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

Exploring the therapeutic potential of galidesivir analogs against Zaire ebolavirus protein 24 (V24): database screening, molecular docking, drug-relevant property evaluation and molecular dynamics simulations

The recent outbreak of the Ebola virus (EBOV) has marked it as one of the most severe health threats globally. Among various anti-EBOV inhibitors studied, galidesivir (BCX4430) has shown remarkable efficacy. This study aims to identify novel potential anti-EBOV drugs among galidesivir analogs, focusing on the Zaire ebolavirus (Z-EBOV), which exhibits a mortality rate of 90%. We subjected 200 candidate compounds to molecular docking calculations, followed by an evaluation of the bioactivity of the top 25 compounds using the OSIRIS Property Explorer. Initial 50 ns molecular dynamics (MD) simulations were then performed. According to our findings, only six compounds exhibited positive drug scores. We further performed molecular mechanics Poisson-Boltzmann surface area (MM/PBSA) calculations of binding energy over 50 ns, selecting the two top-performing compounds for extended 150 ns MD simulations. CID 117698807 and CID 117712809 showed higher binding stability compared to galidesivir, with ΔGbinding values of -36.7 and -53.4 kcal/mol, respectively. Both compounds demonstrated high stability within the Z-EBOV-V24 active site over the 150 ns MD simulations. Hence, our study proposes CID 117698807 and CID 117712809 as potential anti-Z-EBOV-V24 drug candidates, warranting further investigation.Communicated by Ramaswamy H. Sarma.

Navigating the asthma network on Twitter: Insights from social network and sentiment analysis

Background

Asthma is a condition in which the airways become inflamed and constricted, causing breathing difficulties, wheezing, coughing, and chest tightness. Social networks can have a substantial effect on asthma management and results. However, no studies of social networks addressing asthma have been undertaken.

Objective

The aim of this research was to identify the significant social network structures, key influencers, top topics, and sentiments of asthma-related Twitter conversations.

Methods

All the tweets collected for this study included the keyword "asthma" or were mentioned in or in replies to tweets that were performed. For this study, a random sample of Twitter data was collected using NodeXL Pro software between December 1, 2022, and January 29, 2023. The data collected includes the user's display name, Twitter handle, tweet text, and the tweet's publishing date and time. After being imported into the Gephi application, the NodeXL data were then shown using the Fruchterman-Reingold layout method. In our study, SNA (Social Network Analysis) metrics were utilized to identify the most popular subject using hashtags, sentiment-related phrases (positive, negative, or neutral), and top influencer by centrality measures (degree, betweenness).

Results

The study collected 48,122 tweets containing the keyword "asthma" or mentioned in replies. News reporters and journalists emerged as top influencers based on centrality measures in Twitter conversations about asthma, followed by government and healthcare institutions. Education, trigger factors (e.g., cat exposure, diet), and associated conditions were highly discussed topics on asthma-related social media posts (e.g., sarscov2, copd). Our study's sentiment analysis revealed that there were 8427 phrases associated neutral comments (18%), 12,582 words reflecting positive viewpoints (26%), and 27,111 words reflecting negative opinions (56%).

Conclusion

This study investigates the relevance of social media influencers, news reporters, health experts, health organizations, and the government in the dissemination and promotion of asthma-related education and awareness during public health information.

The Pulse of Long COVID on Twitter: A Social Network Analysis

BACKGROUND

Long coronavirus disease (COVID) is a complex and multifaceted health condition with a range of severe symptoms that can last for weeks or even months after the acute phase of the illness has passed. Employing social network analysis (SNA) can rapidly provide significant health information to communities related to long COVID. This study aimed to identify the key themes, most influential users, and overall sentiments in the Twitter discourse on long COVID.

METHODS

Data were collected from a Twitter search with the specific keywords "long COVID" from December 1, 2022, to February 22, 2023, using NodeXL Pro. Visualizations, including network graphs and key influencers, were created using Gephi, and sentiment analysis was conducted with Azure Machine.

RESULTS

In total, 119,185 tweets from 94325 users were related to long COVID. Top influencers include medical professionals, researchers, journalists, and public figures, with news media platforms as primary information sources; the most common hashtag was #longCOVID, indicating that it is a significant issue of concern among the Twitter community. In the sentiment analysis, most tweets were negative.

CONCLUSION

The study highlights the importance of critically evaluating information shared by influential users and seeking out multiple sources of information when making health-related decisions. In addition, it emphasizes the value of examining social media conversations to understand public discourse on long COVID and suggests that future researchers could explore the role of social media in shaping public perceptions and behaviors related to health issues. Strategies for enhancing scientific journal engagement and influence in online discussions are discussed as well.

