How Opinions are Received by Online Communities- Case study on Amazon helpfulness votes Cristian Danescu-Niculescu-Mizil 1 , Gueorgi Kossinets 2 , Jon Kleinberg 1 , Lillian Lee 1 1 Dept. of Computer Science, Cornell University, 2 Google Inc. WWW 2009 Emin Sadiyev Cmpe 493
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Cristian Danescu-Niculescu-Mizil 1, Gueorgi Kossinets 2, Jon Kleinberg 1, Lillian Lee 1 1 Dept. of Computer Science, Cornell University, 2 Google Inc.
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How Opinions are Received by Online Communities- Case study on Amazon helpfulness votes
Cristian Danescu-Niculescu-Mizil1, Gueorgi Kossinets2, Jon Kleinberg1, Lillian Lee1
1Dept. of Computer Science, Cornell University, 2Google Inc.
WWW 2009
Emin Sadiyev
Cmpe 493
Amazon.com layout
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Average star rating
Helpfulness ratio
OutlineUsers’ evaluation on online reviews:
Helpfulness votesMake hypothesisProving their validityComing up with a mathematical model that
explains these behaviors
Introduction
OpinionWhat did Y think of X?
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Introduction
Meta-OpinionWhat did Z think of Y’s opinion of X?
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The Helpfulness of ReviewsWidely-used web sites include not just reviews,
but also evaluations of the helpfulness of the reviewsThe helpfulness vote
“Was this review helpful to you?”Helpfulness ratio:
“a out of b people found the review itself helpful”
b
a
Flow of Presentation
Hypothe-siz-ing
Verify-ing
Model-ing
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Flow of PresentationHypoth-esizing•Con-formity
•Individ-ual-bias
•Bril-liant-but-cruel
•Quality-only
Verifying Modeling
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Hypotheses: Social MechanismsWell-studied hypotheses for how social effects
influence group’s reaction to an opinionThe conformity hypothesisThe individual-bias hypothesisThe brilliant-but-cruel hypothesisThe quality-only straw-man hypothesis
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HypothesesThe conformity hypothesis
Review is evaluated as more helpful when its star rating is closer to the consensus star rating Helpfulness ratio will be the highest of which
reviews have star rating equal to overall average
The individual-bias hypothesisWhen a user considers a review, he or she will
rate it more highly if it expresses an opinion that he or she agrees with
Classification using machine learning methods We cannot guarantee the accuracies of algorithms
Plagiarized reviewsAlmost(not exact) same text
same text could be considered as spam reviewsDifferent non-textual information
If the quality-only straw man hypothesis holds, helpfulness ratios of documents in each pair should be the same
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PlagiarismMaking use of plagiarism is effective way to
control for the effect of review textDefinition of plagiarized pair(s) of reviews
Two or more reviews of different productsWith near-complete textual overlap
Author takes %70 textual overlap as plagiarism
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An ExampleSkull-splitting headache guaranteed!•If you enjoy thumping, skull splitting migraine headache, then Sing N Learn is for you.As a longtime language instructor, I agree with the attempt and ef-fort that this series makes, but it is the execution that ultimately weakens Sing N Learn Chinese.To be sure, there are much, much better ways to learn Chinese. In fact, I would recommend this title only as a last resort and after you’ve thoroughly exhausted traditional ways to learn Chinese …
Migraine Headache at No Extra Charge•If you enjoy a thumping, skull splitting migraine headache, then the Sing N Learn series is for you.As a longtime language instructor, I agree with the effort that this series makes, but it is the execution that ultimately weakens Sing N Learn series. To be sure, there are much, much better ways to learn a foreign language. In fact, I would recommend this title only as a last resort and after you’ve thoroughly exhausted tradi-tional ways to learn Korean …
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Experiments with PlagiarismText quality is not the only explanatory factor
Statistically significant difference between the helpfulness ratios of plagiarized pairs
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The plagiarized reviews with deviation 1 is significantly more helpful than those with deviation 1.5
HypothesesConformity•A review is evaluated as more helpful when its star rating is closer to the average star rating
Individual-bias•A review is evaluated as more helpful when its star rating is closer to evaluator’s opinion
Brilliant-but-cruel•A review is evaluated as more helpful when its star rating is below to the average star rating