Review spam detector with rating consistency check

Sharma, Kuldeep; Lin, King-Ip

doi:10.1145/2498328.2500083

Cited by 34 publications

(17 citation statements)

References 11 publications

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“…A burst pattern may not be a good indicator to judge the spam, as a user may be writing reviews for many products. In [30], the authors used temporal based rating-consistency to detect spam and marked abnormally frequent reviewers as spammers. In [31], the authors criticized the temporal approach and considered it unuseful as spammer may post fewer reviews.…”

Section: Related Workmentioning

confidence: 99%

Finding Rotten Eggs: A Review Spam Detection Model using Diverse Feature Sets

2018

KSII TIIS

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Social media enables customers to share their views, opinions and experiences as product reviews. These product reviews facilitate customers in buying quality products. Due to the significance of online reviews, fake reviews, commonly known as spam reviews are generated to mislead the potential customers in decision-making. To cater this issue, review spam detection has become an active research area. Existing studies carried out for review spam detection have exploited feature engineering approach; however limited number of features are considered. This paper proposes a Feature-Centric Model for Review Spam Detection (FMRSD) to detect spam reviews. The proposed model examines a wide range of feature sets including ratings, sentiments, content, and users. The experimentation reveals that the proposed technique outperforms the baseline and provides better results.

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Section: Related Workmentioning

confidence: 99%

Finding Rotten Eggs: A Review Spam Detection Model using Diverse Feature Sets

2018

KSII TIIS

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“…Opinion and sentiment analysis of reviews extract and aggregate positive and negative opinions from product reviews [5]. Researchers study the problem of generating feature-based summ aries of customer reviews of products sold online.…”

Section: It Related Workmentioning

confidence: 99%

“…Given a data set of customer reviews of any particular product, the task involves three subtasks: (1) identifying features of product that customers have discussed or expressed their opinion on. (2) For each feature, identifYing review sentences which gives an opinion (positive or negative) and (3) producing a summary using the discovered information [5].…”

Section: It Related Workmentioning

confidence: 99%

One methodology for spam review detection based on review coherence metrics

Yang

2015

Proceedings of 2015 International Conference on Intelligent Computing and Internet of Things

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“…Second, users' sentiment expressed in reviews should be precisely identified. Although reviews contain the information of star ratings, the ratings and actual emotions may not be totally consistent [21], [22]. For example, one user may write positive feedback like "Great", but only give one-star rating.…”

Section: Introductionmentioning

confidence: 99%

Automating App Review Response Generation

Gao

Zeng

Xia

et al. 2019

2019 34th IEEE/ACM International Conference on Automated Software Engineering (ASE)

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Previous studies showed that replying to a user review usually has a positive effect on the rating that is given by the user to the app. For example, Hassan et al. found that responding to a review increases the chances of a user updating their given rating by up to six times compared to not responding. To alleviate the labor burden in replying to the bulk of user reviews, developers usually adopt a template-based strategy where the templates can express appreciation for using the app or mention the company email address for users to follow up. However, reading a large number of user reviews every day is not an easy task for developers. Thus, there is a need for more automation to help developers respond to user reviews.Addressing the aforementioned need, in this work we propose a novel approach RRGen that automatically generates review responses by learning knowledge relations between reviews and their responses. RRGen explicitly incorporates review attributes, such as user rating and review length, and learns the relations between reviews and corresponding responses in a supervised way from the available training data. Experiments on 58 apps and 309,246 review-response pairs highlight that RRGen outperforms the baselines by at least 67.4% in terms of BLEU-4 (an accuracy measure that is widely used to evaluate dialogue response generation systems). Qualitative analysis also confirms the effectiveness of RRGen in generating relevant and accurate responses.

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Review spam detector with rating consistency check

Cited by 34 publications

References 11 publications

Finding Rotten Eggs: A Review Spam Detection Model using Diverse Feature Sets

Finding Rotten Eggs: A Review Spam Detection Model using Diverse Feature Sets

One methodology for spam review detection based on review coherence metrics

Automating App Review Response Generation

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