A Survey on COVID-19 Data Analysis Using AI, IoT, and Social Media

Coronaviruses are a well-established and deadly group of viruses that cause illness in both humans and animals. The novel type of this virus group, named COVID-19, was firstly reported in December 2019, and, with the passage of time, coronavirus has spread to almost all parts of the world. Coronavirus has been the cause of millions of deaths around the world. Furthermore, many countries are struggling with COVID-19 and have experimented with various kinds of vaccines to eliminate the deadly virus and its variants. This survey deals with COVID-19 data analysis and its impact on human social life. Data analysis and information related to coronavirus can greatly help scientists and governments in controlling the spread and symptoms of the deadly coronavirus. In this survey, we cover many areas of discussion related to COVID-19 data analysis, such as how artificial intelligence, along with machine learning, deep learning, and IoT, have worked together to fight against COVID-19. We also discuss artificial intelligence and IoT techniques used to forecast, detect, and diagnose patients of the novel coronavirus. Moreover, this survey also describes how fake news, doctored results, and conspiracy theories were spread over social media sites, such as Twitter, by applying various social network analysis and sentimental analysis techniques. A comprehensive comparative analysis of existing techniques has also been conducted. In the end, the Discussion section presents different data analysis techniques, provides future directions for research, and suggests general guidelines for handling coronavirus, as well as changing work and life conditions.

The victims, villains and heroes of 'panic buying': News media attribution of responsibility for COVID-19 stockpiling

Societies often respond to a crisis by attributing blame to some groups while constructing others as victims and heroes. While it has received scant sociological attention, 'panic buying' is a critical indicator of such public sentiment at the onset of a crisis, and thus a crucial site for analysis. This article traces dynamics of blame in news media representations of an extreme period of panic buying during COVID-19 in Australia. Analysis reveals that lower socio-economic and ethnically diverse consumers were blamed disproportionately. Unlike wealthier consumers who bulk-bought online, shoppers filling trollies in-store were depicted as selfish and shameful, described using dehumanising language, and portrayed as 'villains' who threatened social order. Supermarkets were cast simultaneously as 'victims' of consumer aggression and 'heroes' for their moral leadership, trustworthiness and problem-solving. This portrayal misunderstands the socio-emotional drivers of panic buying, exacerbates stigma towards already disadvantaged groups, and veils the corporate profiteering that encourages stockpiling.

Effects of Chinese provincial CDCs WeChat official account article features on user engagement during the COVID-19 pandemic

Background

WeChat has become a potent medium for disseminating public health information, especially during the coronavirus disease 2019 (COVID-19) pandemic. WeChat is important for public health organizations when considering users' information needs and preferences to further explore factors that affect user engagement.

Methods

We collected data from WeChat official accounts (WOAs) of the Chinese provincial Center for Disease Control and Prevention (CDC) to identify factors affecting and predicting the behavior of user engagement as measured by the level of reading and re-sharing during different phases of the COVID-19 pandemic between January 1, 2019, and December 31, 2020. We used multiple logistic regression analyses to identify features of articles with higher reading and re-sharing levels from 31 Chinese provincial CDCs. We developed a nomogram to predict the effect on user engagement.

Results

We collected a total of 26 302 articles. Release position, title type, article content, article type, communication skills, marketing elements, article length, and video length were key determinants of user engagement. Although the feature patterns also varied between different pandemic stages, the article content, release position, and article type were still the most prominent features driving user engagement. Regarding article content, the COVID-19 pandemic report and guidance for public protection were more likely to obtain high-level reading (normalization: odds ratio (OR) = 12.340, 95% confidence interval (CI) = 9.357-16.274) and re-sharing (normalization: OR = 7.254, 95% CI = 5.554-9.473) than other contents throughout the pandemic. When we compared release position with secondary push, users who used main push were more likely to exhibit high-level reading and re-sharing during any period, especially during normalization (OR = 6.169, 95% CI = 5.554-6.851; OR = 4.230, 95% CI = 3.833-4.669). For article type, a combination of text, links and pictures was associated with a higher rate of reading (normalization: OR = 4.262, 95% CI = 3.509-5.176) and re-sharing level (normalization: OR = 4.480, 95% CI = 3.635-5.522) compared to text only. Simultaneously, the prediction model showed good discriminatory power and calibration.

Conclusions

Discrepancies exist in article features between different pandemic stages. Public health agencies should make full use of official WOAs and consider the information needs and preferences of users in order to better carry out health education and health communication with the public when public health events occur.

