences of pandemic-related social media discourse in Asian countries. We observed multiple prominent peaks in the daily tweet counts across all countries, indicating multiple issue-attention cycles. Our analysis identified which topics the public concentrated on; some of these topics were related to misinformation and hate speech. These findings and the ability to quickly identify key topics can empower global efforts to fight against an infodemic during a pandemic.

An exploratory study of COVID-19 misinformation on Twitter

During the COVID-19 pandemic, social media has become a home ground for misinformation. To tackle this infodemic, scientific oversight, as well as a better understanding by practitioners in crisis management, is needed. We have conducted an exploratory study into the propagation, authors and content of misinformation on Twitter around the topic of COVID-19 in order to gain early insights. We have collected all tweets mentioned in the verdicts of fact-checked claims related to COVID-19 by over 92 professional fact-checking organisations between January and mid-July 2020 and share this corpus with the community. This resulted in 1500 tweets relating to 1274 false and 226 partially false claims, respectively. Exploratory analysis of author accounts revealed that the verified twitter handle(including Organisation/celebrity) are also involved in either creating(new tweets) or spreading(retweet) the misinformation. Additionally, we found that false claims propagate faster than partially false claims. Compare to a background corpus of COVID-19 tweets, tweets with misinformation are more often concerned with discrediting other information on social media. Authors use less tentative language and appear to be more driven by concerns of potential harm to others. Our results enable us to suggest gaps in the current scientific coverage of the topic as well as propose actions for authorities and social media users to counter misinformation.

Multilingual topic modeling for tracking COVID-19 trends based on Facebook data analysis

Social data has shown important role in tracking, monitoring and risk management of disasters. Indeed, several works focused on the benefits of social data analysis for the healthcare practices and curing domain. Similarly, these data are exploited now for tracking the COVID-19 pandemic but the majority of works exploited Twitter as source. In this paper, we choose to exploit Facebook, rarely used, for tracking the evolution of COVID-19 related trends. In fact, a multilingual dataset covering 7 languages (English (EN), Arabic (AR), Spanish (ES), Italian (IT), German (DE), French (FR) and Japanese (JP)) is extracted from Facebook public posts. The proposal is an analytics process including a data gathering step, pre-processing, LDA-based topic modeling and presentation module using graph structure. Data analysing covers the duration spanned from January 1st, 2020 to May 15, 2020 divided on three periods in cumulative way: first period January-February, second period March-April and the last one to 15 May. The results showed that the extracted topics correspond to the chronological development of what has been circulated around the pandemic and the measures that have been taken according to the various languages under discussion representing several countries.

Understanding the Public Discussion About the Centers for Disease Control and Prevention During the COVID-19 Pandemic Using Twitter Data: Text Mining Analysis Study

BACKGROUND

The Centers for Disease Control and Prevention (CDC) is a national public health protection agency in the United States. With the escalating impact of the COVID-19 pandemic on society in the United States and around the world, the CDC has become one of the focal points of public discussion.

OBJECTIVE

This study aims to identify the topics and their overarching themes emerging from the public COVID-19-related discussion about the CDC on Twitter and to further provide insight into public's concerns, focus of attention, perception of the CDC's current performance, and expectations from the CDC.

METHODS

Tweets were downloaded from a large-scale COVID-19 Twitter chatter data set from March 11, 2020, when the World Health Organization declared COVID-19 a pandemic, to August 14, 2020. We used R (The R Foundation) to clean the tweets and retain tweets that contained any of five specific keywords-cdc, CDC, centers for disease control and prevention, CDCgov, and cdcgov-while eliminating all 91 tweets posted by the CDC itself. The final data set included in the analysis consisted of 290,764 unique tweets from 152,314 different users. We used R to perform the latent Dirichlet allocation algorithm for topic modeling.

RESULTS

The Twitter data generated 16 topics that the public linked to the CDC when they talked about COVID-19. Among the topics, the most discussed was COVID-19 death counts, accounting for 12.16% (n=35,347) of the total 290,764 tweets in the analysis, followed by general opinions about the credibility of the CDC and other authorities and the CDC's COVID-19 guidelines, with over 20,000 tweets for each. The 16 topics fell into four overarching themes: knowing the virus and the situation, policy and government actions, response guidelines, and general opinion about credibility.

CONCLUSIONS

Social media platforms, such as Twitter, provide valuable databases for public opinion. In a protracted pandemic, such as COVID-19, quickly and efficiently identifying the topics within the public discussion on Twitter would help public health agencies improve the next-round communication with the public.

