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

Harnessing Social Media Data to Understand Information Needs About Kidney Diseases and Emotional Experiences With Disease Management: Topic and Sentiment Analysis

BACKGROUND

Kidney diseases encompass a variety of conditions, including chronic kidney disease, acute kidney injury, glomerulonephritis, and polycystic kidney disease. These diseases significantly impact patients' quality of life and health care costs, often necessitating substantial lifestyle changes, especially regarding dietary management. However, patients frequently receive ambiguous or conflicting dietary advice from health care providers, leading them to seek information and support from online health communities.

OBJECTIVE

This study aimed to analyze social media data to better understand the experiences, challenges, and concerns of patients with kidney disease and their caregivers in South Korea. Specifically, it explored how online communities assist in disease management and examined the sentiment surrounding dietary management.

METHODS

Data were collected from KidneyCafe, a prominent South Korean online community for patients with kidney disease hosted on the Naver platform. A total of 124,211 posts from 10 disease-specific boards were analyzed using latent Dirichlet allocation for topic modeling and Bidirectional Encoder Representations From Transformers-based sentiment analysis. In addition, Efficiently Learning an Encoder That Classifies Token Replacements Accurately-based classification was used to further analyze posts related to disease management.

RESULTS

The analysis identified 6 main topics within the community: family health and support, medication and side effects, examination and diagnosis, disease management, surgery for dialysis, and costs and insurance. Sentiment analysis revealed that posts related to the medication and side effects and surgery for dialysis topics predominantly expressed negative sentiments. Both significant negative sentiments concerning worries about kidney transplantation among family members and positive sentiments regarding physical improvements after transplantation were expressed in posts about family health and support. For disease management, 7 key subtopics were identified, with inquiries about dietary management being the leading subtopic.

CONCLUSIONS

The findings highlight the critical role of online communities in providing support and information for patients with kidney disease and their caregivers. The insights gained from this study can inform health care providers, policy makers, and support organizations to better address the needs of patients with kidney disease, particularly in areas related to dietary management and emotional support.

People's Attitude and Perception of the Pandemic on Twitter: A Case Study of COVID-19 in India

Background Understanding the attitudes and perceptions of the general population is necessary for organizing health promotion initiatives. During outbreaks, social media has a significant impact on creating social perceptions. This study aims to identify and examine the emotions expressed and topics of discussion among Indian citizens related to COVID-19 third wave, from the messages posted on Twitter using text mining techniques. Methods Twitter messages (tweets) were downloaded using Twitter API from June 1, 2021 to July 10, 2021. After pre-processing the downloaded messages, 8933 unique tweets from various individuals were taken into account for the analysis. To identify and extract emotions expressed by the people in the Twitter texts, the text mining and sentiment analysis package "syuzhet" in R software was used. In order to identify the concerns and themes of discussion by the public during the pandemic, topic analysis was done using the Latent Dirichlet Allocation (LDA) technique. To understand and measure the tweets' reachability in relation to the themes and emotions conveyed, an engagement metrics analysis was also conducted. Results According to the emotional analysis performed on Twitter messages about the COVID-19 third wave, anticipation was exhibited maximum in 4180 tweets, followed by fear in 4070, and trust in 4001 tweets. Results of topic modeling revealed that there were widespread discussions and concerns about preventative measures to deal with the COVID-19 third wave. Engagement metrics verified that the greatest number of individuals liked and retweeted tweets expressing disgust. Maximum people favorited tweets with information on preventive measures for COVID-19, and a large number of individuals re-shared messages comparing various aspects of different COVID-19 waves. Conclusion Data from social media platforms can be used to comprehend public opinions and emotions during pandemics and emergencies. This can assist public health stakeholders in managing pandemic circumstances by planning effective health communication strategies that quickly reach a larger audience. The study's findings will assist stakeholders and public health experts in effectively using social media to communicate information about COVID-19 that combat people's negative emotions and concerns. Future research can use a similar approach to comprehend people's perspectives and concerns during outbreaks and emergency circumstances.

