How opinions are received by online communities: a case study on amazon.com helpfulness votes

By Cristian Danescu-Niculescu-Mizil, Gregory Kossinets, Jon M. Kleinberg, Lillian Lee. The Web Conference, 2009

313 citations recorded by the source corpus.

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 ifferent countries.

Subfields: Information Systems, Sociology and Political Science, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/a817c9ea2050965c874ee2238440afa422cb7bbc

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Citations in the corpus

Finding Deceptive Opinion Spam by Any Stretch of the Imagination (2011, methodology): “, 2007), and in the contexts of helpfulness (Danescu-Niculescu-Mizil et al., 2009; Kim et al., 2006; O’Mahony and Smyth, 2009) and credibil-”
How Community Feedback Shapes User Behavior (2014, background): “All rights reserved. the community-level (Danescu-Niculescu-Mizil et al. 2009; Anderson et al. 2012a; Muchnik, Aral, and Taylor 2013; Sipos, Ghosh, and Joachims 2014).”
Semi-Supervised Recognition of Sarcasm in Twitter and Amazon (2010, background): “Following the ‘brilliant-butcruel’ hypothesis (Danescu-Niculescu-Mizil et al., 2009), it can help improve ranking and recommendation systems (Tsur and Rappoport, 2009).”
Helpfulness of Online Product Reviews as Seen by Consumers: Source and Content Features (2013, background): “The first category focuses on the assessment of product review helpfulness from the perspective of the source-based feature [22, 23, 25, 29] by considering the review message believability of the product reviews [46].”
Discovering value from community activity on focused question answering sites: a case study of stack overflow (2012, background): “liberation, voting and explicit user feedback in online com munities [5, 12, 14].”
Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics (2014, unknown): “, deviation from average star rating) (Danescu-Niculescu-Mizil et al. 2009; Kim et al. 2006), review completeness (i.”
In Search of Negativity Bias: An Empirical Study of Perceived Helpfulness of Online Reviews (2013, methodology): “This interesting feature produces aggregated numbers of helpfulness votes which are used as a proxy measure for the perceived value of online reviews by most academic publications (Danescu-Niculescu-Mizil et al., 2009; Mudambi & Schuff, 2010; Wu, van der Heijden, & Korfiatis, 2011).”
ICWSM - A Great Catchy Name: Semi-Supervised Recognition of Sarcastic Sentences in Online Product Reviews (2010, background): “Studies of user preferences suggest that some users find sarcastic reviews biased and less helpful while others prefer reading sarcastic reviews (the 'brilliant-but-cruel' hypothesis (Danescu-Niculescu-Mizil et al. 2009)).”
Estimating the Helpfulness and Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics (2011, background): “A recent stream of research indicates that the helpfulness of a review is also a function of the other submitted reviews [55] and that temporal dynamics can play a role in the perceived helpfulness of a review [25], [56], [57] (e.”
Detecting Information-Dense Texts in Multiple News Domains (2014, background): “Their modern counterparts have instead zeroed in on issues of text style and potential impact, developing methods for deducing how information is conveyed and how information will be perceived by readers (Yu and Hatzivassiloglou 2003; Danescu-Niculescu-Mizil et al. 2009; Cook and Hirst 2013).”
The Enhancing Versus Backfiring Effects of Positive Emotion in Consumer Reviews (2020, methodology): “To index the helpfulness of each review, we followed prior research (e.g., Chen, Dhanasobhon, and Smith 2008; Danescu-Niculescu-Mizil et al. 2009; Mudambi and Schuff 2010) and calculated the proportion of visiting consumers who found that review helpful out of the total number of consumers who…”