论文标题
运动评论：大规模的，纵向研究Yelp的审查建议
Reviews in motion: a large scale, longitudinal study of review recommendations on Yelp
论文作者
论文摘要
联合国消费者保护指南列出了“访问...足够的信息……做出明智的选择”作为核心消费者保护权。但是，算法中有问题的在线评论和瑕疵检测到这些评论对实现这一权利构成了障碍。关于评论和评论平台的研究通常会从单个网络爬网中获得见解，但是这些爬网的决定可能不是静态的。一个平台可能有一天会采用评论，并在第二天从视图中过滤。要了解平台如何选择评论消费者遇到的评论以及哪些评论可能是无助或可疑的，这是必要的。我们引入了一个新的纵向角度，以研究评论。我们专注于“重新分类”，其中一个平台改变了其审查的过滤决定。为此，我们执行Yelp的重复网络爬网来创建三个纵向数据集。这些数据集突出了该平台对评论的动态处理。我们编制了超过1250万的评论 - 比200万独特的评论 - 超过10K企业。我们的数据集可供研究人员使用。 我们的纵向方法使我们对Yelp的分类器有了独特的看法，并使我们能够探索重新分类。我们发现，评论通常在Yelp的两个主要分类器类别（“推荐”和“不推荐”）之间进行 - 在八年内高达8％ - 对先前的工作的使用'将Yelp的类用作地面真相引起了人们的关注。这些变化对小规模有影响。例如，尽管没有新的评论，但业务从3.5到4.5星级。一些评论多次移动：我们在11个月内最多观察到五次重新分类。我们的数据表明，重新分类的人口统计学差异，较低的密度和低中间收入领域的变化发生了更多变化。
The United Nations Consumer Protection Guidelines lists "access ... to adequate information ... to make informed choices" as a core consumer protection right. However, problematic online reviews and imperfections in algorithms that detect those reviews pose obstacles to the fulfillment of this right. Research on reviews and review platforms often derives insights from a single web crawl, but the decisions those crawls observe may not be static. A platform may feature a review one day and filter it from view the next day. An appreciation for these dynamics is necessary to understand how a platform chooses which reviews consumers encounter and which reviews may be unhelpful or suspicious. We introduce a novel longitudinal angle to the study of reviews. We focus on "reclassification," wherein a platform changes its filtering decision for a review. To that end, we perform repeated web crawls of Yelp to create three longitudinal datasets. These datasets highlight the platform's dynamic treatment of reviews. We compile over 12.5M reviews--more than 2M unique--across over 10k businesses. Our datasets are available for researchers to use. Our longitudinal approach gives us a unique perspective on Yelp's classifier and allows us to explore reclassification. We find that reviews routinely move between Yelp's two main classifier classes ("Recommended" and "Not Recommended")--up to 8% over eight years--raising concerns about prior works' use of Yelp's classes as ground truth. These changes have impacts on small scales; for example, a business going from a 3.5 to 4.5 star rating despite no new reviews. Some reviews move multiple times: we observed up to five reclassifications in eleven months. Our data suggests demographic disparities in reclassifications, with more changes in lower density and low-middle income areas.
