Dax Gerts 1 , Courtney D Shelley 1 , Nidhi Parikh 1 , Travis Pitts 1 , Chrysm Watson Ross 1,2 , Geoffrey Fairchild 1 , Nidia Yadria Vaquera Chavez 1,2 , Ashlynn R Daughton 1 Show Affiliations »
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. ©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.
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. ©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.
Entities: Chemical
Disease
Gene
Species
Keywords:
5G; COVID-19; Twitter; active learning; communication; conspiracy; conspiracy theories; coronavirus; health communication; infodemic; infodemiology; machine learning; misinformation; public health; random forest; social media; supervised learning; unsupervised learning; vaccine; vaccine hesitancy
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Year: 2021
PMID： 33764882 DOI： 10.2196/26527
Source DB: PubMed Journal: JMIR Public Health Surveill ISSN： 2369-2960