Leveraging twitter data to understand nurses' emotion dynamics during the COVID-19 pandemic

The nursing workforce is the largest discipline in healthcare and has been at the forefront of the COVID-19 pandemic response since the outbreak of COVID-19. However, the impact of COVID-19 on the nursing workforce is largely unknown as is the emotional burden experienced by nurses throughout the different waves of the pandemic. Conventional approaches often use survey question-based instruments to learn nurses' emotions, and may not reflect actual everyday emotions but the beliefs specific to survey questions. Social media has been increasingly used to express people's thoughts and feelings. This paper uses Twitter data to describe the emotional dynamics of registered nurse and student nurse groups residing in New South Wales in Australia during the COVID-19 pandemic. A novel analysis framework that considered emotions, talking topics, the unfolding development of COVID-19, as well as government public health actions and significant events was utilised to detect the emotion dynamics of nurses and student nurses. The results found that the emotional dynamics of registered and student nurses were significantly correlated with the development of COVID-19 at different waves. Both groups also showed various emotional changes parallel to the scale of pandemic waves and corresponding public health responses. The results have potential applications such as to adjust the psychological and/or physical support extended to the nursing workforce. However, this study has several limitations that will be considered in the future study such as not validated in a healthcare professional group, small sample size, and possible bias in tweets.

User-Chatbot Conversations During the COVID-19 Pandemic: Study Based on Topic Modeling and Sentiment Analysis

BACKGROUND

Chatbots have become a promising tool to support public health initiatives. Despite their potential, little research has examined how individuals interacted with chatbots during the COVID-19 pandemic. Understanding user-chatbot interactions is crucial for developing services that can respond to people's needs during a global health emergency.

OBJECTIVE

This study examined the COVID-19 pandemic-related topics online users discussed with a commercially available social chatbot and compared the sentiment expressed by users from 5 culturally different countries.

METHODS

We analyzed 19,782 conversation utterances related to COVID-19 covering 5 countries (the United States, the United Kingdom, Canada, Malaysia, and the Philippines) between 2020 and 2021, from SimSimi, one of the world's largest open-domain social chatbots. We identified chat topics using natural language processing methods and analyzed their emotional sentiments. Additionally, we compared the topic and sentiment variations in the COVID-19-related chats across countries.

RESULTS

Our analysis identified 18 emerging topics, which could be categorized into the following 5 overarching themes: "Questions on COVID-19 asked to the chatbot" (30.6%), "Preventive behaviors" (25.3%), "Outbreak of COVID-19" (16.4%), "Physical and psychological impact of COVID-19" (16.0%), and "People and life in the pandemic" (11.7%). Our data indicated that people considered chatbots as a source of information about the pandemic, for example, by asking health-related questions. Users turned to SimSimi for conversation and emotional messages when offline social interactions became limited during the lockdown period. Users were more likely to express negative sentiments when conversing about topics related to masks, lockdowns, case counts, and their worries about the pandemic. In contrast, small talk with the chatbot was largely accompanied by positive sentiment. We also found cultural differences, with negative words being used more often by users in the United States than by those in Asia when talking about COVID-19.

CONCLUSIONS

Based on the analysis of user-chatbot interactions on a live platform, this work provides insights into people's informational and emotional needs during a global health crisis. Users sought health-related information and shared emotional messages with the chatbot, indicating the potential use of chatbots to provide accurate health information and emotional support. Future research can look into different support strategies that align with the direction of public health policy.

COVID-19-Associated Misinformation Across the South Asian Diaspora: Qualitative Study of WhatsApp Messages

Background

South Asians, inclusive of individuals originating in India, Pakistan, Maldives, Bangladesh, Sri Lanka, Bhutan, and Nepal, comprise the largest diaspora in the world, with large South Asian communities residing in the Caribbean, Africa, Europe, and elsewhere. There is evidence that South Asian communities have disproportionately experienced COVID-19 infections and mortality. WhatsApp, a free messaging app, is widely used in transnational communication within the South Asian diaspora. Limited studies exist on COVID-19-related misinformation specific to the South Asian community on WhatsApp. Understanding communication on WhatsApp may improve public health messaging to address COVID-19 disparities among South Asian communities worldwide.

Objective

We developed the COVID-19-Associated misinfoRmation On Messaging apps (CAROM) study to identify messages containing misinformation about COVID-19 shared via WhatsApp.