Evaluating the motivation of Red Cross Health volunteers in the COVID-19 pandemic: a mixed-methods study protocol

INTRODUCTION

Voluntary organisations provide essential support to vulnerable populations and front-line health responders to the COVID-19 pandemic. The French Red Cross (FRC) is prominent among organisations offering health and support services in the current crisis. Comprised primarily of lay volunteers and some trained health workers, FRC volunteers in the Paris (France) region have faced challenges in adapting to pandemic conditions, working with sick and vulnerable populations, managing limited resources and coping with high demand for their services. Existing studies of volunteers focus on individual, social and organisational determinants of motivation, but attend less to contextual ones. Public health incertitude about the COVID-19 pandemic is an important feature of this pandemic. Whether and how uncertainty interacts with volunteer understandings and experiences of their work and organisational relations to contribute to Red Cross worker motivation is the focus of this investigation.

METHODS AND ANALYSIS

This mixed-methods study will investigate volunteer motivation using ethnographic methods and social network listening. Semi-structured interviews and observations will illuminate FRC volunteer work relations, experiences and concerns during the pandemic. A questionnaire targeting a sample of Paris region volunteers will allow quantification of motivation. These findings will iteratively shape and be influenced by a social media (Twitter) analysis of biomedical and public health uncertainties and debates around COVID-19. These tweets provide insight into a French lay public's interpretations of these debates. We evaluate whether and how socio-political conditions and discourses concerning COVID-19 interact with volunteer experiences, working conditions and organisational relations to influence volunteer motivation. Data collection began on 15 June 2020 and will continue until 15 April 2021.

ETHICS AND DISSEMINATION

The protocol has received ethical approval from the Institut Pasteur Institutional Review Board (no 2020-03). We will disseminate findings through peer-reviewed articles, conference presentations and recommendations to the FRC.

Toward a conceptual framework of health crisis information needs: an analysis of COVID-19 questions in a Chinese social Q&A website

Purpose

The purpose of this paper is to understand how the contextual factors of health crisis information needs are different from a general health context and how these factors work together to shape human information needs.

Design/methodology/approach

This study collected the COVID-19-related questions posted on a Chinese social Q&A website for a period of 90 days since the pandemic outbreak in China. A qualitative thematic approach was applied to analyze the 1,681 valid questions using an open coding process.

Findings

A taxonomy of information need topics for a health crisis context that identifies 8 main categories and 33 subcategories was developed, from which four overarching themes were extracted. These include understanding, clarification and preparation; affection expression of worries and confidence; coping with a challenging situation and resuming normal life; and social roles in the pandemic. The authors discussed the differences between a health crisis and a normal health context shaping information needs. Finally, a conceptual framework was developed to illustrate the typology, nature and triggers of health crisis information needs.

Research limitations/implications

First, only the Baidu Zhidao platform was investigated, and caution is advised before assuming the generalizability of the results, as the questioners of Baidu Zhidao are not representative of the whole population. Furthermore, since at the time of writing the COVID-19 is still in an emerging and evolving situation (Centers for Disease Control and Prevention, 2020), the collected data included only a relatively small sample size compared to the post-pandemic period, and this might have impact on the interpretation of the study’s findings.

Practical implications

The study’s taxonomy of information needs provides a reference for indexing and organizing related information during a disaster.

Social implications

The study helps authoritative organizations track and send information in social media and to inform about policies related to the pandemic (e.g., quarantine and traffic control policies in our study) to the right people in the right regions and settings when the next disaster emerges.

Originality/value

The taxonomy of information need topics for a health crisis context can be used to index and organize related information during a disaster and support many information agents to enhance their information service practices. It also deepens the understanding of the formation mechanism of information needs during a global health crisis.

Topics, Trends, and Sentiments of Tweets About the COVID-19 Pandemic: Temporal Infoveillance Study

Background

With restrictions on movement and stay-at-home orders in place due to the COVID-19 pandemic, social media platforms such as Twitter have become an outlet for users to express their concerns, opinions, and feelings about the pandemic. Individuals, health agencies, and governments are using Twitter to communicate about COVID-19.

Objective

The aims of this study were to examine key themes and topics of English-language COVID-19–related tweets posted by individuals and to explore the trends and variations in how the COVID-19–related tweets, key topics, and associated sentiments changed over a period of time from before to after the disease was declared a pandemic.

Methods

Building on the emergent stream of studies examining COVID-19–related tweets in English, we performed a temporal assessment covering the time period from January 1 to May 9, 2020, and examined variations in tweet topics and sentiment scores to uncover key trends. Combining data from two publicly available COVID-19 tweet data sets with those obtained in our own search, we compiled a data set of 13.9 million English-language COVID-19–related tweets posted by individuals. We use guided latent Dirichlet allocation (LDA) to infer themes and topics underlying the tweets, and we used VADER (Valence Aware Dictionary and sEntiment Reasoner) sentiment analysis to compute sentiment scores and examine weekly trends for 17 weeks.

