TY  - JOUR
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
PY  - 2021
DA  - 2021/4/14
TI  - “Thought I’d Share First” and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study
JO  - JMIR Public Health Surveill
SP  - e26527
VL  - 7
IS  - 4
KW  - COVID-19
KW  - coronavirus
KW  - social media
KW  - misinformation
KW  - health communication
KW  - Twitter
KW  - infodemic
KW  - infodemiology
KW  - conspiracy theories
KW  - vaccine hesitancy
KW  - 5G
KW  - unsupervised learning
KW  - random forest
KW  - active learning
KW  - supervised learning
KW  - machine learning
KW  - conspiracy
KW  - communication
KW  - vaccine
KW  - public health
AB  - 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://publichealth.jmir.org/2021/4/e26527
UR  - https://doi.org/10.2196/26527
UR  - http://www.ncbi.nlm.nih.gov/pubmed/33764882
DO  - 10.2196/26527
ID  - info:doi/10.2196/26527
ER  -