Abstract
The digital trails of activity on social media are valuable for public health due to their potential to reveal risky health behavior, but there are still considerable methodological issues associated to using data from social media. One particular source of bias is the presence of automated accounts, or social bots, whose activity may compromise predictive tasks based on social media data. In this work, we collected a corpus of public tweets about electronic vaping and combine them with data from the CDC to predict the incidence of lung injuries by state. We show that only when likely bot accounts are removed the relative volume of tweets about vaping predicts injuries, but this correlation disappears otherwise. We compare the predictive power of these data against survey-based predictions, and show that our models achieve the lowest generalization error. These results highlight the importance of bot detection as a data cleaning step and the potential value of social media data in the context of public health.
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Change history
13 December 2022
In an older version of this paper, there was error in figure 2. This has been corrected.
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Acknowledgement
The authors would like to thank Filippo Menczer and Kai-Cheng Yang for providing access to the BotometerLite API, and to Hunter Morera for help with data collection and coding during the initial part of this project.
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Gera, P., Ciampaglia, G.L. (2022). Chasing the Wrong Cloud: Mapping the 2019 Vaping Epidemic Using Data from Social Media. In: Thomson, R., Dancy, C., Pyke, A. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2022. Lecture Notes in Computer Science, vol 13558. Springer, Cham. https://doi.org/10.1007/978-3-031-17114-7_1
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DOI: https://doi.org/10.1007/978-3-031-17114-7_1
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