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

Abstract

Background The coronavirus disease (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 the proliferation of this content is the growing ubiquity of internet-based technologies, including popular social media platforms that now have billions of global users. Objective This study aims to collect, analyze, identify, and enable reporting of suspected fake, counterfeit, and unapproved COVID-19–related health care products from Twitter and Instagram. Methods This study is conducted in two phases beginning with the collection of COVID-19–related Twitter and Instagram posts using a combination of web scraping on Instagram and filtering the public streaming Twitter application programming interface for keywords associated with suspect marketing and sale of COVID-19 products. The second phase involved data analysis using natural language processing (NLP) 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. Results 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 to April for Twitter and February to May for Instagram. After applying our NLP and deep learning approaches, we identified 1271 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. Conclusions 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.

Tracking Discussions of Complementary, Alternative, and Integrative Medicine in the Context of the COVID-19 Pandemic: A Month-by-Month Sentiment Analysis of Twitter Data

“…Other studies have focused their objectives on identifying types or prevalence of misinformation. Mackey et al [72] used NLP and deep learning to detect and characterize illicit COVID-19 product sales using Twitter and Instagram data. They identi ed a few hundred tweets and posts, respectively, containing questionable immunity-boosting treatments or involving suspect testing kits, as well as a small number of posts about pharmaceuticals that had not been approved for COVID-19 treatment.…”

The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic

“…32 features (mobility trends over time and demographic features) for 97 different countries. 4,625 inputs of 32 features each Jan 3 – Apr 29, 2020 Feed-Forward Neural Network (FFNN) R 2 : 0.97 vs R 2 (LSTM): 0.95 FFNN provides accurate and interpretable predictions better feature engineering or neural architecture search (with CNN or RNN) Mackey et al ( 2020 ) Twitter and Instagram text . Sales of COVID-19 related products.…”

“…The first diffusion of a pandemic in a global hyper-connected world has not only opened up new forms of international collaboration but has also revealed novel challenges to face. Misinformation and fake news, fraudulent sales of suspicious immunity-boosting treatments and health-related goods, and not approved medical treatments have highlighted the need for new policies and systems able to collect, evaluate, recognize and report dangerous news and counterfeit products to address these new social and economic issues (Mackey et al 2020 ). Healthcare structures are now under stress, and, especially in hospitals, tasks such as the optimization of diagnostic procedures and the isolation of infected patients reach their peak importance.…”

“…The temporal windows during which the data were gathered extend until April 2020. Despite the time interval reported for Mackey et al (2020), the relationship with Stage 3 for the COVID-19 is due to the fact that in the USA, by that time, the pandemic phase was still in the first stages For the model to work perfectly, the data must be accurate.…”

Engagement with COVID-19 Public Health Measures in the United States: A Cross-Sectional Social Media Analysis from June to November 2020

“…[5,14,15] However, existing findings are based on evidence during only the beginning of the outbreak, from December 2019 to April 2020, and the range of topics and keywords explored is also limited. [7,14,[15][16][17][18][19] Additionally, studies analyzing COVID-19 behaviors and beliefs on social media have primarily used Twitter as their source, which has several limitations. [14][15][16] Most notably, highly rated retweets are more likely to derive from spam and bot accounts, which are also actively posting about COVID- 19, and can obscure the targeting of signals from human discussions.…”