The unsanitary other and racism during the pandemic: analysis of purity discourses on social media in India, France and United States of America during the COVID-19 pandemic

The global rise of populism and concomitant polarizations across disenfranchised and marginalized groups has been magnified by so-called echo chambers, and a major public health crisis like the COVID-19 pandemic has only served to fuel these intergroup tensions. Media institutions disseminating information on ways to prevent the propagation of the virus have reactivated a specific discursive phenomenon previously observed in many epidemics: the construction of a defiled 'Other'. With anthropological lenses, discourse on defilement is an interesting path to understand the continuous emergence of pseudo-scientific forms of racism. In this paper, the authors focus on 'borderline racism', that is the use of an institutionally 'impartial' discourse to reaffirm the inferiority of another race. The authors employed inductive thematic analysis of 1200 social media comments reacting to articles and videos published by six media in three different countries (France, United States and India). Results delineate four major themes structuring defilement discourses: food (and the relationship to animals), religion, nationalism and gender. Media articles and videos portrayed Western and Eastern countries through contrasting images and elicited a range of reaction in readers and viewers. The discussion reflects on how borderline racism can be an appropriate concept to understand the appearance of hygienic othering of specific subgroups on social media. Theoretical implications and recommendations on a more culturally sensitive approach of media coverage of epidemics and pandemics are discussed.

COVID-19 pandemic: Ghana and the geographies of blame

The emergence and the rapid spread of the novel Coronavirus (COVID-19) have resulted in a global public health crisis. The debilitating social and economic impact of the COVID-19 pandemic on vulnerable societies has given rise to questionings, blames, and accusations about how the pandemic has been managed at the national level. This study uses the concept of 'Geographies of blame' to investigate how the national government, citizenry and other stakeholders have blamed each other for the rise in COVID-19 cases in Ghana. The study employs a qualitative research approach and administered 45 online surveys to the residents of Accra Metropolis, Ghana, that inquired about who is to be blamed for the rising COVID-19 cases in Ghana. Our results revealed that while the government of Ghana must share the blame due to how they poorly handled the pandemic, the citizens are more to blame for the spread and continued increase of the COVID-19 cases in the country. Based on the results, the study highlights the need for a pro-active and continuous analysis of the 'babel of blame' as a useful guide to create public awareness and help governments develop and implement strategic plans to tackle the COVID-19 pandemic.

Arabic Twitter Conversation Dataset about the COVID-19 Vaccine

The development and rollout of COVID-19 vaccination around the world offers hope for controlling the pandemic. People turned to social media such as Twitter seeking information or to voice their opinion. Therefore, mining such conversation can provide a rich source of data for different applications related to the COVID-19 vaccine. In this data article, we developed an Arabic Twitter dataset of 1.1 M Arabic posts regarding the COVID-19 vaccine. The dataset was streamed over one year, covering the period from January to December 2021. We considered a set of crawling keywords in the Arabic language related to the conversation about the vaccine. The dataset consists of seven databases that can be analyzed separately or merged for further analysis. The initial analysis depicts the embedded features within the posts, including hashtags, media, and the dynamic of replies and retweets. Further, the textual analysis reveals the most frequent words that can capture the trends of the discussions. The dataset was designed to facilitate research across different fields, such as social network analysis, information retrieval, health informatics, and social science.

The deployment of social media by political authorities and health experts to enhance public information during the COVID-19 pandemic

Social media have increasingly been used by political bodies and experts to disseminate health information to the public. However, we still know little about how the communication of these actors on social media is received by other users and how it reflects trends in public trust. We examined social media dynamics in the communication of information by major actors (n = 188) involved in COVID-19 online discussions in Switzerland. These actors are scientists (experts), policymakers (government officials, cantonal executives, and other parties), and representatives of mass media. We found little correlation between Twitter features (other users' engagement and negativity in other users' replies) and the level of public trust found in representative opinion surveys. We used topic modelling in combination with correspondence analysis, and including additional variables for actor types and the period of the public debate further enabled us to detect salient episodes related to the pandemic on social media. In particular, we found that differing roles were played by the (health) experts and political authorities in terms of both topics and influence on the specific timing of the pandemic. The results of this study provide helpful conclusions for communication among political authorities, health experts, and the public.

Leveraging Dynamic Heterogeneous Networks to Study Transnational Issue Publics. The Case of the European COVID-19 Discourse on Twitter

