Auditing E-Commerce Platforms for Algorithmically Curated Vaccine Misinformation

In our first set of audits, we ask,

We find a higher percentage of products promoting health misinformation (10.47%) compared to products that debunk misinformation (8.99%) in the unpersonalized search results. We discover that Amazon returns high number of misinformative search results when users sort their searches by filter “featured” and high number of debunking results when they sort results by filter “newest arrivals”. We also find Amazon ranking misinformative results higher than debunking results especially when results are sorted by filters “average customer reviews” and “price low to high”. Overall, search results of topics “vaccination”, “andrew wakefield” and “hpv vaccine” contain the highest misinformation bias when sorted by default filter “featured”. Our analysis of product page recommendations suggests that recommendations of products promoting health misinformation contain more health misinformation when compared to recommendations of neutral and debunking products.

Our Personalized audit reveals that search results sorted by filters “average customer review”, “price low to high” and “newest arrivals” along with auto-complete suggestions are not personalized. Additionally, we find that user actions involving clicking a search product leads to personalized homepages. We find evidence of filter-bubble effect in various recommendations found in homepages, product and pre-purchase pages. Surprisingly, the amount of misinformation present in homepages of accounts building their history by performing actions “search + click” and “mark top-rated all positive review as helpful” on misinformative products was more than the amount of misinformation present in homepages of accounts that added the same misinformative products in cart. The finding suggests that Amazon nudges users more towards misinformation once a user shows interest in a misinformative product by clicking on it but hasn’t shown any intention of purchasing it. Overall, our audits suggest that Amazon has a severe vaccine/health misinformation problem exacerbated by its search and recommendation algorithms. Yet, the platform has not taken any steps to address this issue.

In the absence of an online regulatory body monitoring the quality of content created, sold and shared, vaccine misinformation is rampant on online platforms. Through our work, we specifically bring the focus on e-commerce platforms since they have the power to influence browsing as well as buying habits of millions of people. We believe our study is the first large-scale systematic audit of an e-commerce platform that investigates the role of its algorithms in surfacing and amplifying vaccine misinformation. Our work provides an elaborate understanding of how Amazon’s algorithm is introducing misinformation bias in product selection stage and ranking of search results across 5 Amazon filters for 10 impactful vaccine-related topics. We find that even use of different search filters on Amazon can dictate what kind of content a user can be exposed to. For example, use of default filter “featured” lead users to more health misinformation while sorting search results by filter “newest arrivals” lead users to products debunking health-related misinformation. Ours is also the first study to empirically establish how certain real-world actions on health misinformative products on Amazon could drive users into problematic echo chambers of health misinformation. Both our audit experiments resulted in a dataset of 4,997 unique Amazon products distributed across 48 search queries, 5 search filters, 15 recommendation types, and 6 user actions, conducted over 22 (15+7) days ¹¹1https://social-comp.github.io/AmazonAudit-data/. Our findings suggest that traditional recommendation algorithms should not be blindly applied to all topics equally. There is an urgent need for Amazon to treat vaccine related searches as searches of higher importance and ensure higher quality content for them. Finally, our findings also have several design implications that we discuss in detail in Section 7.4.

We took several steps to minimize the potential harm of our experiments to retailers. For example, buying and later returning an Amazon product for the purpose of our project can be deemed unethical and thus, we avoid performing this activity. Similarly, writing a fake positive review about an Amazon product containing misinformation could negatively influence the audience. Therefore, in our Personalized audit we explored other alternatives that could mimic similar if not the same influence as the aforementioned activities. For example, instead of buying a product, we performed ”add to cart” action that shows users’ intent to purchase a product. Instead of writing positive reviews for products, we marked top rated positive review as helpful. Since, accounts did not have any purchase history, marking a review helpful did not increase the “Helpful” count for that review. Through this activity, the account shows positive reaction towards the product, at the same time avoids manipulation and thus, eliminates impacting potential buyers or users. Lastly, we refrained from performing the experiments on real-world users. Performing actions on misinformative products could contaminate users’ searches and recommendations. It could potentially have long-term consequences in terms of what types of products are pushed at participants. Thus, in our audit experiments, accounts were managed by bots that emulated the actions of actual users.

