在线社区如何接收意见：关于Amazon.com有益投票的案例研究
How opinions are received by online communities: A case study on Amazon.com helpfulness votes
摘要：
有许多用户公开表达意见的在线设置。其中一些提供了其他用户评估这些意见的机制;一个典型的例子是Amazon.com，其中评论带有注释，如“32人中有26人认为以下评论有帮助。“意见评估也出现在许多离线环境中，包括市场研究和政治运动。对一个观点的评价进行推理与对观点本身进行推理是根本不同的：而不是问：“Y对X有什么看法？我们要问的是，“Z对Y对X的看法是什么？”“在这里，我们开发了一个分析和建模意见评估的框架，使用大规模的亚马逊书评作为数据集。我们发现，一个审查的感知帮助不仅取决于它的内容，但也在微妙的方式上如何表达的评价涉及到同一产品的其他评价。作为我们方法的一部分，我们开发了新的方法，利用审查“剽窃”的现象来控制文本在意见评价中的影响，我们提供了一个简单而自然的数学模型与我们的研究结果相一致。我们的分析还使我们能够区分来自社会学和社会心理学的竞争理论的预测，并发现来自不同国家的用户群体的集体意见评估行为的意想不到的差异。
英文摘要：
There are many on-line settings in which users publicly express opinions. A number of these offer mechanisms for other users to evaluate these opinions; a canonical example is Amazon.com, where reviews come with annotations like "26 of 32 people found the following review helpful." Opinion evaluation appears in many off-line settings as well, including market research and political campaigns. Reasoning about the evaluation of an opinion is fundamentally different from reasoning about the opinion itself: rather than asking, "What did Y think of X?", we are asking, "What did Z think of Y's opinion of X?" Here we develop a framework for analyzing and modeling opinion evaluation, using a large-scale collection of Amazon book reviews as a dataset. We find that the perceived helpfulness of a review depends not just on its content but also but also in subtle ways on how the expressed evaluation relates to other evaluations of the same product. As part of our approach, we develop novel methods that take advantage of the phenomenon of review "plagiarism" to control for the effects of text in opinion evaluation, and we provide a simple and natural mathematical model consistent with our findings. Our analysis also allows us to distinguish among the predictions of competing theories from sociology and social psychology, and to discover unexpected differences in the collective opinion-evaluation behavior of user populations from different countries.
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