The social aspect of voting for useful reviews

Asher Levi, Osnat Mokryn

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Word-of-mouth is being replaced by online reviews on products and services. To identify the most useful reviews, many web sites enable readers to vote on which reviews they find useful. In this work we use three hypotheses to predict which reviews will be voted useful. The first is that useful reviews induce feelings. The second is that useful reviews are both informative and expressive, thus contain less adjectives while being longer. The third hypothesis is that the reviewer's history can be used as a predictor. We devise impact metrics similar to the scientific metrics for assessing the impact of a scholar, namely h-index, i5 -index. We analyze the performance of our hypotheses over three datasets collected from Yelp and Amazon. Our surprising and robust results show that the only good predictor to the usefulness of a review is the reviewer's impact metrics score. We further devise a regression model that predicts the usefulness rating of each review. To further understand these results we characterize reviewers with high impact metrics scores and show that they write reviews frequently, and that their impact scores increase with time, on average. We suggest the term local celebs for these reviewers, and analyze the conditions for becoming local celebs on sites.

Original languageEnglish
Title of host publicationSocial Computing, Behavioral-Cultural Modeling, and Prediction - 7th International Conference, SBP 2014, Proceedings
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783319055787
StatePublished - 2014
Externally publishedYes
Event7th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2014 - Washington, DC, United States
Duration: 1 Apr 20144 Apr 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8393 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2014
Country/TerritoryUnited States
CityWashington, DC

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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