Toward a Language Modeling Approach for Consumer Review Spam Detection

Lai, Cheng-Yu; Xu, Kaiquan; Lau, Raymond Y. K.; Li, Y.; Jing, Liping

doi:10.1109/icebe.2010.47

Cited by 52 publications

(26 citation statements)

References 22 publications

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“…Lai et al [4] paper refers to the difficulties of detecting opinion spamming relative to the detection of Web spam and email spam. Spam (fake) reviews look like legitimate reviews, so applying any feature(s) to identify opinion spamming will not be effective.…”

Section: Related Workmentioning

confidence: 99%

SPAR: A system to detect spam in Arabic opinions

Wahsheh

Al-Kabi

Alsmadi³

2013

2013 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)

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The evaluation of the public opinion through websites, social networks, news feedback, etc. is currently getting an extensive research to discover public opinion regarding the current social and political changes in the Middle Eastern countries. However, the level of trust or confidentiality of such public opinion evaluations may have the risk of being spammed. This study aims to detect the spam opinions in the Yahoo!-Maktoob social network. The proposed system reads the opinions and classifies them into one of the following two classes: spam and non-spam opinions, based on a number of features. Each spam opinion categorizes into; high levels spam and low level spam, based on special metrics. While each non-spam opinion is labeled as; positive, negative, or neutral based on the language polarity dictionaries. Those dictionaries include words that can be classified as: positive, negative or neutral. The proposed system adopts machine learning classification technique to perform classification and prediction.

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Section: Related Workmentioning

confidence: 99%

SPAR: A system to detect spam in Arabic opinions

Wahsheh

Al-Kabi

Alsmadi³

2013

2013 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT)

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“…C.L. Lai [4], focused on the development of a novel computational methodology to combat online review spam. Our experimental results confirm that the KL divergence and the probabilistic language modeling based computational model is effective for the detection of untruthful reviews.…”

Section: Literature Surveymentioning

confidence: 99%

“…Review spam is intended to give unfair perspective of a few products so as to impact the consumers' impression of the products by specifically or indirectly or damaging the product's reputation. In [4], it was discovered that 10 to 15% of reviews basically reverberate the prior reviews and may possibly be affected by review spam. Consider Figure 1a which shows a review for product p1 by user "Mr Unhappy".…”

Section: Introductionmentioning

confidence: 99%

Detection of Fake Users Account Based on Review

Dwivedi¹

2018

IJRASET

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An incredible source of gathering the reviews on particular product is different web based shopping destinations where individuals share their reviews on products and their shopping knowledge. Individuals may get through the wrong suppositions known as survey spam. In this manner, for this it is fundamental to distinguish it by a few means. In this paper, presents strategies for recognition of spam users account utilizing highlight extraction and discretization, in mix with EM calculation. Our structure can recognize various spammers by knowing just little arrangement of spammer sets. Proposed technique adequately chooses important highlights and manufacture highlights set to distinguish the spammers. In this paper, we have hindered the clients with counterfeit id or who are anticipated as spammer. Keywords: Review spam, un-truthful reviews, openion spam, rating spam. I. INTRODUCTIONAt the present, there is no quality control for social networking sites and one has having freedom to share their reviews on social networking sites which helps to lead the review spam. And it is a requirement to recognize review spam because most of the users make their decision based on the reviews. This condition mainly arises for various online shopping sites or the sites or hotels also. . In this paper, we focus on spam found in online product review sites usually known as review spam or opinion spam [5,6]. Review spam is intended to give unfair perspective of a few products so as to impact the consumers' impression of the products by specifically or indirectly or damaging the product's reputation. In [4], it was discovered that 10 to 15% of reviews basically reverberate the prior reviews and may possibly be affected by review spam. Consider Figure 1a which shows a review for product p1 by user "Mr Unhappy". The review is very negative with 1-star rating conversely with the high general 4.5 star rating. This review does not cause any caution until we find another exceedingly negative review by a similar user on an alternate product p2 and the two reviews are indistinguishable in content (see Figure 1b). Since indistinguishable review content for difierent products mirrors a solid predisposition or an absence of earnestness, and the user's ratings are exceptionally difierent from the rest, we consider the two reviews liable to be spam and the user liable to be a spammer. Products p1 and p2 have 16 and 80 reviews separately. It isn't clear how much review spam exists in online product review sites however their reality causes a few issues counting unfair treatment of products either freely or in examination with other comparative products. Either under-rating (or "bad mouthing") and over-rating (or "ballot stuffing") affect the business execution of the affected products particularly for review sites that additionally offer purchasing and offering of products. At the point when consumers depend on reviews from spammers to buy products, they could be frustrated by obtained products not meeting their desire, or misconceivi...

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“…We find that singleton review is a huge wellspring of spam reviews and to a great extent influences the appraisals of online stores. C.L.Lai et al [8]. Various reports have demonstrated the seriousness of phony reviews (i.e., spam) presented on different web based business or supposition sharing Web destinations.…”

Section: Spam Detection Methodsmentioning

confidence: 99%

Review on Various Fake Review Detection Techniques on E-Commerce Website

Dwivedi¹

2018

IJRASET

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The advancement of Internet affected huge numbers of our everyday exercises. Web based business is one of the fast development zones in the Internet time. Individuals are anxious to purchase items from online locales like Amazon, eBay, Flipkart and so forth. Online destinations additionally give office to clients to compose review on items they purchase. These reviews help shoppers and merchants for settling on choice on showcasing procedures, and the change of items and administrations. These days' individuals are especially intrigued to peruse reviews before obtaining any item and getting administrations. This makes territories for sentiment spammers to compose counterfeit reviews to elevate or to downgrade the two items and business administrations. This kind of exercises is frequently alluded as Review spam. Therefore recognizing counterfeit reviews has turned out to be more basic issue for clients to settle on better choice on buy and also the merchants to make their items reliable. In this paper a point by point overview is finished utilizing different machine learning strategies for identifying spam and veritable reviews.

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Toward a Language Modeling Approach for Consumer Review Spam Detection

Cited by 52 publications

References 22 publications

SPAR: A system to detect spam in Arabic opinions

SPAR: A system to detect spam in Arabic opinions

Detection of Fake Users Account Based on Review

Review on Various Fake Review Detection Techniques on E-Commerce Website

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