Methods

We collected messages forwarded globally through WhatsApp from self-identified South Asian community members between March 23 and June 3, 2021. We excluded messages that were in languages other than English, did not contain misinformation, or were not relevant to COVID-19. We deidentified each message and coded them for one or more content categories, media types (eg, video, image, text, web link, or a combination of these elements), and tone (eg, fearful, well intentioned, or pleading). We then performed a qualitative content analysis to arrive at key themes of COVID-19 misinformation.

Results

We received 108 messages; 55 messages met the inclusion criteria for the final analytic sample; 32 (58%) contained text, 15 (27%) contained images, and 13 (24%) contained video. Content analysis revealed the following themes: "community transmission" relating to misinformation on how COVID-19 spreads in the community; "prevention" and "treatment," including Ayurvedic and traditional remedies for how to prevent or treat COVID-19 infection; and messaging attempting to sell "products or services" to prevent or cure COVID-19. Messages varied in audience from the general public to South Asians specifically; the latter included messages alluding to South Asian pride and solidarity. Scientific jargon and references to major organizations and leaders in health care were included to provide credibility. Messages with a pleading tone encouraged users to forward them to friends or family.

Conclusions

Misinformation in the South Asian community on WhatsApp spreads erroneous ideas regarding disease transmission, prevention, and treatment. Content evoking solidarity, "trustworthy" sources, and encouragement to forward messages may increase the spread of misinformation. Public health outlets and social media companies must actively combat misinformation to address health disparities among the South Asian diaspora during the COVID-19 pandemic and in future public health emergencies.

A corpus-based pragmatic analysis of Jordanians Facebook status updates during COVID-19

This study investigates the communicative functions of status updates on Facebook during COVID-19. For this purpose, a corpus of 500 status updates was collected from 100 Facebook users for 90 consecutive days. Subsequently, the data were characterized into five speech acts drawing heavily on Searle's speech act framework, prominent among which are expressives and assertives. Data analysis revealed that status updates could be considered a substantial medium for understanding intended human communication. Various types of speech acts were used with different frequencies and percentages, although people were inclined mostly to use expressive speech acts. The sociocultural variations in conjunction with forming and constructing identities were reflected in the status updates manifested in the current situation of the pandemic, which makes Jordanians appear more humorous than before. This research is significant because studying aspects of a language helps in understanding the hidden motivations, beliefs, ideas, attitudes and identities along with the social, cultural, and political factors, which in turn provides logical solutions for certain problems.

Measuring the Evolution of Risk Communication Strategy for Health Authorities During the COVID-19 Pandemic: An Empirical Comparison Between China and the United States

Objectives: Investigate how the speech context of news conferences reveals the risk communication strategies for health authorities during COVID-19 and measure the evolution of those risk communication strategies.

Methods: We collected news conference transcripts concerning COVID-19 for the first quarter from the official websites of the Centers for Disease Control and Prevention (CDC) and the National Health Commission of the People’s Republic of China (NHC) in 2020. Quantitative analyses were conducted on the topics and emotions of transcripts to measure the evolution of risk communication strategy. A total of three types of analysis were carried out in our study: topic, sentiment, and risk communication evolution analyses.

Results: The trending topics and the number of these in the two institutions evolved with the infection status. The CDC and NHC maintained primarily neutral sentiment, while the non-neutral sentiment of the CDC swung more dramatically. Furthermore, the changing pattern of risk communication evolution for the CDC and NHC varied, where the latter had a more stable change routine.

Conclusion: Our study finds that the strategies could be measured by topic variation, emotional expressions, and confirmed cases. The CDC and NHC tend to adopt different risk communication strategies and have specific change routines facing the pandemic. In addition, our findings contribute to addressing the WHO research agenda for managing risk communication during the COVID-19 pandemic, which helps health authorities formulate and measure risk communication strategies.

