Manipulation of online reviews: An analysis of ratings, readability, and sentiments

Hu, Nan; Bose, Indranil; Koh, Noi Sian; Liu, Ling

doi:10.1016/j.dss.2011.11.002

Cited by 409 publications

(230 citation statements)

References 19 publications

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“…After all, such entries could result in significant financial gains and fames for www.ijacsa.thesai.org businesses [5,6,20]. Hence, it is not surprising that posting positive deceptive reviews is fast becoming a well-established e-business malpractice [7].…”

Section: Literature Reviewmentioning

confidence: 99%

“…For the purpose of this paper, opinion spamming involves posting deceptive reviews containing fictitious praises and kudos with a deliberate attempt to resemble genuine entries. Such a practice is gradually growing into one of the popular e-business tactics among businesses [5,6,7]. Hence, consumers could be misled while making purchase decisions.…”

Section: Introductionmentioning

confidence: 99%

“…Readability refers to the effort and expertise required on the part of users to comprehend the meaning of reviews [7,8,9]. Genre refers to the degree to which reviews are informative, which in turn, could influence their distribution of part-of-speech (POS) tags [10,11,12].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Banerjee¹,

Chua²

2014

SpecialIssue

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“…Some examples are a Top List of items bought, or a Top Search Items or a List of Recent Items. -Ratings, reviews, and recommendations that consists of understanding how collective information from users can influence others [17]. -User-generated content like blogs, wikis or message boards that consist of extracting some kind of intelligence from contributions by users [24].…”

Section: Related Workmentioning

confidence: 99%

Semantic similarity measurement using historical google search patterns

Martinez‐Gil

Aldana‐Montes

2013

Inf Syst Front

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Computing the similarity between terms (or short text expressions) that have the same meaning but which are not lexicographically similar is a key challenge in the information integration field. The problem is that techniques for textual semantic similarity measurement often fail to deal with words not covered by synonym dictionaries. In this paper, we try to solve this problem by determining the semantic similarity for terms using the knowledge inherent in the search history logs from the Google search engine. To do that, we have designed and evaluated four algorithmic methods for measuring the semantic similarity between terms using their associated history search patterns. These algorithmic methods are: a) frequent co-occurrence of terms in search patterns, b) computation of the relationship between search patterns, c) outlier coincidence on search patterns, and d) forecasting comparisons. We have shown experimentally that some of these methods correlate well with respect to human judgment when evaluating general purpose benchmark datasets, and significantly outperform existing methods when evaluating datasets containing terms that do not usually appear in dictionaries.

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“…However, they are also faced with an increasingly complex world of opinions and possibly new challenges, such as how to determine the credibility of a review. If the reviews are skewed or manipulated in any way, consumers could be left confused and it would lead to suboptimal choices (Hu et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

An Empirical Investigation of Self-Selection Bias and Factors Influencing Review Helpfulness

Bjering¹,

Havro²,

Moen³

2015

IJBM

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This paper build on 1 489194 product reviews from 30 product categories retrieved from Amazon.com. Product categories are classified by use of natural language analysis tools with computing of subjectivity scores reflecting a search/experience product dimension. Results show a distinct effect of self-selection where the average review score gradually decreases. For most products, no undershooting period was observed, even though a limited number of products groups had this development pattern. Review length, verified purchase and use of real names contributed to increasing helpfulness ratings. The results further suggest search products to be more influenced by review length than experience products.

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Manipulation of online reviews: An analysis of ratings, readability, and sentiments

Cited by 409 publications

References 19 publications

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

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

Semantic similarity measurement using historical google search patterns

An Empirical Investigation of Self-Selection Bias and Factors Influencing Review Helpfulness

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