Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews

Pan, Yue; Zhang, Jason Q.

doi:10.1016/j.jretai.2011.05.002

Cited by 481 publications

(362 citation statements)

References 56 publications

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“…Specifically, the goal of the study is to examine the joint effect of the message length of a review (word count) together with reviewer characteristics and the patterns of the review on the utility of the review. While prior studies have suggested that there is a positive correlation between word count and the perceived helpfulness of a review [52,77], the results of this study point out that the association between word count and helpfulness is valid only in reviews with 144 or less words [82]. The hypothesis results of this study are listed in table 5.…”

Section: Categorymentioning

confidence: 58%

“…That study examined the influence of other nearby reviews on the perceived helpfulness of the review itself. The results show that, whether or not the reviews are being positive or negative, consistent reviews are more helpful than inconsistent ones, which was not the case in prior studies [76,77]. For example, Scholz and Dorner [78] found that positive reviews achieve better helpfulness scores than negative ones.…”

Section: Categorymentioning

confidence: 59%

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A Survey of Quality Prediction of Product Reviews

Almagrabi¹,

Malibari²,

McNaught³

2015

ijacsa

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Abstract-With the help of Web-2.0, the Internet offers a vast amount of reviews on many topics and in different domains. This has led to an explosive growth of product reviews and customer feedback, which presents the problem of how to handle the abundant volume of data. It is an expensive and time-consuming task to analyze this huge content of opinions. Therefore, the need for automated sentiment analysis systems is vital. However, these systems encounter many challenges; assessing the content quality of the posted opinions is an important area of study that is related to sentiment analysis. Currently, review helpfulness is assessed manually; however the task of automatically assessing it has gained more attention in recent years. This paper provides a survey of approaches to the challenge of identifying the content quality of product reviews.

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Section: Categorymentioning

confidence: 58%

Section: Categorymentioning

confidence: 59%

A Survey of Quality Prediction of Product Reviews

Almagrabi¹,

Malibari²,

McNaught³

2015

ijacsa

View full text Add to dashboard Cite

show abstract

“…Prior research suggests that when users browse reviews, they could be tempted to trust positive entries that are generally abundant [1,2,53]. However, not every positive review is necessarily authentic [7,11].…”

Section: Discussionmentioning

confidence: 99%

“…In particular, they are often inclined to trust positive reviews, which are meant to applaud products and services, as confirming evidence before making a choice [1]. Furthermore, positive reviews are usually more abundant in review websites than those with either negative or mixed opinions [2].…”

Section: Introductionmentioning

confidence: 99%

Dissecting Genuine and Deceptive Kudos: The Case of Online Hotel Reviews

Banerjee¹,

Chua²

2014

SpecialIssue

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Abstract-As users continue to rely on online hotel reviews for making purchase decisions, the trend of posting deceptive reviews to heap praises and kudos is gradually becoming a wellestablished e-business malpractice. Conceivably, it is not trivial for users to distinguish between genuine and deceptive kudos in reviews. Hence, this paper identifies three linguistic cues that could offer telltale signs to distinguish between genuine and deceptive reviews. These linguistic cues include readability, genre and writing style. Drawing data from a publicly available secondary dataset, results indicate that readability and writing style of reviews offer useful clues to distinguish between genuine and deceptive reviews. Specifically, genuine reviews could be more readable and less hyperbolic compared with deceptive entries. With respect to review genre however, the differences were largely blurred. The implications of the findings for theory and practice are highlighted.

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“…In an examination of the innovativeness of the reviewer, it was found that the relationship of reviewer and perceived helpfulness was curvilinear (Pan & Zhang 2011). Research has also assessed the identification of which raters are the most influential (Xu et al 2012).…”

Section: Evidence Of Impact Of Ugcmentioning

confidence: 99%

If we Build it will they Stay?: User Generated Content and Website Effectiveness

Chiagouris¹,

Williams²

2014

JMM

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There has been increased interest in the dimensions and impact of User Generated Content (UGC). Research has most often focused on online ratings as the most prominent form of UGC in terms of evaluating its impact. Other forms of UGC have not been the focus of the consequences of UGC. This study examined a variety of forms of UGC to determine the relationship between the presence of UGC on a brand sponsored website and website effectiveness. The results indicate that the presence of UGC is positively related to key website effectiveness measures, to include page views, time on the site and bounce rate.

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Born Unequal: A Study of the Helpfulness of User-Generated Product Reviews

Cited by 481 publications

References 56 publications

A Survey of Quality Prediction of Product Reviews

A Survey of Quality Prediction of Product Reviews

Dissecting Genuine and Deceptive Kudos: The Case of Online Hotel Reviews

If we Build it will they Stay?: User Generated Content and Website Effectiveness

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