The current research on online health misinformation including vaccine misinformation spans three broad themes: 1) quantifying the characteristics of anti-vaccine discourse (Mitra et al., 2016; Mønsted and Lehmann, 2019; Cossard et al., 2020), 2) building machine learning models to identify users engaging with health misinformation or instances of health misinformation itself (Ghenai and Mejova, 2018; Dai et al., 2020; Ghenai and Mejova, 2017) and 3) designing and evaluating effective interventions to ensure that users critically think when presented with health (mis)information (Kim et al., 2020; van der Meer and Jin, 2020). Most of these studies are post-hoc investigations of health misinformation, i.e the misinformation has already propagated. Moreover, existing scholarship rarely takes into account how the user encountered health misinformation or what role is played by the source of the misinformation. With the increasing reliance on online sources for health information, search engines have become the primary avenue of such information, with 55% of American adults relying on the web to get medical information (Rainie and Fox, 2000). A Pew survey reports that for 5.9M people, web search results influenced their decision to visit a doctor and 14.7M claimed that online information affected their decision on how to treat a disease (Rainie and Fox, 2000). Given how medical information can directly influence one’s health and well-being, it is essential that search engines return quality results in response to health related search queries. However, currently online health information has been contaminated by several outlets. These sources could be conspiracy groups or websites spreading misinformation due to vested interests or companies having commercial interests in selling herbal cures or fictitious medical treatments (Schwitzer, 2017). Moreover, online curation algorithms themselves are not built to take into account the credibility of information. Thus, it is of paramount importance that the role of search engines are investigated for harvesting health misinformation. How can we empirically and systematically probe search engines to investigate problematic behaviour like prevalence of health misinformation? In the next section, we briefly describe the emerging research field of “algorithmic auditing” that is focused on investigating search engines to reveal problematic biases. We discuss this field as well as our contribution to this growing research space in the next section.

Search engines are modern day gatekeepers and curators of information. Their black-box algorithm can shape user behaviour, alter beliefs and even affect voting behaviour either by impeding or facilitating the flow of certain kinds of information (Epstein and Robertson, 2015; Diakopoulos et al., 2018; Knobloch-Westerwick et al., 2015). Despite their importance and the power they exert, till date, search engine results and recommendations have mostly been unregulated. Information quality of search engine’s output is still measured in terms of relevance and it is up to the user to determine the credibility of information. Thus, researchers have advocated for making algorithms more accountable. One primary method to achieve this is to perform systematic audits to empirically establish the conditions under which problematic behavior surfaces. Raji et al provide the following definition of algorithmic audits. An algorithmic audit involves the collection and analysis of outcomes from a fixed algorithm or defined model within a system. Through the stimulation of a mock user population, these audits can uncover problematic patterns in models of interest (Raji and Buolamwini, 2019).

Previous audit studies have investigated the search engines for partisan bias (Robertson et al., 2018; Mustafaraj et al., 2020), gender bias (Chen et al., 2018; Kay et al., 2015), content diversity (Trielli and Diakopoulos, 2019; Steiner et al., 2020; Puschmann, 2019), and price discrimination (Hannak et al., 2014). However, only a few have systematically investigated search engines’ role in surfacing misinformation ((Hussein et al., 2020) is the only exception). Moreover, there is a dearth of systematic audits focusing specifically on health misinformation. The past literature, mostly consists of small-scale experiments that probe search engines with a handful of search queries. For example, an analysis of the first 30 pages of search results for query “vaccines autism” revealed that Google.com has 10% less anti-vaccine search results compared to the other search engines, like Qwant, Swisscows and Bing (Ghezzi et al., 2020). Whereas, search results present in the first 102 pages for the query “autism vaccine” on Google’s Turkey version returned 20% websites with incorrect information (Erden et al., 2019). One recently published work, closely related to this study, examined Amazon’s first 10 pages of search results in response to the query “vaccine”. They only collected and annotated books appearing in the searches for misinformation (Shin and Valente, 2020). The aforementioned studies probed the search engine for one single query and did the analysis on multiple search results pages. We, on the other hand, perform our Unpersonalized audit on a curated list of 48 search queries belonging to 10 most searched vaccine-related topics, spanning various combinations of search filters and recommendation types, over multiple days—an aspect missing in prior work. Additionally, we are the first ones to experimentally quantify the prevalence of misinformation in various search queries, topics, and filters on an e-commerce platform. Furthermore, instead of just focusing on books, we analyze the platform for products belonging to different categories, resulting in an extensive all-category inclusive coding scheme for health misinformation.

