'Thought I'd Share First' and Other Conspiracy Theory Tweets from the COVID-19 Infodemic: Exploratory Study
| Autor: | Chrysm Watson Ross, Geoffrey Fairchild, Courtney D. Shelley, Ashlynn R. Daughton, Nidhi Parikh, Travis Pitts, Nidia Yadria Vaquera Chavez, Dax Gerts |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
conspiracy
FOS: Computer and information sciences Computer Science - Machine Learning 020205 medical informatics Conspiracy theory coronavirus 02 engineering and technology Machine Learning (cs.LG) 0302 clinical medicine infodemic Statistics - Machine Learning vaccine 0202 electrical engineering electronic engineering information engineering 030212 general & internal medicine Misinformation communication lcsh:Public aspects of medicine public health Computer Science - Social and Information Networks Data matching machine learning vaccine hesitancy Psychology medicine.medical_specialty Coronavirus disease 2019 (COVID-19) social media Internet privacy Exploratory research Health Informatics Machine Learning (stat.ML) unsupervised learning infodemiology supervised learning 03 medical and health sciences conspiracy theories active learning medicine health communication Humans Social media misinformation Social and Information Networks (cs.SI) Original Paper Information Dissemination business.industry Public health Public Health Environmental and Occupational Health COVID-19 lcsh:RA1-1270 business 5G random forest |
| Zdroj: | JMIR Public Health and Surveillance, Vol 7, Iss 4, p e26527 (2021) JMIR Public Health and Surveillance |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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