Auditing E-Commerce Platforms for Algorithmically Curated Vaccine Misinformation

TL;DR

This study audits Amazon’s search and recommendation algorithms, revealing biases and filter bubbles that promote vaccine misinformation, with misinformative content ranking higher and personalized recommendations reinforcing misinformation.

Contribution

It provides the first systematic analysis of vaccine misinformation amplification on Amazon’s platform through unpersonalized and personalized algorithmic audits.

Findings

10.47% of search results promote vaccine misinformation

Amazon ranks misinformative results higher than debunking ones

Personalized recommendations reinforce misinformation through filter bubbles

Abstract

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.