Another recent study on YouTube, audited the platform for various misinformative topics including vaccine controversies (Hussein et al., 2020). The work established the effect of personalization due to watching videos on the amount of misinformation present in search results and recommendations on YouTube. However, there are no studies investigating the impact of personalization on misinformation present in the product search engines of e-commerce platforms. Our work fills this gap by conducting a second set of audit—Personalized audit where we shortlist several real-world user actions and investigate their role in amplifying misinformation in Amazon’s searches and recommendations.

Here, we present our methodology to curate high impact vaccine-related topics and search queries.

Unlike partisan bias where bias could be determined by using features such as news source bias (Robertson et al., 2018), labelling a product for misinformation is hard and time-consuming. There are no pre-determined sources of misinformation such as list of sellers or authors of misinformative products on Amazon. Additionally, we found that the annotation process for some categories of products, like Books, Kindle ebooks, etc. required us to consider the product image, read the book’s preview, if available, and even perform external search about the authors. Therefore, we opted to manually annotate our data collection. We developed a qualitative coding scheme to label our Amazon data collection through an iterative process that required several rounds of discussions to reach an agreement on the annotation scale.

In the first round, first author randomly sampled 200 Amazon products across different topics and categories. After multiple iterations of analyzing and interpreting each product, the author came up with an initial 7-point annotation scale. Then, six researchers with extensive work experience on online misinformation independently annotated 32 products, randomly selected from the 200 products. We discussed every product’s annotation value and the researchers’ annotation process. We refined the scale as well as the scheme based on the feedback. This process was repeated thrice after which all six annotators reached a consensus on the annotation scheme and process. In the fourth round, we gathered additional feedback from an external researcher from the Credibility Coalition group⁸⁸8https://credibilitycoalition.org/—an international organization of interdisciplinary researchers and practitioners dedicated to developing standards for news credibility and tackling the problem of online misinformation. The final result of the multi-stage iterative process (see Appendix, Figure 14) is a 7-point annotation scale comprising of values ranging from -1 to 5 (see Table 5). The scale measures the scientific quality of products that users are exposed to when they make vaccine-related searches on Amazon.

In this section, we describe our method to determine the amount of misinformation present in search results. How do we estimate the misinformation bias present in Amazon’s SERPs? First, we used our annotation scheme to assign misinformation bias scores ($s_{i}$) to individual products present in SERPs. We converted our 7 point (-1 to 5) scale to misinformation bias scores with values -1, 0 and 1. We mapped annotation values 2, 3, 4, and 5 to bias score 0. Merging “unknown” annotations to neutral will result in a conservative estimate of misinformation bias present in the search results. Now, a product can be assigned one of the three bias scores: -1 suggests that product debunks misinformation, 0 indicates a neutral stance and 1 implies that the product promotes misinformation. Next, to quantify misinformation bias in Amazon’s SERPs, we adopt the framework and metrics proposed in prior work to quantify partisan bias in Twitter search results (Kulshrestha et al., 2017). Below we discuss three kinds of bias proposed by the framework and delineate how we estimate each bias with respect to misinformation. Table 6 illustrates how we calculated the bias values.

1. (i)

   The input bias (ib) of a list of Amazon products is the mean of misinformation bias scores of the constituting products (Kulshrestha et al., 2017). Therefore, ib = ${\sum_{i=1}^{n}{s_{i}}}$, where n is the length of the list & ${s_{i}}$ is the misinformation bias score of ith product in the list. Input bias is an unweighted bias, i.e it is not affected by the rank/ordering of the items.

2. (ii)

   The output bias (ob) of a ranked list is the overall bias present in the SERPs and is the sum of biases introduced due to input and ranks of the input. We first calculate weighted bias score B(r) of every rank r, which is the average misinformation bias of products ranked from 1 to r. Thus, B(r) = $\frac{\sum_{i=1}^{r}{s_{i}}}{r}$, where ${s_{i}}$ is the misinformation bias score of ith product. Output bias (ob) is the average of weighted bias score B(r) for all ranks. Thus, by definition ob = $\frac{\sum_{i=1}^{r}{B(i)}}{r}$.

3. (iii)

   The ranking bias (rb) is introduced by the ranking algorithm of search engine (Kulshrestha et al., 2017). It is calculated by subtracting input bias from output bias. Thus, rb = ob-ib. In our case, high ranking bias indicates that search algorithm ranks misinformative products higher than neutral or debunking products.