On network backbone extraction for modeling online collective behavior

Collective user behavior in social media applications often drives several important online and offline phenomena linked to the spread of opinions and information. Several studies have focused on the analysis of such phenomena using networks to model user interactions, represented by edges. However, only a fraction of edges contribute to the actual investigation. Even worse, the often large number of non-relevant edges may obfuscate the salient interactions, blurring the underlying structures and user communities that capture the collective behavior patterns driving the target phenomenon. To solve this issue, researchers have proposed several network backbone extraction techniques to obtain a reduced and representative version of the network that better explains the phenomenon of interest. Each technique has its specific assumptions and procedure to extract the backbone. However, the literature lacks a clear methodology to highlight such assumptions, discuss how they affect the choice of a method and offer validation strategies in scenarios where no ground truth exists. In this work, we fill this gap by proposing a principled methodology for comparing and selecting the most appropriate backbone extraction method given a phenomenon of interest. We characterize ten state-of-the-art techniques in terms of their assumptions, requirements, and other aspects that one must consider to apply them in practice. We present four steps to apply, evaluate and select the best method(s) to a given target phenomenon. We validate our approach using two case studies with different requirements: online discussions on Instagram and coordinated behavior in WhatsApp groups. We show that each method can produce very different backbones, underlying that the choice of an adequate method is of utmost importance to reveal valuable knowledge about the particular phenomenon under investigation.

Platform Effects on Public Health Communication: A Comparative and National Study of Message Design and Audience Engagement Across Twitter and Facebook

BACKGROUND

Public health agencies widely adopt social media for health and risk communication. Moreover, different platforms have different affordances, which may impact the quality and nature of the messaging and how the public engages with the content. However, these platform effects are not often compared in studies of health and risk communication and not previously for the COVID-19 pandemic.

OBJECTIVE

This study measures the potential media effects of Twitter and Facebook on public health message design and engagement by comparing message elements and audience engagement in COVID-19-related posts by local, state, and federal public health agencies in the United States during the pandemic, to advance theories of public health messaging on social media and provide recommendations for tailored social media communication strategies.

METHODS

We retrieved all COVID-19-related posts from major US federal agencies related to health and infectious disease, all major state public health agencies, and selected local public health departments on Twitter and Facebook. A total of 100,785 posts related to COVID-19, from 179 different accounts of 96 agencies, were retrieved for the entire year of 2020. We adopted a framework of social media message elements to analyze the posts across Facebook and Twitter. For manual content analysis, we subsampled 1677 posts. We calculated the prevalence of various message elements across the platforms and assessed the statistical significance of differences. We also calculated and assessed the association between message elements with normalized measures of shares and likes for both Facebook and Twitter.

RESULTS

Distributions of message elements were largely similar across both sites. However, political figures (P<.001), experts (P=.01), and nonpolitical personalities (P=.01) were significantly more present on Facebook posts compared to Twitter. Infographics (P<.001), surveillance information (P<.001), and certain multimedia elements (eg, hyperlinks, P<.001) were more prevalent on Twitter. In general, Facebook posts received more (normalized) likes (0.19%) and (normalized) shares (0.22%) compared to Twitter likes (0.08%) and shares (0.05%). Elements with greater engagement on Facebook included expressives and collectives, whereas posts related to policy were more engaged with on Twitter. Science information (eg, scientific explanations) comprised 8.5% (73/851) of Facebook and 9.4% (78/826) of Twitter posts. Correctives of misinformation only appeared in 1.2% (11/851) of Facebook and 1.4% (12/826) of Twitter posts.

CONCLUSIONS

In general, we find a data and policy orientation for Twitter messages and users and a local and personal orientation for Facebook, although also many similarities across platforms. Message elements that impact engagement are similar across platforms but with some notable distinctions. This study provides novel evidence for differences in COVID-19 public health messaging across social media sites, advancing knowledge of public health communication on social media and recommendations for health and risk communication strategies on these online platforms.

Readability of Korean-Language COVID-19 Information from the South Korean National COVID-19 Portal Intended for the General Public: Cross-sectional Infodemiology Study

BACKGROUND

The coronavirus pandemic has increased reliance on the internet as a tool for disseminating information; however, information is useful only when it can be understood. Prior research has shown that web-based health information is not always easy to understand. It is not yet known whether the Korean-language COVID-19 information from the internet is easy for the general public to understand.

OBJECTIVE

We aimed to evaluate the readability of Korean-language COVID-19 information intended for the general public from the national COVID-19 portal of South Korea.

METHODS

A total of 122 publicly available COVID-19 information documents written in Korean were obtained from the South Korean national COVID-19 portal. We determined the level of readability (at or below ninth grade, 10th to 12th grade, college, or professional) of each document using a readability tool for Korean-language text. We measured the reading time, character count, word count, sentence count, and paragraph count for each document. We also evaluated the characteristics of difficult-to-read documents to modify the readability from difficult to easy.

