Auditing E-Commerce Platforms for Algorithmically Curated Vaccine Misinformation
This addresses the problem of algorithmically amplified misinformation on e-commerce platforms, which poses risks to public health, and is incremental as it applies existing audit methods to a new context.
The study audited Amazon's search and recommendation algorithms for vaccine misinformation, finding that 10.47% of search results promoted misinformative products and that personalization led to a filter-bubble effect, increasing misinformation exposure after user interactions.
There is a growing concern that e-commerce platforms are amplifying vaccine-misinformation. To investigate, we conduct two-sets of algorithmic audits for vaccine misinformation on the search and recommendation algorithms of Amazon -- world's leading e-retailer. First, we systematically audit search-results belonging to vaccine-related search-queries without logging into the platform -- unpersonalized audits. We find 10.47% of search-results promote misinformative health products. We also observe ranking-bias, with Amazon ranking misinformative search-results higher than debunking search-results. Next, we analyze the effects of personalization due to account-history, where history is built progressively by performing various real-world user-actions, such as clicking a product. We find evidence of filter-bubble effect in Amazon's recommendations; accounts performing actions on misinformative products are presented with more misinformation compared to accounts performing actions on neutral and debunking products. Interestingly, once user clicks on a misinformative product, homepage recommendations become more contaminated compared to when user shows an intention to buy that product.