The ongoing COVID-19 pandemic constitutes a critical phase for the transnationalization of public spheres. Against this backdrop, we ask how transnational COVID-19 related online discourse has been throughout the EU over the first year of the pandemic. Which events triggered higher transnational coherence or national structuration of this specific issue public on Twitter? In order to study these questions, we rely on Twitter data obtained from the TBCOV database, i.e., a dataset for multilingual, geolocated COVID-19 related Twitter communication. We selected corpora for the 27 member states of the EU plus the United Kingdom. We defined three research periods representing different phases of the pandemic, namely April (1st wave), August (interim) and December 2020 (2nd wave) resulting in a set of 51,893,966 unique tweets for comparative analysis. In order to measure the level and temporal variation of transnational discursive linkages, we conducted a spatiotemporal network analysis of so-called Heterogeneous Information Networks (HINs). HINs allow for the integration of multiple, heterogeneous network entities (hashtags, retweets, @-mentions, URLs and named entities) to better represent the complex discursive structures reflected in social media communication. Therefrom, we obtained an aggregate measure of transnational linkages on a daily base by relating these linkages back to their geolocated authors. We find that the share of transnational discursive linkages increased over the course of the pandemic, indicating effects of adaptation and learning. However, stringent political measures of crisis management at the domestic level (such as lockdown decisions) caused stronger national structuration of COVID-19 related Twitter discourse.

Dynamic topic modeling of twitter data during the COVID-19 pandemic

In an effort to gauge the global pandemic's impact on social thoughts and behavior, it is important to answer the following questions: (1) What kinds of topics are individuals and groups vocalizing in relation to the pandemic? (2) Are there any noticeable topic trends and if so how do these topics change over time and in response to major events? In this paper, through the advanced Sequential Latent Dirichlet Allocation model, we identified twelve of the most popular topics present in a Twitter dataset collected over the period spanning April 3rd to April 13th, 2020 in the United States and discussed their growth and changes over time. These topics were both robust, in that they covered specific domains, not simply events, and dynamic, in that they were able to change over time in response to rising trends in our dataset. They spanned politics, healthcare, community, and the economy, and experienced macro-level growth over time, while also exhibiting micro-level changes in topic composition. Our approach differentiated itself in both scale and scope to study the emerging topics concerning COVID-19 at a scale that few works have been able to achieve. We contributed to the cross-sectional field of urban studies and big data. Whereas we are optimistic towards the future, we also understand that this is an unprecedented time that will have lasting impacts on individuals and society at large, impacting not only the economy or geo-politics, but human behavior and psychology. Therefore, in more ways than one, this research is just beginning to scratch the surface of what will be a concerted research effort into studying the history and repercussions of COVID-19.

The importance of social media users' responses in tackling digital COVID-19 misinformation in Africa

Objective

Digital technologies present both an opportunity and a threat for advancing public health. At a time of pandemic, social media has become a tool for the rapid spread of misinformation. Mitigating the impacts of misinformation is particularly acute across Africa, where WhatsApp and other forms of social media dominate, and where the dual threats of misinformation and COVID-19 threaten lives and livelihoods. Given the scale of the problem within Africa, we set out to understand (i) the potential harm that misinformation causes, (ii) the available evidence on how to mitigate that misinformation and (iii) how user responses to misinformation shape the potential for those mitigating strategies to reduce the risk of harm.

Methods

We undertook a multi-method study, combining a rapid review of the research evidence with a survey of WhatsApp users across Africa.

Results

We identified 87 studies for inclusion in our review and had 286 survey respondents from 17 African countries. Our findings show the considerable harms caused by public health misinformation in Africa and the lack of evidence for or against strategies to mitigate against such harms. Furthermore, they highlight how social media users' responses to public health misinformation can mitigate and exacerbate potential harms. Understanding the ways in which social media users respond to misinformation sheds light on potential mitigation strategies.

Conclusions

Public health practitioners who utilise digital health approaches must not underestimate the importance of considering the role of social media in the circulation of misinformation, nor of the responses of social media users in shaping attempts to mitigate against the harms of such misinformation.

Perceptions of blame on social media during the coronavirus pandemic

The outbreak of the coronavirus (COVID-19) disease is overwhelming resources, economies and countries around the world. Millions of people have been infected and hundreds of thousands have succumbed to the virus. Research regarding the coronavirus pandemic is published every day. However, there is limited discourse regarding societal perception. Thus, this paper examines blame attribution concerning the origin and propagation of the coronavirus crisis according to public perception. Specifically, data were extracted from the social media platform Twitter concerning the coronavirus during the early stages of the outbreak and further investigated using thematic analysis. The findings revealed the public predominantly blames national governments for the coronavirus pandemic. In addition, the results documented the explosion of conspiracy theories among social media users regarding the virus' origin. In the early stages of the pandemic, the blame tendency was most frequent to conspiracy theories and restriction of information from the government, whilst in the later months, responsibility had shifted to political leaders and the media. The findings indicate an emerging government mistrust that may result in disregard of preventive health behaviours and the amplification of conspiracy theories, and an evolving dynamic of blame. This study argues for a transparent, continuing dialogue between governments and the public to stop the spread of the coronavirus.

