动态评论：Yelp上评论推荐的大规模纵向研究
Reviews in motion: a large scale, longitudinal study of review recommendations on Yelp
摘要：
《联合国消费者保护准则》列出了“准入...充分的信息...作出知情选择”作为一项核心的消费者保护权利。然而，有问题的在线评论和检测这些评论的算法的不完善对实现这一权利构成了障碍。对评论和评论平台的研究通常从单个网络抓取中获得见解，但这些抓取观察到的决定可能不是静态的。一个平台可能会在某一天发布评论，第二天就将其从视图中过滤掉。了解这些动态对于理解平台如何选择消费者遇到的评论以及哪些评论可能是无用或可疑的是必要的。我们引入了一个新的纵向角度来研究评论。我们专注于“重新分类”，即平台更改其过滤决定以进行审查。为此，我们对Yelp进行了重复的网络抓取，以创建三个纵向数据集。这些数据集突出了平台对评论的动态处理。我们收集了超过1250万条评论--超过200万条独立评论--涉及超过1万家企业。我们的数据集可供研究人员使用。我们的纵向方法为我们提供了对Yelp分类器的独特视角，并允许我们探索重新分类。我们发现，评论经常在Yelp的两个主要分类器类别（“推荐”和“不推荐”）之间移动-八年内高达8%-引起了对先前作品使用Yelp类别作为基础事实的担忧。这些变化对小规模的影响;例如，一个企业从3.5星级到4.5星级，尽管没有新的评论。有些审查多次移动：我们观察到在11个月内有5次重新分类。我们的数据表明，重新分类中存在人口差异，低密度和中低收入地区的变化更大。
英文摘要：
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.
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