Results

Topic modeling yielded 26 topics, which were grouped into 10 broader themes underlying the COVID-19–related tweets. Of the 13,937,906 examined tweets, 2,858,316 (20.51%) were about the impact of COVID-19 on the economy and markets, followed by spread and growth in cases (2,154,065, 15.45%), treatment and recovery (1,831,339, 13.14%), impact on the health care sector (1,588,499, 11.40%), and governments response (1,559,591, 11.19%). Average compound sentiment scores were found to be negative throughout the examined time period for the topics of spread and growth of cases, symptoms, racism, source of the outbreak, and political impact of COVID-19. In contrast, we saw a reversal of sentiments from negative to positive for prevention, impact on the economy and markets, government response, impact on the health care industry, and treatment and recovery.

Conclusions

Identification of dominant themes, topics, sentiments, and changing trends in tweets about the COVID-19 pandemic can help governments, health care agencies, and policy makers frame appropriate responses to prevent and control the spread of the pandemic.

Framing COVID-19: How we conceptualize and discuss the pandemic on Twitter

Doctors and nurses in these weeks and months are busy in the trenches, fighting against a new invisible enemy: Covid-19. Cities are locked down and civilians are besieged in their own homes, to prevent the spreading of the virus. War-related terminology is commonly used to frame the discourse around epidemics and diseases. The discourse around the current epidemic makes use of war-related metaphors too, not only in public discourse and in the media, but also in the tweets written by non-experts of mass communication. We hereby present an analysis of the discourse around #Covid-19, based on a large corpus tweets posted on Twitter during March and April 2020. Using topic modelling we first analyze the topics around which the discourse can be classified. Then, we show that the WAR framing is used to talk about specific topics, such as the virus treatment, but not others, such as the effects of social distancing on the population. We then measure and compare the popularity of the WAR frame to three alternative figurative frames (MONSTER, STORM and TSUNAMI) and a literal frame used as control (FAMILY). The results show that while the FAMILY frame covers a wider portion of the corpus, among the figurative frames WAR, a highly conventional one, is the frame used most frequently. Yet, this frame does not seem to be apt to elaborate the discourse around some aspects involved in the current situation. Therefore, we conclude, in line with previous suggestions, a plethora of framing options-or a metaphor menu-may facilitate the communication of various aspects involved in the Covid-19-related discourse on the social media, and thus support civilians in the expression of their feelings, opinions and beliefs during the current pandemic.

Identifying Influential Factors in the Discussion Dynamics of Emerging Health Issues on Social Media: Computational Study

Background

Social media has become a major resource for observing and understanding public opinions using infodemiology and infoveillance methods, especially during emergencies such as disease outbreaks. For public health agencies, understanding the driving forces of web-based discussions will help deliver more effective and efficient information to general users on social media and the web.

Objective

The study aimed to identify the major contributors that drove overall Zika-related tweeting dynamics during the 2016 epidemic. In total, 3 hypothetical drivers were proposed: (1) the underlying Zika epidemic quantified as a time series of case counts; (2) sporadic but critical real-world events such as the 2016 Rio Olympics and World Health Organization’s Public Health Emergency of International Concern (PHEIC) announcement, and (3) a few influential users’ tweeting activities.

Methods

All tweets and retweets (RTs) containing the keyword Zika posted in 2016 were collected via the Gnip application programming interface (API). We developed an analytical pipeline, EventPeriscope, to identify co-occurring trending events with Zika and quantify the strength of these events. We also retrieved Zika case data and identified the top influencers of the Zika discussion on Twitter. The influence of 3 potential drivers was examined via a multivariate time series analysis, signal processing, a content analysis, and text mining techniques.

Results

Zika-related tweeting dynamics were not significantly correlated with the underlying Zika epidemic in the United States in any of the four quarters in 2016 nor in the entire year. Instead, peaks of Zika-related tweeting activity were strongly associated with a few critical real-world events, both planned, such as the Rio Olympics, and unplanned, such as the PHEIC announcement. The Rio Olympics was mentioned in >15% of all Zika-related tweets and PHEIC occurred in 27% of Zika-related tweets around their respective peaks. In addition, the overall tweeting dynamics of the top 100 most actively tweeting users on the Zika topic, the top 100 users receiving most RTs, and the top 100 users mentioned were the most highly correlated to and preceded the overall tweeting dynamics, making these groups of users the potential drivers of tweeting dynamics. The top 100 users who retweeted the most were not critical in driving the overall tweeting dynamics. There were very few overlaps among these different groups of potentially influential users.

Conclusions

Using our proposed analytical workflow, EventPeriscope, we identified that Zika discussion dynamics on Twitter were decoupled from the actual disease epidemic in the United States but were closely related to and highly influenced by certain sporadic real-world events as well as by a few influential users. This study provided a methodology framework and insights to better understand the driving forces of web-based public discourse during health emergencies. Therefore, health agencies could deliver more effective and efficient web-based communications in emerging crises.

Google Trends-based non-English language query data and epidemic diseases: a cross-sectional study of the popular search behaviour in Taiwan

OBJECTIVE

This study developed a surveillance system suitable for monitoring epidemic outbreaks and assessing public opinion in non-English-speaking countries. We evaluated whether social media reflects social uneasiness and fear during epidemic outbreaks and natural catastrophes.

