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"Thought I'd Share First" and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study.
JMIR Public Health Surveill. 2021 04 14; 7(4):e26527.JP

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.

Links

Authors+Show Affiliations

Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, United States.

Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, United States.

Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, United States.

Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, United States.

Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, United States. Department of Computer Science, University of New Mexico, Albuquerque, NM, United States.

Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, United States.

Vaquera Chavez NY

Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, United States. Department of Computer Science, University of New Mexico, Albuquerque, NM, United States.

Analytics, Intelligence, and Technology Division, Los Alamos National Laboratory, Los Alamos, NM, United States.

MeSH

COVID-19CommunicationHumansInformation DisseminationSocial Media

Pub Type(s)

Journal Article

Research Support, Non-U.S. Gov't

Research Support, U.S. Gov't, Non-P.H.S.

Language

eng

PubMed ID

33764882

Citation

Gerts, Dax, et al. ""Thought I'd Share First" and Other Conspiracy Theory Tweets From the COVID-19 Infodemic: Exploratory Study." JMIR Public Health and Surveillance, vol. 7, no. 4, 2021, pp. e26527.

Gerts D, Shelley CD, Parikh N, et al. "Thought I'd Share First" and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study. JMIR Public Health Surveill. 2021;7(4):e26527.

Gerts, D., Shelley, C. D., Parikh, N., Pitts, T., Watson Ross, C., Fairchild, G., Vaquera Chavez, N. Y., & Daughton, A. R. (2021). "Thought I'd Share First" and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study. JMIR Public Health and Surveillance, 7(4), e26527. https://doi.org/10.2196/26527

Gerts D, et al. "Thought I'd Share First" and Other Conspiracy Theory Tweets From the COVID-19 Infodemic: Exploratory Study. JMIR Public Health Surveill. 2021 04 14;7(4):e26527. PubMed PMID: 33764882.

* Article titles in AMA citation format should be in sentence-case

TY - JOUR T1 - "Thought I'd Share First" and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study. AU - Gerts,Dax, AU - Shelley,Courtney D, AU - Parikh,Nidhi, AU - Pitts,Travis, AU - Watson Ross,Chrysm, AU - Fairchild,Geoffrey, AU - Vaquera Chavez,Nidia Yadria, AU - Daughton,Ashlynn R, Y1 - 2021/04/14/ PY - 2020/12/15/received PY - 2021/03/19/accepted PY - 2021/02/17/revised PY - 2021/3/26/pubmed PY - 2021/4/21/medline PY - 2021/3/25/entrez KW - 5G KW - COVID-19 KW - Twitter KW - active learning KW - communication KW - conspiracy KW - conspiracy theories KW - coronavirus KW - health communication KW - infodemic KW - infodemiology KW - machine learning KW - misinformation KW - public health KW - random forest KW - social media KW - supervised learning KW - unsupervised learning KW - vaccine KW - vaccine hesitancy SP - e26527 EP - e26527 JF - JMIR public health and surveillance JO - JMIR Public Health Surveill VL - 7 IS - 4 N2 - 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. SN - 2369-2960 UR - https://www.unboundmedicine.com/medline/citation/33764882/"Thought_I'd_Share_First"_and_Other_Conspiracy_Theory_Tweets_from_the_COVID_19_Infodemic:_Exploratory_Study_ DB - PRIME DP - Unbound Medicine ER -