Information Circulation in times of Ebola: Twitter and the Sexual Transmission of Ebola by Survivors

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.

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.

Prevalence of Health Misinformation on Social Media: Systematic Review

Background

Although at present there is broad agreement among researchers, health professionals, and policy makers on the need to control and combat health misinformation, the magnitude of this problem is still unknown. Consequently, it is fundamental to discover both the most prevalent health topics and the social media platforms from which these topics are initially framed and subsequently disseminated.

Objective

This systematic review aimed to identify the main health misinformation topics and their prevalence on different social media platforms, focusing on methodological quality and the diverse solutions that are being implemented to address this public health concern.

Methods

We searched PubMed, MEDLINE, Scopus, and Web of Science for articles published in English before March 2019, with a focus on the study of health misinformation in social media. We defined health misinformation as a health-related claim that is based on anecdotal evidence, false, or misleading owing to the lack of existing scientific knowledge. We included (1) articles that focused on health misinformation in social media, including those in which the authors discussed the consequences or purposes of health misinformation and (2) studies that described empirical findings regarding the measurement of health misinformation on these platforms.

Results

A total of 69 studies were identified as eligible, and they covered a wide range of health topics and social media platforms. The topics were articulated around the following six principal categories: vaccines (32%), drugs or smoking (22%), noncommunicable diseases (19%), pandemics (10%), eating disorders (9%), and medical treatments (7%). Studies were mainly based on the following five methodological approaches: social network analysis (28%), evaluating content (26%), evaluating quality (24%), content/text analysis (16%), and sentiment analysis (6%). Health misinformation was most prevalent in studies related to smoking products and drugs such as opioids and marijuana. Posts with misinformation reached 87% in some studies. Health misinformation about vaccines was also very common (43%), with the human papilloma virus vaccine being the most affected. Health misinformation related to diets or pro–eating disorder arguments were moderate in comparison to the aforementioned topics (36%). Studies focused on diseases (ie, noncommunicable diseases and pandemics) also reported moderate misinformation rates (40%), especially in the case of cancer. Finally, the lowest levels of health misinformation were related to medical treatments (30%).

Conclusions

The prevalence of health misinformation was the highest on Twitter and on issues related to smoking products and drugs. However, misinformation on major public health issues, such as vaccines and diseases, was also high. Our study offers a comprehensive characterization of the dominant health misinformation topics and a comprehensive description of their prevalence on different social media platforms, which can guide future studies and help in the development of evidence-based digital policy action plans.

Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA

Twitter is a social media platform with more than 500 million users worldwide. It has become a tool for spreading the news, discussing ideas and comments on world events. Twitter is also an important source of health-related information, given the amount of news, opinions and information that is shared by both citizens and official sources. It is a challenge identifying interesting and useful content from large text-streams in different languages, few works have explored languages other than English. In this paper, we use topic identification and sentiment analysis to explore a large number of tweets in both countries with a high number of spreading and deaths by COVID-19, Brazil, and the USA. We employ 3,332,565 tweets in English and 3,155,277 tweets in Portuguese to compare and discuss the effectiveness of topic identification and sentiment analysis in both languages. We ranked ten topics and analyzed the content discussed on Twitter for four months providing an assessment of the discourse evolution over time. The topics we identified were representative of the news outlets during April and August in both countries. We contribute to the study of the Portuguese language, to the analysis of sentiment trends over a long period and their relation to announced news, and the comparison of the human behavior in two different geographical locations affected by this pandemic. It is important to understand public reactions, information dissemination and consensus building in all major forms, including social media in different countries.

A thematic analysis of highly retweeted early COVID-19 tweets: consensus, information, dissent and lockdown life

Purpose

Public attitudes towards COVID-19 and social distancing are critical in reducing its spread. It is therefore important to understand public reactions and information dissemination in all major forms, including on social media. This article investigates important issues reflected on Twitter in the early stages of the public reaction to COVID-19.

Design/methodology/approach

A thematic analysis of the most retweeted English-language tweets mentioning COVID-19 during March 10–29, 2020.

Findings

The main themes identified for the 87 qualifying tweets accounting for 14 million retweets were: lockdown life; attitude towards social restrictions; politics; safety messages; people with COVID-19; support for key workers; work; and COVID-19 facts/news.

Research limitations/implications

Twitter played many positive roles, mainly through unofficial tweets. Users shared social distancing information, helped build support for social distancing, criticised government responses, expressed support for key workers and helped each other cope with social isolation. A few popular tweets not supporting social distancing show that government messages sometimes failed.

Practical implications

Public health campaigns in future may consider encouraging grass roots social web activity to support campaign goals. At a methodological level, analysing retweet counts emphasised politics and ignored practical implementation issues.

Originality/value

This is the first qualitative analysis of general COVID-19-related retweeting.

Digital Health Strategies to Fight COVID-19 Worldwide: Challenges, Recommendations, and a Call for Papers

The coronavirus disease (COVID-19) pandemic has created an urgent need for coordinated mechanisms to respond to the outbreak across health sectors, and digital health solutions have been identified as promising approaches to address this challenge. This editorial discusses the current situation regarding digital health solutions to fight COVID-19 as well as the challenges and ethical hurdles to broad and long-term implementation of these solutions. To decrease the risk of infection, telemedicine has been used as a successful health care model in both emergency and primary care. Official communication plans should promote facile and diverse channels to inform people about the pandemic and to avoid rumors and reduce threats to public health. Social media platforms such as Twitter and Google Trends analyses are highly beneficial to model pandemic trends as well as to monitor the evolution of patients' symptoms or public reaction to the pandemic over time. However, acceptability of digital solutions may face challenges due to potential conflicts with users' cultural, moral, and religious backgrounds. Digital tools can provide collective public health benefits; however, they may be intrusive and can erode individual freedoms or leave vulnerable populations behind. The COVID-19 pandemic has demonstrated the strong potential of various digital health solutions that have been tested during the crisis. More concerted measures should be implemented to ensure that future digital health initiatives will have a greater impact on the epidemic and meet the most strategic needs to ease the life of people who are at the forefront of the crisis.

Evaluating the Effectiveness of the Standard Insights Extraction Pipeline for Bantu Languages

Extracting insights from data obtained from the web in order to identify people’s views and opinions on various topics is a growing practice. The standard insights extraction pipeline is typically an unsupervised machine learning task composed of processes that preprocess the text, visualize it, cluster and identify the topics and sentiment in each cluster, and then graph the network. Given the increasing amount of data being generated on the internet in Africa today, and the multilingual state of African countries, we evaluated how well the standard pipeline works when applied to text wholly or partially written in indigenous African languages, specifically Bantu languages. We carried out an exploratory investigation using Twitter data and compared the outputs from each step of the pipeline for an English dataset and a mixed Bantu language dataset. We found that for Bantu languages, due to their complex grammatical structure, extra preprocessing steps such as part-of-speech tagging and morphological analysis are required during data cleaning, threshold values should be adjusted during topic modeling, and semantic analysis should be performed before completing text preprocessing.