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

Amazon.com layout 2 Average star rating Helpfulness ratio

Outline Users’ evaluation on online reviews: Helpfulness votes Make hypothesis Proving their validity Coming up with a mathematical model that explains these behaviors

Introduction Opinion What did Y think of X? 4

Introduction Meta-Opinion What did Z think of Y’s opinion of X? 5

The Helpfulness of Reviews Widely-used web sites include not just reviews, but also evaluations of the helpfulness of the reviews The helpfulness vote “Was this review helpful to you?” Helpfulness ratio: “a out of b people found the review itself helpful” 6

Flow of Presentation HypothesizingVerifyingModeling 7

Flow of Presentation Hypothesizing Conformity Individual-bias Brilliant-but-cruel Quality-only VerifyingModeling 8

Hypotheses: Social Mechanisms Well-studied hypotheses for how social effects influence group’s reaction to an opinion The conformity hypothesis The individual-bias hypothesis The brilliant-but-cruel hypothesis The quality-only straw-man hypothesis 9

Hypotheses The 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 hypothesis When a user considers a review, he or she will rate it more highly if it expresses an opinion that he or she agrees with 10

Hypotheses (contd.) The brilliant-but-cruel hypothesis Negative reviewers are perceived as more intelligent, competent, and expert than positive reviewers The Quality-only straw-man hypothesis Helpfulness is being evaluated purely based on the textual content of reviews Non-textual factors are simply correlates of textual quality 11

Flow of Presentation Hypothesizing Verifying Absolute deviation of helpfulness ratio Signed deviation of helpfulness ratio Variance of star rating and helpfulness ratio Making use of plagiarism Modeling 12

Hypotheses Conformity 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 Quality-only Only textual information affects helpfulness evaluation 13

Absolute Deviation from Average  Consistent with conformity hypothesis Strong inverse correlation between the median helpfulness ratio and the absolute deviation Reviews with star rating close to the average gets higher helpfulness ratio 14

Hypotheses Conformity 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 Quality-only Only textual information affects helpfulness evaluation 15

Signed Deviation from Average Not consistent with brilliant- but-cruel hypothesis There is tendency towards positivity Black lines should not be sloped that way if it is valid hypothesis 16

Hypotheses Conformity 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 Quality-only Only textual information affects helpfulness evaluation 17

Addressing Individual-bias Effects It is hard to distinguish between the conformity and the individual-bias hypothesis We need to examine cases in which individual people’s opinions do not come from exactly the same distribution Cases in which there is high variance in star ratings Otherwise conformity and individual-bias are indistinguishable Everyone has same opinion 18

Variance of Star Rating and Helpfulness Ratio 19 Helpfulness ratio is the highest with reviews of which rating is slightly- above the average Two-humped camel plots: local minimum around average Helpfulness ratio is the highest when star ratings of reviews have average value

Hypotheses Conformity 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 Quality-only Only textual information affects helpfulness evaluation 20

Quality-only hypothesis Possible other methods Human annotation Could be subjective Classification using machine learning methods We cannot guarantee the accuracies of algorithms Plagiarized reviews Almost(not exact) same text same text could be considered as spam reviews Different non-textual information If the quality-only straw man hypothesis holds, helpfulness ratios of documents in each pair should be the same 21

Plagiarism Making use of plagiarism is effective way to control for the effect of review text Definition of plagiarized pair(s) of reviews Two or more reviews of different products With near-complete textual overlap Author takes %70 textual overlap as plagiarism 22

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Experiments with Plagiarism Text quality is not the only explanatory factor Statistically significant difference between the helpfulness ratios of plagiarized pairs 24 The plagiarized reviews with deviation 1 is significantly more helpful than those with deviation 1.5

Hypotheses Conformity 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 Quality-only Only textual information affects helpfulness evaluation 25

Flow of Presentation HypothesizingVerifying Modeling Based on individual bias and mixtures of distributions 26

Authors’ Model Based on individual bias and mixtures of distributions Two distributions: one for positive, one for negative evaluators Balance between positive and negative evaluators: Controversy level: Density function of helpfulness ratios of positive evaluators Gaussian distribution of which average is -centered Density function of helpfulness ratios of negative evaluators Gaussian distribution of which average is -centered 27

Validity of the Model Empirical observation and model generated 28

Conclusion A review’s perceived helpfulness depends not just on its content, but also the relation of its score to other scores The dependence of the score is consistent with a simple and natural model of individual-bias in the presence of a mixture of opinion distributions Directions for further research Variations in the effect can be used to form hypotheses about differences in the collective behaviors of the underlying populations It would be interesting to consider social feedback mechanisms that might be capable of modifying the effects authors observed here Considering possible outcomes of design problem for systems enabling the expression and dissemination of opinions 29

Discussions So, how can we use this? In which cases would this information be helpful? Available information is very limited Star ratings Helpfulness ratios Conclusion is rather trivial Does not present new discoveries 30