Algorithms and Health Misinformation: A Case Study of Vaccine Books on Amazon

Abstract

This study examines how vaccine-related books appear on Amazon, focusing on search and recommendation algorithms. We collected vaccine related books that appeared on the first 10 search result pages by Amazon for seven consecutive days and content coded each book. We also collected Amazon's recommendations for each vaccine book and mapped the network of recommendation among these books. First, we found that the number of vaccine-hesitant books outnumbered vaccine-supportive books two to one. Of these vaccine-hesitant books, 21% were written by physicians and medical experts. Second, although we did not find evidence that their search algorithm systematically favored any particular type of book, the three top ranked books across the seven days were all vaccine-hesitant ones. Lastly, using a network model, we found that books sharing similar views of vaccines were recommended together such that when a user views a vaccine-hesitant book, many other vaccine-hesitant books are further recommended for the user. The three most frequently recommended books were vaccine-hesitant ones. The potential consequences of blindly applying commercial algorithms to a complicated health messages such as vaccines are discussed.