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

Journal Article · Wed Apr 14 00:00:00 EDT 2021 · JMIR Public Health and Surveillance

DOI:https://doi.org/10.2196/26527· OSTI ID:1804356

Gerts, Dax ^[1]; Shelley, Courtney D. ^[1]; Parikh, Nidhi ^[1]; Pitts, Travis ^[1]; Watson Ross, Chrysm ^[2]; Fairchild, Geoffrey ^[1]; Vaquera Chavez, Nidia Yadria ^[2]; Daughton, Ashlynn R. ^[1]

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1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Univ. of New Mexico, Albuquerque, NM (United States)

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 non-misinformation 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

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA); Univ. of California National Laboratory Fees Research Program
• Grant/Contract Number: 89233218CNA000001
• OSTI ID: 1804356
• Report Number(s): LA-UR--20-28305
• Journal Information: JMIR Public Health and Surveillance, Journal Name: JMIR Public Health and Surveillance Journal Issue: 4 Vol. 7; ISSN 2369-2960
• Publisher: JMIR Publications
• Country of Publication: United States
• Language: English

Related Subjects

5g
60 APPLIED LIFE SCIENCES
COVID-19
active learning
conspiracy theories
coronavirus
health communication
misinformation
random forest
social media
supervised learning
twitter
unsupervised learning
vaccine hesitancy