Algorithms and Health Misinformation: A Case Study of Vaccine Books on Amazon
Algorithms and Health Misinformation: A Case Study of Vaccine Books on Amazon
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算法和健康错误信息：亚马逊疫苗书籍的案例研究
DOI：
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发表时间：
2020
影响因子：
4.4
通讯作者：
T. Valente
中科院分区：
文献类型：
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作者：
Jieun Shin;T. Valente
这项研究考察了疫苗相关书籍在亚马逊上的出现方式，重点关注搜索和推荐算法。我们收集了连续七天出现在亚马逊前10个搜索结果页面上的疫苗相关书籍，并对每本书进行了内容编码。我们还收集了亚马逊对每本疫苗书籍的推荐，并绘制了这些书籍之间的推荐网络。首先，我们发现犹豫接种疫苗的书籍数量是支持接种疫苗书籍的两倍。在这些疫苗犹豫书中，21%是由医生和医学专家撰写的。其次，尽管我们没有发现证据表明他们的搜索算法系统地倾向于任何特定类型的书籍，但七天内排名最高的三本书都是疫苗犹豫的书籍。最后，使用网络模型，我们发现共享疫苗相似观点的书籍被一起推荐，这样当用户查看疫苗犹豫书时，许多其他疫苗犹豫书被进一步推荐给用户。最常被推荐的三本书都是关于疫苗的。讨论了盲目地将商业算法应用于疫苗等复杂的健康信息的潜在后果。
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.