Why do we need three bias scores? Amazon’s search algorithm is not only selecting the products to be shown in the search results but it is also ranking them according to their internal algorithm. Therefore, the overall bias (ob) could be introduced either at the product selection stage (ib), or ranking stage (rb) or both. Studying all three biases gives us an elaborate understanding of how biases are introduced by the search algorithm. All three bias values (ib, ob and rb) lie between -1 and 1. A bias score larger than 0 indicates a lean towards misinformation. Conversely, a bias score less than 0 indicates a propensity towards debunking information. We only consider top 10 search results in each SERP. Thus, in the bias calculations, rank always varies from 1 to 10.

We collected 36,000 search results from our Unpersonalized audit run, out of which 3,180 were unique. Recall, we collected these products by searching for 48 search queries belonging to vaccine-related topics and sorting results by each of the 5 Amazon filters. We later extracted and annotated top 10 search results from all the collected SERPs resulting in 3,180 annotations. Figure 6(a) shows the number (and percentage) of products corresponding to each annotation value. Through our audits, we find a high percentage (10.47%) of misinformative products in the search results. Moreover, the number of misinformative products outnumbered the debunking products. Figure 7 illustrates the distribution of categories of Amazon products annotated as debunking (-1), neutral (0) and promoting (1). Note that the products promoting health misinformation primarily belong to categories Books (35.43%), Kindle eBooks (28.52%), Amazon Fashion (12.61%)—a category that includes t-shirts, apparel, etc. and Health & Personal Care (10.21%)—a category consisting of dietary supplements. Below we discuss the misinformation bias observed across all the vaccine-related topics, the Amazon search filters and search queries.

We extracted the product page recommendations of top 3 search results present in the SERPs. The product page constitutes of various types of recommendations. For analysis, we considered the first product present in 5 types of recommendations “Customers who bought this item also bought” (CBB), “Customers who viewed this item also viewed” (CVV), “Frequently bought together” (FBT), “Sponsored products related to this item” and “What other items customers buy after viewing this item” (CBV). The process resulted in 16,815 recommendations out of which 1,853 were unique. Figure 6(b) shows the number and percentage of recommendations belonging to different annotation values. The percentage of misinformative recommendations (12.95%) is much higher than the debunking recommendations (1.95%). The total input bias in all 16,815 recommendations is 0.417 while in all 1,853 unique recommendations is 0.109, indicating a lean towards misinformation.

Does filter-bubble effect occur in product page recommendations? To answer, we compared the misinformation bias scores of all types of recommendations considered together (refer Table 7). Kruskal Wallis Anova test revealed the difference to be significant (KW H(2, N=16815) = 6,927.6, p=0.0). Post-hoc Tukey HSD test showed that the product page recommendations of misinformative products contain more misinformation when compared to recommendations of neutral and debunking products. Even more concerning is that the recommendations of debunking products have more misinformation than neutral products. To investigate further, we qualitatively studied the recommendation graphs of each of the five recommendation types (Figure 11). Each node in the graph represents an Amazon product. An edge A$\rightarrow$ B indicates that B was recommended in the product page of A. Node size is proportional to the number of times the product was recommended.

We measure personalization in search results for each Amazon filter using two metrics: Jaccard index and Kendall $\tau$ coefficient. Jaccard index determines similarity between two lists. A Jaccard index of 1 indicates that the two lists have same elements and zero indicates that the lists are completely different. On the other hand, Kendall $\tau$ coefficient, also known as Kendall rank correlation coefficient determines the ordinal correlation between two lists. It can take values between [-1,1] with -1 indicating that lists have inverse ordering, 0 signifying no correlation and 1 suggesting that items in the list have same ranks.

First, we compare search results of control account and its twin. Recall we created twins for our 2 control accounts in the Personalized audit to establish the baseline noise. Ideally, both should have Jaccard and Kendall rank correlation coefficient closer to 1 since the accounts do not build any history, are set up in a similar manner, perform searches at the same time and are in the same geolocation. Next, we compare search results of control account with treatment accounts that built account histories by performing different actions. If personalization is occurring, the difference between search results of treatment and control should be more than the baseline noise (or Jaccard index and Kendall $\tau$ should be less). Whereas, if the baseline noise itself is large, it indicates inconsistencies and randomness in the search results. Interestingly, we found significant noise in search results of control and its twin for “featured” filter with jaccard index ¡0.8 and Kendall’s rank correlation coefficient ¡0.2, that is, control and its twins seldom matched. Presence of noise suggests that Amazon is injecting some randomness in the “featured” search results. Unfortunately, this means that we would not be able to study the effect of personalization on the accounts for the “featured” search filter setting.