Information extraction using a mixed method analysis of social media data: A case study of the police shooting during the anti-Sterlite protests at Thoothukudi, India

During unexpected social events, information extracted from social media content posted by the people could play a crucial role in understanding the public opinion about the event. In this study, a mixed method procedure, which combines automated and human-based methods, is proposed to mine information from tweets to understand people's thoughts toward an unexpected turn of events. The proposed framework was applied on tweets posted regarding the police shooting to disperse protesters during the anti-Sterlite protests on May 22, 2018, at Thoothukudi in the Indian state of Tamil Nadu. The tweets were analyzed in two ways: (i) sentiment classification with automated computational methods and (ii) qualitatively examining the context of the expressed sentiments. In the case of anti-Sterlite protests, people expressed mixed emotions toward the protests for the closure of the Sterlite plant. A large negative sentiment toward the police shooting could be gleaned from the tweets. Analyzing tweets by the proposed method provides clear insights regarding the incident, which in turn will aid in planning an emergency response.

Public perception of COVID-19 vaccines from the digital footprints left on Twitter: analyzing positive, neutral and negative sentiments of Twitterati

Purpose

Twitter is gaining popularity as a microblogging and social networking service to discuss various social issues. Coronavirus disease 2019 (COVID-19) has become a global pandemic and is discussed worldwide. Social media is an instant platform to deliberate various dimensions of COVID-19. The purpose of the study is to explore and analyze the public sentiments related to COVID-19 vaccines across the Twitter messages (positive, neutral, and negative) and the impact tweets make across digital social circles.

Design/methodology/approach

To fetch the vaccine-related posts, a manual examination of randomly selected 500 tweets was carried out to identify the popular hashtags relevant to the vaccine conversation. It was found that the hashtags “covid19vaccine” and “coronavirusvaccine” were the two popular hashtags used to discuss the communications related to COVID-19 vaccines. 23,575 global tweets available in public domain were retrieved through “Twitter Application Programming Interface” (API), using “Orange Software”, an open-source machine learning, data visualization and data mining toolkit. The study was confined to the tweets posted in English language only. The default data cleaning and preprocessing techniques available in the “Orange Software” were applied to the dataset, which include “transformation”, “tokenization” and “filtering”. The “Valence Aware Dictionary for sEntiment Reasoning” (VADER) tool was used for classification of tweets to determine the tweet sentiments (positive, neutral and negative) as well as the degree of sentiments (compound score also known as sentiment score). To assess the influence/impact of tweets account wise (verified and unverified) and sentiment wise (positive, neutral, and negative), the retweets and likes, which offer a sort of reward or acknowledgment of tweets, were used.

Findings

A gradual decline in the number of tweets over the time is observed. Majority (11,205; 47.52%) of tweets express positive sentiments, followed by neutral (7,948; 33.71%) and negative sentiments (4,422; 18.75%), respectively. The study also signifies a substantial difference between the impact of tweets tweeted by verified and unverified users. The tweets related to verified users have a higher impact both in terms of retweets (65.91%) and likes (84.62%) compared to the tweets tweeted by unverified users. Tweets expressing positive sentiments have the highest impact both in terms of likes (mean = 10.48) and retweets (mean = 3.07) compared to those that express neutral or negative sentiments.

Research limitations/implications

The main limitation of the study is that the sentiments of the people expressed over one single social platform, that is, Twitter have been studied which cannot generalize the global public perceptions. There can be a variation in the results when the datasets from other social media platforms will be studied.

Practical implications

The study will help to know the people's sentiments and beliefs toward the COVID-19 vaccines. Sentiments that people hold about the COVID-19 vaccines are studied, which will help health policymakers understand the polarity (positive, negative, and neutral) of the tweets and thus see the public reaction and reflect the types of information people are exposed to about vaccines. The study can aid the health sectors to intensify positive messages and eliminate negative messages for an enhanced vaccination uptake. The research can also help design more operative vaccine-advocating communication by customizing messages using the obtained knowledge from the sentiments and opinions about the vaccines.

Originality/value

The paper focuses on an essential aspect of COVID-19 vaccines and how people express themselves (positively, neutrally and negatively) on Twitter.

Forecasting mental health and emotions based on social media expressions during the COVID-19 pandemic

Purpose

The purpose of this paper is to present an approach for forecasting mental health conditions and emotions of a given population during the COVID-19 pandemic in Argentina based on social media contents.

Design/methodology/approach

Mental health conditions and emotions are captured via markers, which link social media contents with lexicons. First, the authors build time series models that describe the evolution of markers and their correlation with crisis events. Second, the authors use the time series for forecasting markers and identifying high prevalence points for the estimated markers.

Findings

The authors evaluated different forecasting strategies that yielded different performance and capabilities. In the best scenario, high prevalence periods of emotions and mental health issues can be satisfactorily predicted with a neural network strategy, even at early stages of a crisis (e.g. a training period of seven days).