RESULTS

The median readability level was at a professional level; 90.2% (110/122) of the information was difficult to read. In all 4 topics, few documents were easy to read (overview: 5/12, 41.7%; prevention: 6/97, 6.2%; test: 0/5, 0%; treatment: 1/8, 12.5%; P=.006), with a median 11th-grade readability level for overview, a median professional readability level for prevention, and median college readability levels for test and treatment. Difficult-to-read information had the following characteristics in common: literacy style, medical jargon, and unnecessary detail.

CONCLUSIONS

In all 4 topics, most of the Korean-language COVID-19 web-based information intended for the general public provided by the national COVID-19 portal of South Korea was difficult to read; the median readability levels exceeded the recommended ninth-grade level. Readability should be a key consideration in developing public health documents, which play an important role in disease prevention and health promotion.

TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels

As the world struggles with several compounded challenges caused by the COVID-19 pandemic in the health, economic, and social domains, timely access to disaggregated national and sub-national data are important to understand the emergent situation but it is difficult to obtain. The widespread usage of social networking sites, especially during mass convergence events, such as health emergencies, provides instant access to citizen-generated data offering rich information about public opinions, sentiments, and situational updates useful for authorities to gain insights. We offer a large-scale social sensing dataset comprising two billion multilingual tweets posted from 218 countries by 87 million users in 67 languages. We used state-of-the-art machine learning models to enrich the data with sentiment labels and named-entities. Additionally, a gender identification approach is proposed to segregate user gender. Furthermore, a geolocalization approach is devised to geotag tweets at country, state, county, and city granularities, enabling a myriad of data analysis tasks to understand real-world issues at national and sub-national levels. We believe this multilingual data with broader geographical and longer temporal coverage will be a cornerstone for researchers to study impacts of the ongoing global health catastrophe and to manage adverse consequences related to people’s health, livelihood, and social well-being.

How to Make Health and Risk Communication on Social Media More "Social" During COVID-19

Social media have changed the way citizens participate in and express opinions about government policy. Social media serve organizations in achieving four main goals: interacting with citizens; fostering citizen participation; furthering open government; and analyzing/monitoring public opinion and activities. We contend that despite the importance of social media, international and local health organizations have been slow to adopt to them, primarily due to the discrepancy between intraorganizational discourse modes and the type of discourse suitable for dialogue with the public. In this perspective paper, we recommend strategies for such public dialogue based on understanding the challenges faced by organizations on the road to becoming more “social” in their social media presence and in their health and risk communication. Subsequently, we propose an integrative approach that combines three complementary paths: (1) putting the “social” back into health organizations’ culture by inserting more “social” content into the internal organizational discourse through consultation with experts from different fields, including those who diverge from the scientific consensus. (2) Using strategies to enable health organizations to respond to the public on social networks, based on health communications research and studies on emerging infectious disease (EID) communication. (3) Engaging the public on social media based on the participatory approach, which considers the public as a partner that understands science and can work with the organizations to develop an open and innovative pandemic realm by using crowdsourcing to solve complex global health problems. For each path, we define the current challenges, among which are (1) overcoming organizational groupthink and hidden profiles, (2) treating all unofficial information as misleading, and (3) insufficient public engagement in solving complex global problems. We then offer recommendations for dealing with each challenge.

Structural Topic Model Analysis of Mask-Wearing Issue Using International News Big Data

Media plays an important role in the acquisition of health information worldwide. This was particularly evident in the face of the COVID-19 epidemic. Relatedly, it is practical and desirable for people to wear masks for health, fashion, and religious regions. However, depending on cultural differences, people naturally accept wearing a mask, or they look upon it negatively. In 2020, the COVID-19 pandemic led to widespread mask-wearing mandates worldwide. In the case of COVID-19, wearing a mask is strongly recommended, so by analyzing the news data before and after the spread of the epidemic, it is possible to see how the direction of crisis management is being structured. In particular, by utilizing big data analysis of international news data, discourses around the world can be analyzed more deeply. This study collected and analyzed 58,061 international news items related to mask-wearing from 1 January 2019 to 31 December 2020. The collected dataset was compared before and after the World Health Organization’s pandemic declaration by applying structural topic model analysis. The results revealed that prior to the declaration, issues related to the COVID-19 outbreak were emphasized, but afterward, issues related to movement restrictions, quarantine management, and local economic impacts emerged.