Monitoring Information Flow on Coronavirus Disease 2019 (COVID-19)

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Abstract

The flow of information on Coronavirus Disease 2019 (COVID-19) is intensifying, requiring concerted efforts of all scholars. Peer-reviewed journals as established channels of scientific communications are struggling to keep up with unprecedented high submission rates. Preprint servers are becoming increasingly popular among researchers and authors who set priority over their ideas and research data by pre-publication archiving of their manuscripts on these professional platforms. Most published articles on COVID-19 are now archived by the PubMed Central repository and available for searches on LitCovid, which is a newly designed hub for specialist searches on the subject. Social media platforms are also gaining momentum as channels for rapid dissemination of COVID-19 information. Monitoring, evaluating and filtering information flow through the established and emerging scholarly platforms may improve the situation with the pandemic and save lives.

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Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study

Alaa A Abd-alrazaq, Dari Alhuwail, Mowafa S. Househ … (2020)

Background The recent coronavirus disease (COVID-19) pandemic is taking a toll on the world’s health care infrastructure as well as the social, economic, and psychological well-being of humanity. Individuals, organizations, and governments are using social media to communicate with each other on a number of issues relating to the COVID-19 pandemic. Not much is known about the topics being shared on social media platforms relating to COVID-19. Analyzing such information can help policy makers and health care organizations assess the needs of their stakeholders and address them appropriately. Objective This study aims to identify the main topics posted by Twitter users related to the COVID-19 pandemic. Methods Leveraging a set of tools (Twitter’s search application programming interface (API), Tweepy Python library, and PostgreSQL database) and using a set of predefined search terms (“corona,” “2019-nCov,” and “COVID-19”), we extracted the text and metadata (number of likes and retweets, and user profile information including the number of followers) of public English language tweets from February 2, 2020, to March 15, 2020. We analyzed the collected tweets using word frequencies of single (unigrams) and double words (bigrams). We leveraged latent Dirichlet allocation for topic modeling to identify topics discussed in the tweets. We also performed sentiment analysis and extracted the mean number of retweets, likes, and followers for each topic and calculated the interaction rate per topic. Results Out of approximately 2.8 million tweets included, 167,073 unique tweets from 160,829 unique users met the inclusion criteria. Our analysis identified 12 topics, which were grouped into four main themes: origin of the virus; its sources; its impact on people, countries, and the economy; and ways of mitigating the risk of infection. The mean sentiment was positive for 10 topics and negative for 2 topics (deaths caused by COVID-19 and increased racism). The mean for tweet topics of account followers ranged from 2722 (increased racism) to 13,413 (economic losses). The highest mean of likes for the tweets was 15.4 (economic loss), while the lowest was 3.94 (travel bans and warnings). Conclusions Public health crisis response activities on the ground and online are becoming increasingly simultaneous and intertwined. Social media provides an opportunity to directly communicate health information to the public. Health systems should work on building national and international disease detection and surveillance systems through monitoring social media. There is also a need for a more proactive and agile public health presence on social media to combat the spread of fake news.

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COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data

Wasim Ahmed, Josep Vidal-Alaball, Joseph Downing … (2020)

Background Since the beginning of December 2019, the coronavirus disease (COVID-19) has spread rapidly around the world, which has led to increased discussions across online platforms. These conversations have also included various conspiracies shared by social media users. Amongst them, a popular theory has linked 5G to the spread of COVID-19, leading to misinformation and the burning of 5G towers in the United Kingdom. The understanding of the drivers of fake news and quick policies oriented to isolate and rebate misinformation are keys to combating it. Objective The aim of this study is to develop an understanding of the drivers of the 5G COVID-19 conspiracy theory and strategies to deal with such misinformation. Methods This paper performs a social network analysis and content analysis of Twitter data from a 7-day period (Friday, March 27, 2020, to Saturday, April 4, 2020) in which the #5GCoronavirus hashtag was trending on Twitter in the United Kingdom. Influential users were analyzed through social network graph clusters. The size of the nodes were ranked by their betweenness centrality score, and the graph’s vertices were grouped by cluster using the Clauset-Newman-Moore algorithm. The topics and web sources used were also examined. Results Social network analysis identified that the two largest network structures consisted of an isolates group and a broadcast group. The analysis also revealed that there was a lack of an authority figure who was actively combating such misinformation. Content analysis revealed that, of 233 sample tweets, 34.8% (n=81) contained views that 5G and COVID-19 were linked, 32.2% (n=75) denounced the conspiracy theory, and 33.0% (n=77) were general tweets not expressing any personal views or opinions. Thus, 65.2% (n=152) of tweets derived from nonconspiracy theory supporters, which suggests that, although the topic attracted high volume, only a handful of users genuinely believed the conspiracy. This paper also shows that fake news websites were the most popular web source shared by users; although, YouTube videos were also shared. The study also identified an account whose sole aim was to spread the conspiracy theory on Twitter. Conclusions The combination of quick and targeted interventions oriented to delegitimize the sources of fake information is key to reducing their impact. Those users voicing their views against the conspiracy theory, link baiting, or sharing humorous tweets inadvertently raised the profile of the topic, suggesting that policymakers should insist in the efforts of isolating opinions that are based on fake news. Many social media platforms provide users with the ability to report inappropriate content, which should be used. This study is the first to analyze the 5G conspiracy theory in the context of COVID-19 on Twitter offering practical guidance to health authorities in how, in the context of a pandemic, rumors may be combated in the future.