Practical implications

This work contributes to a better understanding of how psychological processes related to crises manifest in social media, and this is a valuable asset for the design, implementation and monitoring of health prevention and communication policies.

Originality/value

Although there have been previous efforts to predict mental states of individuals, the analysis of mental health at the collective level has received scarce attention. The authors take a step forward by proposing a forecasting approach for analyzing the mental health of a given population at a larger scale.

Fighting a war without weapons? Lessons from the COVID-19 outbreak

The recent pandemic of COVID-19 revealed that a highly transmissible virus threatens all humanity because extensive mobility, migration, and millions of passengers who travel worldwide shape our globalized environment and make containment of a virus more difficult. In a war between humans and viruses, we should have the necessary weapons, such as masks, gloves, ventilators, and so forth. However, during the COVID-19 outbreak, there was a shortage of this basic medical and personal protective equipment (MAPPE), even for the health workers. This note focuses on this issue and suggests that a global organization, which stores and renews basic MAPPE would be beneficial in the fight against the next pandemic and that such an organization can be established without significantly increasing the public expenses of the countries.

How do social media and individual behaviors affect epidemic transmission and control?

In the outbreak of infectious diseases such as COVID-19, social media channels are important tools for the public to obtain information and form their opinions on infection risk, which can affect their disease prevention behaviors and the consequent disease transmission processes. However, there has been a lack of theoretical investigation into how social media and human behaviors jointly affect the spread of infectious diseases. In this study, we develop an agent-based modeling framework that couples (1) a general opinion dynamics model that describes how individuals form their opinions on epidemic risk with various information sources, (2) a behavioral adoption model that simulates the adoption of disease prevention behaviors, and (3) an epidemiological SEIR model that simulates the spread of diseases in a host population. Through simulating the spread of a coronavirus-like disease in a hypothetical residential area, the modeling results show that social media can make a community more sensitive to external drivers. Social media can increase the public's awareness of infection risk, which is beneficial for epidemic containment, when high-quality epidemic information exists at the early stage of pandemics. However, fabricated and fake news on social media, after a "latent period", can lead to a significant increase in infection rate. The modeling results provide scientific evidence for the intricate interplay between social media and human behaviors in epidemic dynamics and control, and highlight the importance of public education to promote behavioral changes and the need to correct misinformation and fake news on social media in a timely manner.

The COVID-19 pandemic and war

Could there be a symbiotic relationship between COVID-19 and conflict? On the one hand, circumstances associated with armed conflicts may give rise to greater spread of the virus, while, on the other hand, the COVID-19 pandemic may create conditions for violence through heightened xenophobia and nationalism or may change the dynamics of existing conflicts. We illustrate this with the example of war in the South Caucasus, one of the hot spots of the pandemic. Elsewhere, COVID-19 may have reduced the intensity of conflicts in some places, but it also may have contributed to anti-government protests and communal violence. We call for greater emphasis on traditional public health measures in unstable settings coupled with actions to hasten the peaceful resolution of ongoing conflicts.

Public Perspectives on Anti-Diabetic Drugs: Exploratory Analysis of Twitter Posts

BACKGROUND

Diabetes mellitus is a major global public health issue where self-management is critical to reducing disease burden. Social media has been a powerful tool to understand public perceptions. Public perception of the drugs used for the treatment of diabetes may be useful for orienting interventions to increase adherence.

OBJECTIVE

The aim of this study was to explore the public perceptions of anti-diabetic drugs through the analysis of health-related tweets mentioning such medications.

METHODS

This study uses an infoveillance social listening approach to monitor public discourse using Twitter data. We coded 4000 tweets from January 1, 2019 to October 1, 2019 containing key terms related to anti-diabetic drugs by using qualitative content analysis. Tweets were coded for whether they were truly about an anti-diabetic drug and whether they were health-related. Health-related tweets were further coded based on who was tweeting, which anti-diabetic drug was being tweeted about, and the content discussed in the tweet. The main outcome of the analysis was the themes identified by analyzing the content of health-related tweets on anti-diabetic drugs.

RESULTS

We identified 1664 health-related tweets on 33 anti-diabetic drugs. A quarter (415/1664) of the tweets were confirmed to have been from people with diabetes, 17.9% (298/1664) from people posting about someone else, and 2.7% (45/1664) from health care professionals. However, the role of the tweeter was unidentifiable in two-thirds of the tweets. We identified 13 themes, with the health consequences of the cost of anti-diabetic drugs being the most extensively discussed, followed by the efficacy and availability. We also identified issues that patients may conceal from health care professionals, such as purchasing medications from unofficial sources.

