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.
@inproceedings{Danescu-Niculescu-Mizil+al:09a, author = {Cristian Danescu-Niculescu-Mizil and Gueorgi Kossinets and Jon Kleinberg and Lillian Lee}, title = {How opinions are received by online communities: A case study on {Amazon.com} helpfulness votes}, year = {2009}, pages = {141--150}, booktitle = {Proceedings of WWW} }
This paper is based upon work supported in part by a University Fellowship from Cornell, DHS grant N0014-07-1-0152, the National Science Foundation grants BCS-0537606, CCF-0325453, CNS-0403340, and CCF-0728779, a John D. and Catherine T. MacArthur Foundation Fellowship, a Google Research Grant, a Yahoo! Research Alliance gift, a Cornell University Provost’s Award for Distinguished Scholarship, a Cornell University Institute for the Social Sciences Faculty Fellowship, and an Alfred P. Sloan Research Fellowship. Any opinions, findings, and conclusions or recommendations expressed are those of the authors and do not necessarily reflect the views or official policies, either expressed or implied, of any sponsoring institutions, the U.S. government, or any other entity.