DESIGN

Cross-sectional study.

SETTING

Freely available epidemic data in Taiwan.

MAIN OUTCOME MEASURE

We used weekly epidemic incidence data obtained from the Taiwan Centers for Disease Control and online search query data obtained from Google Trends between 4 October 2015 and 2 April 2016. To validate whether non-English query keywords were useful surveillance tools, we estimated the correlation between online query data and epidemic incidence in Taiwan.

RESULTS

With our approach, we noted that keywords ('common cold'), ('fever') and ('cough') exhibited good to excellent correlation between Google Trends query data and influenza incidence (r=0.898, p<0.001; r=0.773, p<0.001; r=0.796, p<0.001, respectively). They also displayed high correlation with influenza-like illness emergencies (r=0.900, p<0.001; r=0.802, p<0.001; r=0.886, p<0.001, respectively) and outpatient visits (r=0.889, p<0.001; r=0.791, p<0.001; r=0.870, p<0.001, respectively). We noted that the query ('enterovirus') exhibited excellent correlation with the number of enterovirus-infected patients in emergency departments (r=0.914, p<0.001).

CONCLUSIONS

These results suggested that Google Trends can be a good surveillance tool for epidemic outbreaks, even in Taiwan, the non-English-speaking country. Online search activity indicates that people are concerned about epidemic diseases, even if they do not visit hospitals. This prompted us to develop useful tools to monitor social media during an epidemic because such media usage reflects infectious disease trends more quickly than does traditional reporting.

Phenomenon of depression and anxiety related to precautions for prevention among population during the outbreak of COVID-19 in Kurdistan Region of Iraq: based on questionnaire survey

Purpose

Since December 2019, the coronavirus disease 2019 (COVID-19) epidemic has swept the world, causing widespread burden and increasingly hospitalizations. Researchers from around the world have tried to study the virus and its effect with more precision in various fields. The purpose of this study is to identify levels of anxiety and depression with regard to precautionary for prevention of COVID-19, and to identify the relationship between demographic variables and both depression and anxiety.

Methods

This was a descriptive cross-sectional study; data were collected by questionnaire via a mobile phone application in the Kurdistan Region of Iraq from 25 March, 2020 to 5 April, 2020. The sample size was 894 after deleting 20 cases because of duplication. The questionnaire consists of three main parts; part one is related to the sociodemographic characteristics of participants, the second and third parts consist of items related to depression and anxiety about COVID-19 using a 5-point Likert Scale (1 = of course no, 2 = no , 3 = normal for me, 4 = yes, and 5 = of course yes). Data was analyzed using SPSS V.25.

Results

The majority of the participants were from Erbil (58.8%), most of them were male (58.4%); nearly 21.2% preferred quarantine and 41.7% chose curfew as a best way to to avoid being infected by COVID-19. Most of the participants had depression because of people's lack of knowledge about how to protect themselves from the virus (88.14%), while the majority of them had anxiety concerning shopping and contact with infected people (97%) and financial problems (97%). Females had higher rates of COVID-19 depression than did males. There was a significant correlation between age and home setting and anxiety, and a significant association between marital status and level of education and depression. There was no significant association between other variables and depression and anxiety

Conclusion

The findings of the study indicated that the majority of participants were depressed and had anxiety about COVID-19. There was a significant association between gender and depression and anxiety, while there was no significant association between occupation and income, and depression and anxiety.

Electronic supplementary material

The online version of this article (10.1007/s10389-020-01325-9) contains supplementary material, which is available to authorized users.

Sexual transmission of Zika virus on Twitter: A depoliticised epidemic

During global health crises, different narratives regarding infectious disease epidemics circulate in traditional media (e.g. news agencies, television channels) and social media. Our study investigated the narratives related to sexual transmission of Zika virus that circulated on Twitter during a public health emergency and analyzed the relationship between information on Twitter and on traditional media. We examined 10,748 tweets posted during the peaks of Twitter activity between January and March 2016. Posts in English, Spanish, French, and Portuguese and websites linked to tweets were manually reviewed and analyzed thematically. During the study period, there were three peaks of Twitter activity related to the sexual transmission of Zika. Most tweets in the first peak (n = 412) had humorous/sarcastic content (55%). Most tweets in the second and third peaks (n = 5,154 and n = 5,182, respectively) disseminated information (>93%). Across languages, textual and visual content on the websites were predominantly placed online by traditional media and highlighted epidemiological narratives published by public health agencies, with little or no mention of the concerns or experiences of individuals most affected by Zika. Prioritising epidemiological/clinical aspects of epidemics may have a depoliticising effect and contribute to overlooking socio-economic determinants of the Zika epidemic and issues related to reproductive justice.

Infodemiology and Infoveillance: Scoping Review

BACKGROUND

Web-based sources are increasingly employed in the analysis, detection, and forecasting of diseases and epidemics, and in predicting human behavior toward several health topics. This use of the internet has come to be known as infodemiology, a concept introduced by Gunther Eysenbach. Infodemiology and infoveillance studies use web-based data and have become an integral part of health informatics research over the past decade.

