document: How opinions are received by online communities : a case study on amazon.com helpfulness votes 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 ifferent countries .
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