“Thought I’d Share First” and Other Conspiracy Theory Tweets from the COVID-19 Infodemic:
      Exploratory Study

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Abstract

Background

The COVID-19 outbreak has left many people isolated within their homes; these people are turning to social media for news and social connection, which leaves them vulnerable to believing and sharing misinformation. Health-related misinformation threatens adherence to public health messaging, and monitoring its spread on social media is critical to understanding the evolution of ideas that have potentially negative public health impacts.

Objective

The aim of this study is to use Twitter data to explore methods to characterize and classify four COVID-19 conspiracy theories and to provide context for each of these conspiracy theories through the first 5 months of the pandemic.

Methods

We began with a corpus of COVID-19 tweets (approximately 120 million) spanning late January to early May 2020. We first filtered tweets using regular expressions (n=1.8 million) and used random forest classification models to identify tweets related to four conspiracy theories. Our classified data sets were then used in downstream sentiment analysis and dynamic topic modeling to characterize the linguistic features of COVID-19 conspiracy theories as they evolve over time.

Results

Analysis using model-labeled data was beneficial for increasing the proportion of data matching misinformation indicators. Random forest classifier metrics varied across the four conspiracy theories considered (F1 scores between 0.347 and 0.857); this performance increased as the given conspiracy theory was more narrowly defined. We showed that misinformation tweets demonstrate more negative sentiment when compared to nonmisinformation tweets and that theories evolve over time, incorporating details from unrelated conspiracy theories as well as real-world events.

Conclusions

Although we focus here on health-related misinformation, this combination of approaches is not specific to public health and is valuable for characterizing misinformation in general, which is an important first step in creating targeted messaging to counteract its spread. Initial messaging should aim to preempt generalized misinformation before it becomes widespread, while later messaging will need to target evolving conspiracy theories and the new facets of each as they become incorporated.

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Most cited references 65

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Systematic Literature Review on the Spread of Health-related Misinformation on Social Media

Yuxi Wang, Martin Mckee, Aleksandra M. Torbica … (2019)

Contemporary commentators describe the current period as “an era of fake news” in which misinformation, generated intentionally or unintentionally, spreads rapidly. Although affecting all areas of life, it poses particular problems in the health arena, where it can delay or prevent effective care, in some cases threatening the lives of individuals. While examples of the rapid spread of misinformation date back to the earliest days of scientific medicine, the internet, by allowing instantaneous communication and powerful amplification has brought about a quantum change. In democracies where ideas compete in the marketplace for attention, accurate scientific information, which may be difficult to comprehend and even dull, is easily crowded out by sensationalized news. In order to uncover the current evidence and better understand the mechanism of misinformation spread, we report a systematic review of the nature and potential drivers of health-related misinformation. We searched PubMed, Cochrane, Web of Science, Scopus and Google databases to identify relevant methodological and empirical articles published between 2012 and 2018. A total of 57 articles were included for full-text analysis. Overall, we observe an increasing trend in published articles on health-related misinformation and the role of social media in its propagation. The most extensively studied topics involving misinformation relate to vaccination, Ebola and Zika Virus, although others, such as nutrition, cancer, fluoridation of water and smoking also featured. Studies adopted theoretical frameworks from psychology and network science, while co-citation analysis revealed potential for greater collaboration across fields. Most studies employed content analysis, social network analysis or experiments, drawing on disparate disciplinary paradigms. Future research should examine susceptibility of different sociodemographic groups to misinformation and understand the role of belief systems on the intention to spread misinformation. Further interdisciplinary research is also warranted to identify effective and tailored interventions to counter the spread of health-related misinformation online.

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Susceptibility to misinformation about COVID-19 around the world

Jon Roozenbeek, Claudia R. Schneider, Sarah Dryhurst … (2020)

Misinformation about COVID-19 is a major threat to public health. Using five national samples from the UK (n = 1050 and n = 1150), Ireland (n = 700), the USA (n = 700), Spain (n = 700) and Mexico (n = 700), we examine predictors of belief in the most common statements about the virus that contain misinformation. We also investigate the prevalence of belief in COVID-19 misinformation across different countries and the role of belief in such misinformation in predicting relevant health behaviours. We find that while public belief in misinformation about COVID-19 is not particularly common, a substantial proportion views this type of misinformation as highly reliable in each country surveyed. In addition, a small group of participants find common factual information about the virus highly unreliable. We also find that increased susceptibility to misinformation negatively affects people's self-reported compliance with public health guidance about COVID-19, as well as people's willingness to get vaccinated against the virus and to recommend the vaccine to vulnerable friends and family. Across all countries surveyed, we find that higher trust in scientists and having higher numeracy skills were associated with lower susceptibility to coronavirus-related misinformation. Taken together, these results demonstrate a clear link between susceptibility to misinformation and both vaccine hesitancy and a reduced likelihood to comply with health guidance measures, and suggest that interventions which aim to improve critical thinking and trust in science may be a promising avenue for future research.