OBJECTIVE

The aim of this paper is to provide a scoping review of the state-of-the-art in infodemiology along with the background and history of the concept, to identify sources and health categories and topics, to elaborate on the validity of the employed methods, and to discuss the gaps identified in current research.

METHODS

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed to extract the publications that fall under the umbrella of infodemiology and infoveillance from the JMIR, PubMed, and Scopus databases. A total of 338 documents were extracted for assessment.

RESULTS

Of the 338 studies, the vast majority (n=282, 83.4%) were published with JMIR Publications. The Journal of Medical Internet Research features almost half of the publications (n=168, 49.7%), and JMIR Public Health and Surveillance has more than one-fifth of the examined studies (n=74, 21.9%). The interest in the subject has been increasing every year, with 2018 featuring more than one-fourth of the total publications (n=89, 26.3%), and the publications in 2017 and 2018 combined accounted for more than half (n=171, 50.6%) of the total number of publications in the last decade. The most popular source was Twitter with 45.0% (n=152), followed by Google with 24.6% (n=83), websites and platforms with 13.9% (n=47), blogs and forums with 10.1% (n=34), Facebook with 8.9% (n=30), and other search engines with 5.6% (n=19). As for the subjects examined, conditions and diseases with 17.2% (n=58) and epidemics and outbreaks with 15.7% (n=53) were the most popular categories identified in this review, followed by health care (n=39, 11.5%), drugs (n=40, 10.4%), and smoking and alcohol (n=29, 8.6%).

CONCLUSIONS

The field of infodemiology is becoming increasingly popular, employing innovative methods and approaches for health assessment. The use of web-based sources, which provide us with information that would not be accessible otherwise and tackles the issues arising from the time-consuming traditional methods, shows that infodemiology plays an important role in health informatics research.

Gaining a deeper understanding of nutrition using social networks and user-generated content

Using user-generated content (UGC) on Twitter, the present study identifies the main themes that revolve around the concept of healthy diet and determine user feelings about various foods. Using a dataset of tweets with the hashtag "#Diet" or "#FoodDiet" (n = 10.591), we first use a Latent Dirichlet Allocation (LDA) model to identify the food categories most discussed on Twitter. Then, based on the results of the LDA model, we apply sentiment analysis to divide the identified tweets into three groups (negative, positive and neutral) based on the feelings expressed in corresponding tweets. Finally, the text mining approach is performed to identify foods according to the feelings expressed about those in corresponding tweets, as well as to derive key indicators that collectively present the UGC-based knowledge of healthy eating. The results of the present study show that among the foods most negatively perceived in the UGC are bacon, sugar, processed foods, red meat, and snacks. By contrast, water, apples, salads, broccoli and spinach are evaluated more positively. Furthermore, our findings suggest that the collective UGC knowledge is lacking on such healthy foods as fish, poultry, dry beans, nuts, as well as yogurt and cheese. The results of the present study can help the World Health Organization (WHO), as well as other institutions concerned with the study of healthy eating, to improve their communication policies on healthy products and preparation of balanced diets.

Coronavirus Goes Viral: Quantifying the COVID-19 Misinformation Epidemic on Twitter

Background

Since the beginning of the coronavirus disease 2019 (COVID-19) epidemic, misinformation has been spreading uninhibited over traditional and social media at a rapid pace. We sought to analyze the magnitude of misinformation that is being spread on Twitter (Twitter, Inc., San Francisco, CA) regarding the coronavirus epidemic. 

Materials and methods

We conducted a search on Twitter using 14 different trending hashtags and keywords related to the COVID-19 epidemic. We then summarized and assessed individual tweets for misinformation in comparison to verified and peer-reviewed resources. Descriptive statistics were used to compare terms and hashtags, and to identify individual tweets and account characteristics.

Results

The study included 673 tweets. Most tweets were posted by informal individuals/groups (66%), and 129 (19.2%) belonged to verified Twitter accounts. The majority of included tweets contained serious content (91.2%); 548 tweets (81.4%) included genuine information pertaining to the COVID-19 epidemic. Around 70% of the tweets tackled medical/public health information, while the others were pertaining to sociopolitical and financial factors. In total, 153 tweets (24.8%) included misinformation, and 107 (17.4%) included unverifiable information regarding the COVID-19 epidemic. The rate of misinformation was higher among informal individual/group accounts (33.8%, p: <0.001). Tweets from unverified Twitter accounts contained more misinformation (31.0% vs 12.6% for verified accounts, p: <0.001). Tweets from healthcare/public health accounts had the lowest rate of unverifiable information (12.3%, p: 0.04). The number of likes and retweets per tweet was not associated with a difference in either false or unverifiable content. The keyword “COVID-19” had the lowest rate of misinformation and unverifiable information, while the keywords “#2019_ncov” and “Corona” were associated with the highest amount of misinformation and unverifiable content respectively.

