论文标题
在线市场中调查错误信息：亚马逊的审计研究
Investigating Misinformation in Online Marketplaces: An Audit Study on Amazon
论文作者
论文摘要
搜索和推荐系统是我们日常生活中无处不在且不可替代的工具。尽管它们在选择和排名最相关的信息方面具有至关重要的作用，但他们通常不考虑向用户提供的信息的真实性。在本文中，我们介绍了一种审计方法，以研究在在线市场上搜索结果和建议中提出的错误信息的程度。我们研究了影响搜索和建议中错误信息数量的因素和个性化属性。最近，一些媒体报道批评亚马逊托管并推荐了促进疫苗等主题错误信息的物品。在这些报告的激励下，我们在亚马逊上应用了算法审计方法来验证这些主张。我们的审计研究调查了可能影响亚马逊搜索算法的因素，以及（b）个性化属性，这些属性有助于扩大向用户推荐给用户搜索结果和建议的错误信息的数量。我们的审计研究收集了〜526K搜索结果和〜182K首页建议，并提供了〜8.5k独特的项目。每个项目都因其对疫苗的错误信息（Pro，中性或抗）的立场而被注释。 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）过滤器气泡内置用户的主页具有错误的信息姿态，与用户与用户互动的项目的错误信息姿态正相关。
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.
