Big Data, Natural Language Processing, and Deep Learning to Detect and Characterize Illicit COVID-19 Product Sales: An Infoveillance Study on Twitter and Instagram.

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Abstract

The COVID-19 pandemic is perhaps the greatest global health challenge of the last century. Accompanying this pandemic is a parallel “infodemic”, including the online marketing and sale of unapproved, illegal and counterfeit COVID-19 health products, including testing kits, treatments, and other questionable “cures”. Enabling proliferation of this content is growing ubiquity of Internet-based technologies, including popular social media platforms that now have billions of global users.
To collect, analyze, identify and enable reporting of suspected fake, counterfeit, and unapproved COVID-19-related healthcare products from Twitter and Instagram.
The study was conducted in two phases beginning with collection of COVID-19-related Twitter and Instagram posts using a combination of web scraping on Instagram and filtering the public streaming Twitter API for keywords associated with suspect marketing and sale of COVID-19 products. The second phase involved data analysis using natural language processing and deep learning to identify potential sellers that were then manually annotated for characteristics of interest. We also visualized illegal selling posts on a customized data dashboard to enable public health intelligence.
We collected a total of 6,029,323 tweets and 204,597 Instagram posts filtered for terms associated with suspect marketing and sale of COVID-19 health products from March – April for Twitter and February – May for Instagram. After applying our NLP and deep learning approaches, we identified 1,271 tweets and 596 Instagram posts associated with questionable sales of COVID-19-related products. Generally, product introduction came in two waves, with the first consisting of questionable immunity-boosting treatments and a second involving suspect testing kits. We also detected a low volume of pharmaceuticals that have not been approved for COVID-19 treatment. Other major themes detected included products offered in different languages, various claims of product credibility, completely unsubstantiated products, unapproved testing modalities, and different payment and seller contact methods.
Results from this study provide initial insight into one front of the “infodemic” fight against COVID-19 by characterizing what types of health products, selling claims and types of sellers were active on two popular social media platforms at earlier stages of the pandemic. This cybercrime challenge is likely to continue as the pandemic progresses and more people seek access to COVID-19 testing and treatment. This data intelligence can help public health agencies, regulatory authorities, legitimate manufacturers, and technology platforms better remove and prevent this content from harming the public.
Not applicable.