CONCLUSIONS

This study uses an infoveillance approach using Twitter data to explore public perceptions related to anti-diabetic drugs. This analysis gives an insight into the real-life issues that an individual faces when taking anti-diabetic drugs, and such findings may be incorporated into health policies to improve compliance and efficacy. This study suggests that there is a fear of not having access to anti-diabetic drugs due to cost or physical availability and highlights the impact of the sacrifices made to access anti-diabetic drugs. Along with screening for diabetes-related health issues, health care professionals should also ask their patients about any non-health-related concerns regarding their anti-diabetic drugs. The positive tweets about dietary changes indicate that people with type 2 diabetes may be more open to self-management than what the health care professionals believe.

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.

Pandémie et communautés minoritaires marginalisées : vers une approche inclusive en santé publique?

The pandemic has highlighted the complexity of public health measures and their side effects, the intricacy of the underlying health, social and political phenomena at play as well as their specificity for marginalized minority communities. Based on this experience, this commentary suggests that it would be relevant in the future to develop consultation mechanisms that take into account the entire population, including marginalized minority groups, in order to better inform decision-making processes in times of pandemic. In a context of fluid crisis, any consultation process must take into account the limits of what is possible and mourn the comprehensiveness in favour of repeated iterations of dialogue, mediation and decision-making. This means creating conditions favourable to the consideration of the most vulnerable people and groups' voices by public authorities. In order to prepare for the next crises, it is necessary to develop strong links between public institutions and communities, both of majorities and minorities, in order to define the mechanisms favouring the emergence of truly inclusive public health, taking into account the physical, mental and social health of the population.

Communicative Blame in Online Communication of the COVID-19 Pandemic: Computational Approach of Stigmatizing Cues and Negative Sentiment Gauged With Automated Analytic Techniques

BACKGROUND

Information about a new coronavirus emerged in 2019 and rapidly spread around the world, gaining significant public attention and attracting negative bias. The use of stigmatizing language for the purpose of blaming sparked a debate.

OBJECTIVE

This study aims to identify social stigma and negative sentiment toward the blameworthy agents in social communities.

METHODS

We enabled a tailored text-mining platform to identify data in their natural settings by retrieving and filtering online sources, and constructed vocabularies and learning word representations from natural language processing for deductive analysis along with the research theme. The data sources comprised of ten news websites, eleven discussion forums, one social network, and two principal media sharing networks in Taiwan. A synthesis of news and social networking analytics was present from December 30, 2019, to March 31, 2020.

RESULTS

We collated over 1.07 million Chinese texts. Almost two-thirds of the texts on COVID-19 came from news services (n=683,887, 63.68%), followed by Facebook (n=297,823, 27.73%), discussion forums (n=62,119, 5.78%), and Instagram and YouTube (n=30,154, 2.81%). Our data showed that online news served as a hotbed for negativity and for driving emotional social posts. Online information regarding COVID-19 associated it with China-and a specific city within China through references to the "Wuhan pneumonia"-potentially encouraging xenophobia. The adoption of this problematic moniker had a high frequency, despite the World Health Organization guideline to avoid biased perceptions and ethnic discrimination. Social stigma is disclosed through negatively valenced responses, which are associated with the most blamed targets.

CONCLUSIONS

Our sample is sufficiently representative of a community because it contains a broad range of mainstream online media. Stigmatizing language linked to the COVID-19 pandemic shows a lack of civic responsibility that encourages bias, hostility, and discrimination. Frequently used stigmatizing terms were deemed offensive, and they might have contributed to recent backlashes against China by directing blame and encouraging xenophobia. The implications ranging from health risk communication to stigma mitigation and xenophobia concerns amid the COVID-19 outbreak are emphasized. Understanding the nomenclature and biased terms employed in relation to the COVID-19 outbreak is paramount. We propose solidarity with communication professionals in combating the COVID-19 outbreak and the infodemic. Finding solutions to curb the spread of virus bias, stigma, and discrimination is imperative.

Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approach

Internet forums and public social media, such as online healthcare forums, provide a convenient channel for users (people/patients) concerned about health issues to discuss and share information with each other. In late December 2019, an outbreak of a novel coronavirus (infection from which results in the disease named COVID-19) was reported, and, due to the rapid spread of the virus in other parts of the world, the World Health Organization declared a state of emergency. In this paper, we used automated extraction of COVID-19-related discussions from social media and a natural language process (NLP) method based on topic modeling to uncover various issues related to COVID-19 from public opinions. Moreover, we also investigate how to use LSTM recurrent neural network for sentiment classification of COVID-19 comments. Our findings shed light on the importance of using public opinions and suitable computational techniques to understand issues surrounding COVID-19 and to guide related decision-making. In addition, experiments demonstrated that the research model achieved an accuracy of 81.15% - a higher accuracy than that of several other well-known machine-learning algorithms for COVID-19-Sentiment Classification.