Conclusions

Medical misinformation and unverifiable content pertaining to the global COVID-19 epidemic are being propagated at an alarming rate on social media. We provide an early quantification of the magnitude of misinformation spread and highlight the importance of early interventions in order to curb this phenomenon that endangers public safety at a time when awareness and appropriate preventive actions are paramount.

Ebola and Localized Blame on Social Media: Analysis of Twitter and Facebook Conversations During the 2014-2015 Ebola Epidemic

This study aimed to analyze main groups accused on social media of causing or spreading the 2014-2016 Ebola epidemic in West Africa. In this analysis, blame is construed as a vehicle of meaning through which the lay public makes sense of an epidemic, and through which certain classes of people become "figures of blame". Data was collected from Twitter and Facebook using key word extraction, then categorized thematically. Our findings indicate an overall proximate blame tendency: blame was typically cast on "near-by" figures, namely national governments, and less so on "distant" figures, such as generalized figures of otherness ("Africans", global health authorities, global elites). Our results also suggest an evolution of online blame. In the early stage of the epidemic, blame directed at the affected populations was more prominent. However, during the peak of the outbreak, the increasingly perceived threat of inter-continental spread was accompanied by a progressively proximal blame tendency, directed at figures with whom the social media users had pre-existing biopolitical frustrations. Our study proposes that pro-active and on-going analysis of blame circulating in social media can usefully help to guide communications strategies, making them more responsive to public perceptions.

Trustworthy Health-Related Tweets on Social Media in Saudi Arabia: Tweet Metadata Analysis

Background

Social media platforms play a vital role in the dissemination of health information. However, evidence suggests that a high proportion of Twitter posts (ie, tweets) are not necessarily accurate, and many studies suggest that tweets do not need to be accurate, or at least evidence based, to receive traction. This is a dangerous combination in the sphere of health information.

Objective

The first objective of this study is to examine health-related tweets originating from Saudi Arabia in terms of their accuracy. The second objective is to find factors that relate to the accuracy and dissemination of these tweets, thereby enabling the identification of ways to enhance the dissemination of accurate tweets. The initial findings from this study and methodological improvements will then be employed in a larger-scale study that will address these issues in more detail.

Methods

A health lexicon was used to extract health-related tweets using the Twitter application programming interface and the results were further filtered manually. A total of 300 tweets were each labeled by two medical doctors; the doctors agreed that 109 tweets were either accurate or inaccurate. Other measures were taken from these tweets’ metadata to see if there was any relationship between the measures and either the accuracy or the dissemination of the tweets. The entire range of this metadata was analyzed using Python, version 3.6.5 (Python Software Foundation), to answer the research questions posed.

Results

A total of 34 out of 109 tweets (31.2%) in the dataset used in this study were classified as untrustworthy health information. These came mainly from users with a non-health care background and social media accounts that had no corresponding physical (ie, organization) manifestation. Unsurprisingly, we found that traditionally trusted health sources were more likely to tweet accurate health information than other users. Likewise, these provisional results suggest that tweets posted in the morning are more trustworthy than tweets posted at night, possibly corresponding to official and casual posts, respectively. Our results also suggest that the crowd was quite good at identifying trustworthy information sources, as evidenced by the number of times a tweet’s author was tagged as favorited by the community.

Conclusions

The results indicate some initially surprising factors that might correlate with the accuracy of tweets and their dissemination. For example, the time a tweet was posted correlated with its accuracy, which may reflect a difference between professional (ie, morning) and hobbyist (ie, evening) tweets. More surprisingly, tweets containing a kashida—a decorative element in Arabic writing used to justify the text within lines—were more likely to be disseminated through retweets. These findings will be further assessed using data analysis techniques on a much larger dataset in future work.

Evaluating Patient Experiences in Dry Eye Disease Through Social Media Listening Research

INTRODUCTION

Social media listening (SML) is an approach to assess patient experience in different indications. This is the first study to report the results of using SML to understand patients' experiences of living with dry eye disease (DED).

METHODS

Publicly available, English-language social media content between December 2016 and August 2017 was searched employing pre-defined criteria using Social Studio®, an online aggregator-tool for posts from social media channels. Using natural language processing (NLP), posts were indexed using patient lexicon and disease-related keywords to derive a set of patient posts. NLP was used to identify relevance, followed by further manual evaluation and analysis to generate patient insights.

