  Search and recommendation systems are ubiquitous and irreplaceable tools in
our daily lives. Despite their critical role in selecting and ranking the most
relevant information, they typically do not consider the veracity of
information presented to the user. In this paper, we introduce an audit
methodology to investigate the extent of misinformation presented in search
results and recommendations on online marketplaces. We investigate the factors
and personalization attributes that influence the amount of misinformation in
searches and recommendations. Recently, several media reports criticized Amazon
for hosting and recommending items that promote misinformation on topics such
as vaccines. Motivated by those reports, we apply our algorithmic auditing
methodology on Amazon to verify those claims. Our audit study investigates (a)
factors that might influence the search algorithms of Amazon and (b)
personalization attributes that contribute to amplifying the amount of
misinformation recommended to users in their search results and
recommendations. Our audit study collected ~526k search results and ~182k
homepage recommendations, with ~8.5k unique items. Each item is annotated for
its stance on vaccines' misinformation (pro, neutral, or anti). Our study
reveals that (1) the selection and ranking by the default Featured search
algorithm of search results that have misinformation stances are positively
correlated with the stance of search queries and customers' evaluation of items
(ratings and reviews), (2) misinformation stances of search results are neither
affected by users' activities nor by interacting (browsing, wish-listing,
shopping) with items that have a misinformation stance, and (3) a filter bubble
built-in users' homepages have a misinformation stance positively correlated
with the misinformation stance of items that a user interacts with.
