TY - GEN
T1 - The social aspect of voting for useful reviews
AU - Levi, Asher
AU - Mokryn, Osnat
PY - 2014
Y1 - 2014
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84958527399&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-05579-4_36
DO - 10.1007/978-3-319-05579-4_36
M3 - Conference contribution
AN - SCOPUS:84958527399
SN - 9783319055787
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 293
EP - 300
BT - Social Computing, Behavioral-Cultural Modeling, and Prediction - 7th International Conference, SBP 2014, Proceedings
PB - Springer Verlag
T2 - 7th International Conference on Social Computing, Behavioral-Cultural Modeling, and Prediction, SBP 2014
Y2 - 1 April 2014 through 4 April 2014
ER -