RESULTS

In all, 2279 possible patient records were identified following NLP, which were filtered for relevance to disease area by analysts, resulting in a total of 1192 posts which formed the basis of this study. Of these, 77% (n = 915) were from the USA. Symptoms, causes, diagnosis and treatments were the most commonly discussed themes. Most common symptoms mentioned were eye dryness (138/901), pain (114/901) and blurry vision (110/901). Pharmaceutical drugs (prescription and over-the-counter; 55%; 764/1393), followed by medical devices (20%; 280/1393), were mentioned as major options for managing symptoms. Of the pharmaceutical drugs, eye drops (33%; 158/476) and artificial tears (10%; 49/476) were the most common over-the-counter options reported, and Restasis® (22%; 103/476) and Xiidra® (6%; 27/476) were the most common prescription drugs. Patients voiced a significant impact of DED on their daily activities (4%; 9/224), work (23%; 51/224) and driving (12%; 26/224). Lack of DED specialists, standard diagnostic procedures, effective treatment options and need to increase awareness of DED among patients were identified as the key unmet needs.

CONCLUSIONS

Insights revealed using SML strengthen our understanding about patient experiences and their unmet needs in DED. This study illustrates that an SML approach contributed effectively in generating patient insights, which can be utilised to inform early drug development process, market access strategies and stakeholder discussions.

FUNDING

Novartis Pharma AG, Basel, Switzerland. Plain language summary available for this article.

Social networks as a tool for science communication and public engagement: focus on Twitter

Social networks have been used to teach and engage people about the importance of science. The integration of social networks in the daily routines of faculties and scientists is strongly recommended to increase their personal brand, improve their skills, enhance their visibility, share and communicate science to society, promote scientific culture, and even as a tool for teaching and learning. Here we review the use of Twitter in science and comment on our previous experience of using this social network as a platform for a Massive Online Open Course (MOOC) in Spain and Latin America. We propose to extend this strategy to a pan-European Microbiology MOOC in the near future.

Use of Twitter data to improve Zika virus surveillance in the United States during the 2016 epidemic

BACKGROUND

Zika virus (ZIKV) is an emerging mosquito-borne arbovirus that can produce serious public health consequences. In 2016, ZIKV caused an epidemic in many countries around the world, including the United States. ZIKV surveillance and vector control is essential to combating future epidemics. However, challenges relating to the timely publication of case reports significantly limit the effectiveness of current surveillance methods. In many countries with poor infrastructure, established systems for case reporting often do not exist. Previous studies investigating the H1N1 pandemic, general influenza and the recent Ebola outbreak have demonstrated that time- and geo-tagged Twitter data, which is immediately available, can be utilized to overcome these limitations.

METHODS

In this study, we employed a recently developed system called Cloudberry to filter a random sample of Twitter data to investigate the feasibility of using such data for ZIKV epidemic tracking on a national and state (Florida) level. Two auto-regressive models were calibrated using weekly ZIKV case counts and zika tweets in order to estimate weekly ZIKV cases 1 week in advance.

RESULTS

While models tended to over-predict at low case counts and under-predict at extreme high counts, a comparison of predicted versus observed weekly ZIKV case counts following model calibration demonstrated overall reasonable predictive accuracy, with an R2 of 0.74 for the Florida model and 0.70 for the U.S.

MODEL

Time-series analysis of predicted and observed ZIKV cases following internal cross-validation exhibited very similar patterns, demonstrating reasonable model performance. Spatially, the distribution of cumulative ZIKV case counts (local- & travel-related) and zika tweets across all 50 U.S. states showed a high correlation (r = 0.73) after adjusting for population.

CONCLUSIONS

This study demonstrates the value of utilizing Twitter data for the purposes of disease surveillance. This is of high value to epidemiologist and public health officials charged with protecting the public during future outbreaks.

Identifying Key Topics Bearing Negative Sentiment on Twitter: Insights Concerning the 2015-2016 Zika Epidemic

Background

To understand the public sentiment regarding the Zika virus, social media can be leveraged to understand how positive, negative, and neutral sentiments are expressed in society. Specifically, understanding the characteristics of negative sentiment could help inform federal disease control agencies’ efforts to disseminate relevant information to the public about Zika-related issues.

Objective

The purpose of this study was to analyze the public sentiment concerning Zika using posts on Twitter and determine the qualitative characteristics of positive, negative, and neutral sentiments expressed.

Methods

Machine learning techniques and algorithms were used to analyze the sentiment of tweets concerning Zika. A supervised machine learning classifier was built to classify tweets into 3 sentiment categories: positive, neutral, and negative. Tweets in each category were then examined using a topic-modeling approach to determine the main topics for each category, with focus on the negative category.

Results

A total of 5303 tweets were manually annotated and used to train multiple classifiers. These performed moderately well (F1 score=0.48-0.68) with text-based feature extraction. All 48,734 tweets were then categorized into the sentiment categories. Overall, 10 topics for each sentiment category were identified using topic modeling, with a focus on the negative sentiment category.

Conclusions

Our study demonstrates how sentiment expressed within discussions of epidemics on Twitter can be discovered. This allows public health officials to understand public sentiment regarding an epidemic and enables them to address specific elements of negative sentiment in real time. Our negative sentiment classifier was able to identify tweets concerning Zika with 3 broad themes: neural defects,Zika abnormalities, and reports and findings. These broad themes were based on domain expertise and from topics discussed in journals such as Morbidity and Mortality Weekly Report and Vaccine. As the majority of topics in the negative sentiment category concerned symptoms, officials should focus on spreading information about prevention and treatment research.

