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

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 theoriesmore » 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« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [1]
  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)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program; Univ. of California National Laboratory Fees Research Program
OSTI Identifier:
1804356
Report Number(s):
LA-UR-20-28305
Journal ID: ISSN 2369-2960
Grant/Contract Number:  
89233218CNA000001; LFR-18-547591; 20200721ER
Resource Type:
Accepted Manuscript
Journal Name:
JMIR Public Health and Surveillance
Additional Journal Information:
Journal Volume: 7; Journal Issue: 4; Journal ID: ISSN 2369-2960
Publisher:
JMIR Publications
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; social media; misinformation; conspiracy theories; COVID-19; coronavirus; health communication; twitter; vaccine hesitancy; 5g; unsupervised learning; random forest; active learning; supervised learning

Citation Formats

Gerts, Dax, Shelley, Courtney D., Parikh, Nidhi, Pitts, Travis, Watson Ross, Chrysm, Fairchild, Geoffrey, Vaquera Chavez, Nidia Yadria, and Daughton, Ashlynn R. “Thought I’d Share First” and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study. United States: N. p., 2021. Web. doi:10.2196/26527.
Gerts, Dax, Shelley, Courtney D., Parikh, Nidhi, Pitts, Travis, Watson Ross, Chrysm, Fairchild, Geoffrey, Vaquera Chavez, Nidia Yadria, & Daughton, Ashlynn R. “Thought I’d Share First” and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study. United States. https://doi.org/10.2196/26527
Gerts, Dax, Shelley, Courtney D., Parikh, Nidhi, Pitts, Travis, Watson Ross, Chrysm, Fairchild, Geoffrey, Vaquera Chavez, Nidia Yadria, and Daughton, Ashlynn R. Wed . "“Thought I’d Share First” and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study". United States. https://doi.org/10.2196/26527. https://www.osti.gov/servlets/purl/1804356.
@article{osti_1804356,
title = {“Thought I’d Share First” and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study},
author = {Gerts, Dax and Shelley, Courtney D. and Parikh, Nidhi and Pitts, Travis and Watson Ross, Chrysm and Fairchild, Geoffrey and Vaquera Chavez, Nidia Yadria and Daughton, Ashlynn R.},
abstractNote = {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},
doi = {10.2196/26527},
journal = {JMIR Public Health and Surveillance},
number = 4,
volume = 7,
place = {United States},
year = {Wed Apr 14 00:00:00 EDT 2021},
month = {Wed Apr 14 00:00:00 EDT 2021}
}

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