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YouTube as a source of information on COVID-19: a pandemic of misinformation?

Heidi Li, Adrian R. Bailey, David Huynh … (2020)

Introduction The COVID-19 pandemic is this century’s largest public health emergency and its successful management relies on the effective dissemination of factual information. As a social media platform with billions of daily views, YouTube has tremendous potential to both support and hinder public health efforts. However, the usefulness and accuracy of most viewed YouTube videos on COVID-19 have not been investigated. Methods A YouTube search was performed on 21 March 2020 using keywords ‘coronavirus’ and ‘COVID-19’, and the top 75 viewed videos from each search were analysed. Videos that were duplicates, non-English, non-audio and non-visual, exceeding 1 hour in duration, live and unrelated to COVID-19 were excluded. Two reviewers coded the source, content and characteristics of included videos. The primary outcome was usability and reliability of videos, analysed using the novel COVID-19 Specific Score (CSS), modified DISCERN (mDISCERN) and modified JAMA (mJAMA) scores. Results Of 150 videos screened, 69 (46%) were included, totalling 257 804 146 views. Nineteen (27.5%) videos contained non-factual information, totalling 62 042 609 views. Government and professional videos contained only factual information and had higher CSS than consumer videos (mean difference (MD) 2.21, 95% CI 0.10 to 4.32, p=0.037); mDISCERN scores than consumer videos (MD 2.46, 95% CI 0.50 to 4.42, p=0.008), internet news videos (MD 2.20, 95% CI 0.19 to 4.21, p=0.027) and entertainment news videos (MD 2.57, 95% CI 0.66 to 4.49, p=0.004); and mJAMA scores than entertainment news videos (MD 1.21, 95% CI 0.07 to 2.36, p=0.033) and consumer videos (MD 1.27, 95% CI 0.10 to 2.44, p=0.028). However, they only accounted for 11% of videos and 10% of views. Conclusion Over one-quarter of the most viewed YouTube videos on COVID-19 contained misleading information, reaching millions of viewers worldwide. As the current COVID-19 pandemic worsens, public health agencies must better use YouTube to deliver timely and accurate information and to minimise the spread of misinformation. This may play a significant role in successfully managing the COVID-19 pandemic.

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Author and article information

Journal

Journal ID (nlm-ta): Mediterr J Rheumatol

Journal ID (iso-abbrev): Mediterr J Rheumatol

Journal ID (publisher-id): MJR

Title: Mediterranean Journal of Rheumatology

Publisher: The Mediterranean Journal of Rheumatology (MJR)

ISSN (Electronic): 2529-198X

Publication date Collection: September 2020

Publication date (Electronic): 08 September 2020

Volume: 31

Issue: Suppl 2

Pages: 243-246

Affiliations

[1 ]Departments of Rheumatology and Research and Development, Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK), Russells Hall Hospital, Dudley, West Midlands, United Kingdom,

[2 ]Department of Internal Medicine #2, Danylo Halytsky Lviv National Medical University, Lviv, Ukraine,

[3 ]Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, India

[4 ]Arthritis Research UK Epidemiology Unit, University of Manchester, Manchester, United Kingdom

Author notes

Corresponding Author: Armen Yuri Gasparyan, MD, PhD Associate Professor of MedicineDepartments of Rheumatology and Research and Development Dudley Group NHS Foundation Trust (Teaching Trust of the University of Birmingham, UK) Russells Hall Hospital Pensnett Road, Dudley DY1 2HQ, West Midlands, United Kingdom, E-mail: a.gasparyan@ 123456gmail.com

Author information

Armen Yuri Gasparyan https://orcid.org/0000-0001-8749-6018

Olena Zimba https://orcid.org/0000-0002-4188-8486

Durga Prasanna Misra https://orcid.org/0000-0002-5035-7396

George D. Kitas https://orcid.org/0000-0002-0828-6176

Article

Publisher ID: MJR-31-Suppl-2-243

DOI: 10.31138/mjr.31.3.243

PMC ID: 7656128

SO-VID: 122c161b-7390-4049-b474-60dc240dda62

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This work is licensed under and Creative Commons Attribution-NonCommercial 4.0 International License.

Monitoring Information Flow on Coronavirus Disease 2019 (COVID-19)

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Novel Coronavirus Disease COVID-19

Most cited references 20

Top Concerns of Tweeters During the COVID-19 Pandemic: Infoveillance Study

COVID-19 and the 5G Conspiracy Theory: Social Network Analysis of Twitter Data

YouTube as a source of information on COVID-19: a pandemic of misinformation?

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