Zika discourse in the Americas: A multilingual topic analysis of Twitter

This work examines Twitter discussion surrounding the 2015 outbreak of Zika, a virus that is most often mild but has been associated with serious birth defects and neurological syndromes. We introduce and analyze a collection of 3.9 million tweets mentioning Zika geolocated to North and South America, where the virus is most prevalent. Using a multilingual topic model, we automatically identify and extract the key topics of discussion across the dataset in English, Spanish, and Portuguese. We examine the variation in Twitter activity across time and location, finding that rises in activity tend to follow to major events, and geographic rates of Zika-related discussion are moderately correlated with Zika incidence (ρ = .398).

Identifying Protective Health Behaviors on Twitter: Observational Study of Travel Advisories and Zika Virus

BACKGROUND

An estimated 3.9 billion individuals live in a location endemic for common mosquito-borne diseases. The emergence of Zika virus in South America in 2015 marked the largest known Zika outbreak and caused hundreds of thousands of infections. Internet data have shown promise in identifying human behaviors relevant for tracking and understanding other diseases.

OBJECTIVE

Using Twitter posts regarding the 2015-16 Zika virus outbreak, we sought to identify and describe considerations and self-disclosures of a specific behavior change relevant to the spread of disease-travel cancellation. If this type of behavior is identifiable in Twitter, this approach may provide an additional source of data for disease modeling.

METHODS

We combined keyword filtering and machine learning classification to identify first-person reactions to Zika in 29,386 English-language tweets in the context of travel, including considerations and reports of travel cancellation. We further explored demographic, network, and linguistic characteristics of users who change their behavior compared with control groups.

RESULTS

We found differences in the demographics, social networks, and linguistic patterns of 1567 individuals identified as changing or considering changing travel behavior in response to Zika as compared with a control sample of Twitter users. We found significant differences between geographic areas in the United States, significantly more discussion by women than men, and some evidence of differences in levels of exposure to Zika-related information.

CONCLUSIONS

Our findings have implications for informing the ways in which public health organizations communicate with the public on social media, and the findings contribute to our understanding of the ways in which the public perceives and acts on risks of emerging infectious diseases.

Detecting Indicators for Startup Business Success: Sentiment Analysis Using Text Data Mining

The main aim of this study is to identify the key factors in User Generated Content (UGC) on the Twitter social network for the creation of successful startups, as well as to identify factors for sustainable startups and business models. New technologies were used in the proposed research methodology to identify the key factors for the success of startup projects. First, a Latent Dirichlet Allocation (LDA) model was used, which is a state-of-the-art thematic modeling tool that works in Python and determines the database topic by analyzing tweets for the #Startups hashtag on Twitter (n = 35.401 tweets). Secondly, a Sentiment Analysis was performed with a Supervised Vector Machine (SVM) algorithm that works with Machine Learning in Python. This was applied to the LDA results to divide the identified startup topics into negative, positive, and neutral sentiments. Thirdly, a Textual Analysis was carried out on the topics in each sentiment with Text Data Mining techniques using Nvivo software. This research has detected that the topics with positive feelings for the identification of key factors for the startup business success are startup tools, technology-based startup, the attitude of the founders, and the startup methodology development. The negative topics are the frameworks and programming languages, type of job offers, and the business angels’ requirements. The identified neutral topics are the development of the business plan, the type of startup project, and the incubator’s and startup’s geolocation. The limitations of the investigation are the number of tweets in the analyzed sample and the limited time horizon. Future lines of research could improve the methodology used to determine key factors for the creation of successful startups and could also study sustainable issues.

Leisure Motivation and Satisfaction: A Text Mining of Yoga Centres, Yoga Consumers, and Their Interactions

Understanding the motivation and satisfaction of yoga consumers is of critical importance for both leisure service providers and leisure researchers to enhance the sustainability of personal lives in terms of physical wellness and mental happiness. For this purpose, this study investigated 25,120 pairs of online ratings and reviews from 100 yoga centres in Shanghai, China using latent Dirichlet allocation (LDA)-based text mining, and successfully established the relationship between rating and review. Findings suggest that Chinese yogis are motivated by improving physical condition, improving psychological condition, gracing appearance, establishing social connection, and creating social isolation. In addition to teaching mainstream yoga, yoga centres also provide additional courses. From a consumer perspective, yogis are relatively satisfied with teachers, courses, and the environment, but complain about the supporting staff, membership price, and reservation service. Managerially, yoga centres are encouraged to continue attending to the motivations of yogis, specialising their guidance, and fostering strengths and circumventing weaknesses in their service. This study also contributes by verifying, elaborating on, and tentatively extending the framework of the Physical Activity and Leisure Motivation Scale (PALMS).