For the other three search filters, “average customer review”, “price low to high” and “newest arrivals”, we see high (¿0.8) jaccard index and kendall $\tau$ metric values between and control and its twin. Additionally, we do not see any personalization for these filters since metrics values for treatment-control comparison are similar to that of control-twin comparison. Figure 12 shows the metrics calculation for control account and treatments that have built their search histories by following contributor’s of misinformative, neutral and debunking products. We see two minor inconsistencies for filter “average customer review” in accounts building their history on debunking products where treatment received more similar results to control than its twin account. In any case, the treatment does not see more inconsistency than the control and its twin indicating no personalization. Other user actions show similar results, hence, we have removed their results for brevity.

We investigated the occurrence of personalization and its impact on the amount of misinformation in three different recommendation pages. We discuss them below.

Statistical tests reveal significant differences in the amount of misinformation present in homepages of accounts that built their histories by performing actions on misinformative, neutral and debunking products (see Table 8). This observation holds true for all three activities “add to cart”, “search + click” and “mark top rated most positive review helpful”. Post hoc test reveals an echo chamber effect. Amount of misinformation in recommendations of products performing actions on misinformative products is more than the amount of misinformation in homepages of accounts performing actions on neutral products which in turn is more than the misinformation present in homepages of accounts performing actions on debunking products.

Figure 13(a) shows per day input bias of homepages of different accounts performing different actions. We take an average of the replicates for plotting the graph. Surprisingly, performing actions “mark top rated most positive review helpful” and “search + click” on a misinformative product leads to highest amount of misinformation in the homepages, even more than the homepages of accounts adding misinformative products to the cart. This means that amount of misinformation present in homepage is comparatively less once a user shows an intention to purchase a misinformative product but high if a user shows interest in the misinformative product but doesn’t show an indication to buy it. Figure 13(a) also shows that amount of misinformation present in homepages of accounts performing actions “mark top rated most positive review helpful” and “search + click” on misinformative products gradually increases and becomes 1 on day 4 (2020-08-15). Bias value 1 indicates that all analysed products in homepages were misinformative. Homepage recommendations of products performing actions on neutral objects show 0 bias constantly indicating all recommendations on all days were neutral. On the other hand, average bias in homepages of accounts building history on debunking accounts rose a little above 0 in the first three days but eventually fells below 0 indicating a debunking lean.

We audited auto-complete suggestions to investigate how personalization affects the change in search query suggestions. Our initial hypothesis was that performing actions on misinformative products could increase the auto-complete suggestions of anti-vaccine search queries. However, we found little to no personalization in the auto-complete suggestions indicating that account history built by performing actions on vaccine-related misinformative, neutral or debunking products have little to no effect on how auto-complete suggestions of accounts change. In interest of brevity, we do not add the results and graphs for this component.

Our analysis shows that Amazon hosts a variety of health misinformative products. Maximum number of such products belong to the category Books and Kindle eBooks (Figure 7). Despite the enormous amount of information available online, people still turn to books to gain information. A Pew Research survey revealed that 73% of Americans read atleast one book in a year (Perrin, 2016). Books are considered “intellectual heft”, have more presence than scientific journals and thus, leave “a wider long lasting wake” (Herr, 2017). Thus, anti-vaccine books could have a wider reach and can easily influence the audience negatively. Moreover, it does not help that a large number of anti-vaccine books are written by authors with medical degrees (Shin and Valente, 2020). Not just anti-vaccine books, there are abundant pseudoscience books on the platform, all suggesting unproven methods to cure diseases. We found diet books suggesting recipes with colloidal silver—an unsafe product, as an ingredient. Some of the books proposing cures for incurable diseases, like autism and auto immune diseases, can have a huge appeal for people suffering with such diseases (Reynolds, 2019). Thus, there is an urgent need to check the quality of health books presented to the users.

The next most prominent category of health misinformative products is Amazon Fashion. Numerous apparels are sold on the platform with innovative anti-vaccine slogans, giving tools to the anti-vaccine propagandists to advocate their anti-vaccine agenda and gain visibility, not just in the online world, but in the offline world. During our annotation process, we also found many dietary supplements claiming to treat and cure diseases—a direct violation of Amazon’s policy on dietary supplements. Overall, we find that health misinformation exists on the platform in various forms—books, t-shirts and other merchandise. Additionally, it is very easy to sell problematic content because of lack of appropriate quality-control policies and their enforcement.