How the Media Places Responsibility for the COVID-19 Pandemic-An Australian Media Analysis

Global pandemics are likely to increase in frequency and severity, and media communication of key messages represents an important mediator of the behavior of individuals in response to public health countermeasures. Where the media places responsibility during a pandemic is therefore important to study as blame is commonly used as a tool to influence public behavior but can also lead to the subjective persecution of groups. The aim of this paper is to investigate where the media places responsibility for COVID-19 in Australia. Specifically, we identify the key themes and frames that are present and observe how they changed over the course of the COVID-19 pandemic in relation to government actions and progression of the pandemic. Understanding media representations of the COVID-19 pandemic will provide insights into ways in which responsibility is framed in relation to health action. Newspaper articles from the Australian and the Sydney Morning Herald were sampled between January 20 and March 31 2020 on every second Monday. Factiva was used to identify and download newspaper articles using the following search criteria: "COVID-19" OR coronavirus OR "Wuhan virus" OR "corona virus" OR "Hebei virus" OR "wet market" OR (Wuhan AND virus) OR (market AND Wuhan and virus) or (China AND Virus) or (Novel AND Virus). Articles were imported into Nvivo and thematic and framing analyses were used. The results show that framing of the pandemic was largely based on societal issues with the theme of economic disruption prevalent throughout the study time period. Moral evaluations of the pandemic were infrequent initially but increased co-incident with the first signs of "flattening of the curve." Explicit examples of blame were very rare but were commonly implied based on the causal origin of the virus. The Australian printed media were slow to report on the COVID-19 pandemic, in addition they were reluctant to apportion blame until the end of the study period, after confirmed case rates had begun to slow. This is interpreted as being due to an evaluation of the pandemic risks as low by the media and therefore the tools of othering and blame were not used until after the study period when the actual risks had begun to abate, more consistent with an inquiry than a mediating mechanism.

Public Engagement and Government Responsiveness in the Communications About COVID-19 During the Early Epidemic Stage in China: Infodemiology Study on Social Media Data

BACKGROUND

Effective risk communication about the outbreak of a newly emerging infectious disease in the early stage is critical for managing public anxiety and promoting behavioral compliance. China has experienced the unprecedented epidemic of the coronavirus disease (COVID-19) in an era when social media has fundamentally transformed information production and consumption patterns.

OBJECTIVE

This study examined public engagement and government responsiveness in the communications about COVID-19 during the early epidemic stage based on an analysis of data from Sina Weibo, a major social media platform in China.

METHODS

Weibo data relevant to COVID-19 from December 1, 2019, to January 31, 2020, were retrieved. Engagement data (likes, comments, shares, and followers) of posts from government agency accounts were extracted to evaluate public engagement with government posts online. Content analyses were conducted for a random subset of 644 posts from personal accounts of individuals, and 273 posts from 10 relatively more active government agency accounts and the National Health Commission of China to identify major thematic contents in online discussions. Latent class analysis further explored main content patterns, and chi-square for trend examined how proportions of main content patterns changed by time within the study time frame.

RESULTS

The public response to COVID-19 seemed to follow the spread of the disease and government actions but was earlier for Weibo than the government. Online users generally had low engagement with posts relevant to COVID-19 from government agency accounts. The common content patterns identified in personal and government posts included sharing epidemic situations; general knowledge of the new disease; and policies, guidelines, and official actions. However, personal posts were more likely to show empathy to affected people (χ²₁=13.3, P<.001), attribute blame to other individuals or government (χ²₁=28.9, P<.001), and express worry about the epidemic (χ²₁=32.1, P<.001), while government posts were more likely to share instrumental support (χ²₁=32.5, P<.001) and praise people or organizations (χ²₁=8.7, P=.003). As the epidemic evolved, sharing situation updates (for trend, χ²₁=19.7, P<.001) and policies, guidelines, and official actions (for trend, χ²₁=15.3, P<.001) became less frequent in personal posts but remained stable or increased significantly in government posts. Moreover, as the epidemic evolved, showing empathy and attributing blame (for trend, χ²₁=25.3, P<.001) became more frequent in personal posts, corresponding to a slight increase in sharing instrumental support, praising, and empathizing in government posts (for trend, χ²₁=9.0, P=.003).

CONCLUSIONS

The government should closely monitor social media data to improve the timing of communications about an epidemic. As the epidemic evolves, merely sharing situation updates and policies may be insufficient to capture public interest in the messages. The government may adopt a more empathic communication style as more people are affected by the disease to address public concerns.