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

Ramez Kouzy, Joseph Abi Jaoude, Afif Kraitem … (2020)

Background Since the beginning of the coronavirus disease 2019 (COVID-19) epidemic, misinformation has been spreading uninhibited over traditional and social media at a rapid pace. We sought to analyze the magnitude of misinformation that is being spread on Twitter (Twitter, Inc., San Francisco, CA) regarding the coronavirus epidemic. Materials and methods We conducted a search on Twitter using 14 different trending hashtags and keywords related to the COVID-19 epidemic. We then summarized and assessed individual tweets for misinformation in comparison to verified and peer-reviewed resources. Descriptive statistics were used to compare terms and hashtags, and to identify individual tweets and account characteristics. Results The study included 673 tweets. Most tweets were posted by informal individuals/groups (66%), and 129 (19.2%) belonged to verified Twitter accounts. The majority of included tweets contained serious content (91.2%); 548 tweets (81.4%) included genuine information pertaining to the COVID-19 epidemic. Around 70% of the tweets tackled medical/public health information, while the others were pertaining to sociopolitical and financial factors. In total, 153 tweets (24.8%) included misinformation, and 107 (17.4%) included unverifiable information regarding the COVID-19 epidemic. The rate of misinformation was higher among informal individual/group accounts (33.8%, p: <0.001). Tweets from unverified Twitter accounts contained more misinformation (31.0% vs 12.6% for verified accounts, p: <0.001). Tweets from healthcare/public health accounts had the lowest rate of unverifiable information (12.3%, p: 0.04). The number of likes and retweets per tweet was not associated with a difference in either false or unverifiable content. The keyword “COVID-19” had the lowest rate of misinformation and unverifiable information, while the keywords “#2019_ncov” and “Corona” were associated with the highest amount of misinformation and unverifiable content respectively. Conclusions Medical misinformation and unverifiable content pertaining to the global COVID-19 epidemic are being propagated at an alarming rate on social media. We provide an early quantification of the magnitude of misinformation spread and highlight the importance of early interventions in order to curb this phenomenon that endangers public safety at a time when awareness and appropriate preventive actions are paramount.

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Author and article information

Contributors

Ashlynn R Daughton:

ORCID: https://orcid.org/0000-0003-1694-3268

Analytics, Intelligence, and Technology DivisionLos Alamos National LaboratoryP.O. Box 1663Los Alamos, NM, 87545United States1 505 664 0062adaughton@lanl.gov

Journal

Journal ID (nlm-ta): JMIR Public Health Surveill

Journal ID (iso-abbrev): JMIR Public Health Surveill

Journal ID (publisher-id): JPH

Title: JMIR Public Health and Surveillance

Publisher: JMIR Publications (Toronto, Canada )

ISSN (Electronic): 2369-2960

Publication date Collection: April 2021

Publication date (Electronic): 14 April 2021

Publication date PMC-release: 14 April 2021

Volume: 7

Issue: 4

Electronic Location Identifier: e26527

Affiliations

[1 ] Analytics, Intelligence, and Technology Division Los Alamos National Laboratory Los Alamos, NM United States

[2 ] Department of Computer Science University of New Mexico Albuquerque, NM United States

Author notes

Corresponding Author: Ashlynn R Daughton adaughton@ 123456lanl.gov

Author information

Dax Gerts https://orcid.org/0000-0001-9445-9573

Courtney D Shelley https://orcid.org/0000-0002-5497-7453

Nidhi Parikh https://orcid.org/0000-0002-5707-6124

Travis Pitts https://orcid.org/0000-0002-9381-9423

Chrysm Watson Ross https://orcid.org/0000-0002-5989-3830

Geoffrey Fairchild https://orcid.org/0000-0001-5500-8120

Nidia Yadria Vaquera Chavez https://orcid.org/0000-0002-4382-5710

Ashlynn R Daughton https://orcid.org/0000-0003-1694-3268

Article

Publisher ID: v7i4e26527

DOI: 10.2196/26527

PMC ID: 8048710

PubMed ID: 33764882

SO-VID: 8eb7561b-e744-48b9-b0b0-214c22ef8eb6

Copyright © ©Dax Gerts, Courtney D Shelley, Nidhi Parikh, Travis Pitts, Chrysm Watson Ross, Geoffrey Fairchild, Nidia Yadria Vaquera Chavez, Ashlynn R Daughton. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 14.04.2021.

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on http://publichealth.jmir.org, as well as this copyright and license information must be included.

History

Date received : 15 December 2020

Date revision requested : 27 January 2021

Date revision received : 17 February 2021

Date accepted : 19 March 2021

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