Analysis of our Unpersonalized audit revealed that 10.47% of search results promote vaccine and other health-related misinformation. Notably, the higher percentage of products promoting misinformation compared to debunking suggests that anti-vaccine and problematic health-related content is churned out more and the attempts to debunk the existing misinformation is less. We also found that Amazon’s search algorithm puts more health misinformative products in search results than debunking products leading to high input bias for topics like “vaccination”, “vaccine controversies”, “hpv vaccine”, etc. This is specifically true for search filters “featured” and “average customer reviews”. Note, that “featured” is the default search filter indicating that by default users will see more misinformation when they search for the aforementioned topics. On the other hand, if users want to make a purchase decision based on product ratings, again users will be presented with more misinformation since our analysis indicates that sorting by filter ”average customer reviews” leads to highest misinformation bias in the search results. We also found a ranking bias in Amazon’s search algorithm with misinformative products getting ranked higher. Past research has shown that people trust higher ranked search results (Guan and Cutrell, 2007). Thus, more number of higher ranked misinformative products can make problematic ideas in these products appear mainstream. The only positive finding of our analysis was the presence of more debunking products in search results sorted by filter “newest arrivals”. This might indicate that more higher quality products are being sold on the platform in recent times. However, since there are no studies/surveys indicating which search filters are mostly used by people while making purchase decisions, it is difficult to conclude how beneficial this finding is.

Many search engines and social media platforms employ personalization to enhance users’ experience on their platform by recommending them items that the algorithm think they will like based on their past browsing or purchasing history. But on the downside, if not checked, personalization can also lead users into a rabbit hole of problematic content. Our analysis of Personalized audit revealed that an echo chamber exists on Amazon where users performing real-world actions on misinformative books are presented with more misinformation in various recommendations. Just a single click on an anti-vaccine book could fill your homepage with several other similar anti vaccine books. And if you proceed to add that book in your cart, Amazon again presents more anti-vaccine books, nudging you to purchase even more problematic content. The worst discovery is that your homepages get filled with more misinformation if you just show an interest in a misinformative product (by clicking on it) compared to when you show an intention to buy it by adding product to your cart. Additionally on the product page itself, you are presented 5 different kinds of recommendations each of which contains equally problematic content. In a nutshell, once you start engaging with misinformative products on the platform, you will be presented with more misinformative stuff at every point of your Amazon navigation route and at multiple places. These findings would not have been concerning if buying a milk chocolate would lead to recommendations of other chocolates of different brands. The problem is that Amazon is blindly applying its algorithms on all products including problematic content. Its algorithms do not differentiate or give special significance to vaccine-related topics. Amazon has learnt from users’ past viewing and purchasing behaviour and has categorized all the anti-vaccine and other problematic health cures together. It presents the problematic content to users performing actions on any of these products, creating a dangerous recommendation loop in the process. There is an urgent need for the platform to treat vaccine and other health related topics differently and ensure high quality searches and recommendations. In the next section, we present a few ways, based on our findings, that could assist the platform in combating health misinformation.

Tackling online health misinformation is a complex problem and there is no easy silver-bullet solution to curb its spread. However, the first step towards addressing is accepting that there is a problem. Many tech giants have acknowledged their social responsibility in ensuring high quality in health-related content and are actively taking many steps to ensure the same. For example, Google’s policy “Your Money Or Your Life” classifies medical and health-related search pages as pages of particular importance, whose content should come from reputable websites (McGee, 2013). Pinterest completely hobbled the search results of certain queries such as ‘anti-vax’ (Caron, 2019) and limited the search results for other vaccine-related queries to content from officially recognized health institutions (Hutchinsona, 2019). Even Facebook—a platform known to have questionable content moderation policies—banned anti-vaccine advertisements and demoted the anti-vaccine content in its search results to make its access difficult (Matsakis, 2019). Therefore, given the massive reach and user base of Amazon—206 million website visits every month (10under100, 2020)—it is disconcerting to see that Amazon has not yet joined the bandwagon. Till date, it has not taken any concrete steps towards addressing the problem of anti-vaccine content on its platform. Through our findings, we recommend several short-term and long-term strategies that the platform can adopt.