55 research outputs found

    Identification of Opinion Spammers using Reviewer Reputation and Clustering Analysis

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    Online reviews have increasingly become a very important resource before making a purchasing decisions. Unfortunately, malicious sellers try to game the system by hiring a person or team (which is called spammers) to fabricate fake reviews to improve their reputation.Existing methods mainly take the problem as a general binary classification or focus on some heuristic rules. However, supervised learning methods relies heavily on a large number of labeled examples of deceptive and truthful opinions by domain experts, and most of features mentioned in the heuristic strategy ignore the characteristic of the group organization among spammers. In this paper, an effective method of identifying opinion spammers is proposed. Firstly, suspected spammers are detected by means of unsupervised learning based on reviewerโ€™s reputation. We believe that the reviewerโ€™s reputation has a direct relation with the quality of reviews. Generally, review written by user with lower reputation, shows lower quality and higher possibility to be fake. Therefore, the model assigns reputation score to each reviewer wherein the content based factors and activeness of reviewers are employed efficiently. On basis of all suspected spammers, k-center clustering algorithm is performed to further spot the spammers based on the observation of burst of review release time. Experimental results on Amazonโ€™s dataset are encouraging and indicate that our approach poses high accuracy and recall, and good performance is achieved

    Opinion spam detection: using multi-iterative graph-based model

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    The demand to detect opinionated spam, using opinion mining applications to prevent their damaging effects on e-commerce reputations is on the rise in many business sectors globally. The existing spam detection techniques in use nowadays, only consider one or two types of spam entities such as review, reviewer, group of reviewers, and product. Besides, they use a limited number of features related to behaviour, content and the relation of entities which reduces the detection's accuracy. Accordingly, these techniques mostly exploit synthetic datasets to analyse their model and are not able to be applied in the context of the real-world environment. As such, a novel graph-based model called โ€œMulti-iterative Graph-based opinion Spam Detectionโ€ (MGSD) in which all various types of entities are considered simultaneously within a unified structure is proposed. Using this approach, the model reveals both implicit (i.e., similar entity's) and explicit (i.e., different entitiesโ€™) relationships. The MGSD model is able to evaluate the โ€˜spamicityโ€™ effects of entities more efficiently given it applies a novel multi-iterative algorithm which considers different sets of factors to update the spamicity score of entities. To enhance the accuracy of the MGSD detection model, a higher number of existing weighted features along with the novel proposed features from different categories were selected using a combination of feature fusion techniques and machine learning (ML) algorithms. The MGSD model can also be generalised and applied in various opinionated documents due to employing domain independent features. The output of the MGSD model showed that our feature selection and feature fusion techniques showed a remarkable improvement in detecting spam. The findings of this study showed that MGSD could improve the accuracy of state-of-the-art ML and graph-based techniques by around 5.6% and 4.8%, respectively, also achieving an accuracy of 93% for the detection of spam detection in our synthetic crowdsourced dataset and 95.3% for Ott's crowdsourced dataset

    A Study on: Opinion/Review Spam Detection

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    The most common mode for consumers to express their level of satisfaction with their purchases is through online ratings, which we can refer as Online Review System. Network analysis has recently gained a lot of attention because of the arrival and the increasing attractiveness of social sites, such as blogs, social networking applications, micro blogging, or customer review sites. Online review systems plays an important part in affecting consumers' actions and decision making, and therefore attracting many spammers to insert fake feedback or reviews in order to manipulate review content and ratings. Malicious users misuse the review website and post untrustworthy, low quality, or sometimes fake opinions, which are referred as Spam Reviews. In this study, we aim at providing an efficient method to identify spam reviews and to filter out the spam content

    ์†Œ์…œ ๋„คํŠธ์›Œํฌ์™€ ์ด์ปค๋จธ์Šค ํ”Œ๋žซํผ์—์„œ์˜ ์ž ์žฌ ๋„คํŠธ์›Œํฌ ๋งˆ์ด๋‹

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2023. 2. ๊ถŒํƒœ๊ฒฝ.์›น ๊ธฐ๋ฐ˜ ์„œ๋น„์Šค์˜ ํญ๋ฐœ์ ์ธ ๋ฐœ๋‹ฌ๋กœ ์‚ฌ์šฉ์ž๋“ค์€ ์˜จ๋ผ์ธ ์ƒ์—์„œ ํญ๋„“๊ฒŒ ์—ฐ๊ฒฐ๋˜๊ณ  ์žˆ๋‹ค. ์˜จ๋ผ์ธ ํ”Œ๋žซํผ ์ƒ์—์„œ, ์‚ฌ์šฉ์ž๋“ค์€ ์„œ๋กœ์—๊ฒŒ ์˜ํ–ฅ์„ ์ฃผ๊ณ ๋ฐ›์œผ๋ฉฐ ์˜์‚ฌ ๊ฒฐ์ •์— ๊ทธ๋“ค์˜ ๊ฒฝํ—˜๊ณผ ์˜๊ฒฌ์„ ๋ฐ˜์˜ํ•˜๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์ธ๋‹ค. ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์—์„œ๋Š” ๋Œ€ํ‘œ์ ์ธ ์˜จ๋ผ์ธ ํ”Œ๋žซํผ์ธ ์†Œ์…œ ๋„คํŠธ์›Œํฌ ์„œ๋น„์Šค์™€ ์ด์ปค๋จธ์Šค ํ”Œ๋žซํผ์—์„œ์˜ ์‚ฌ์šฉ์ž ํ–‰๋™์— ๋Œ€ํ•ด ์—ฐ๊ตฌํ•˜์˜€๋‹ค. ์˜จ๋ผ์ธ ํ”Œ๋žซํผ์—์„œ์˜ ์‚ฌ์šฉ์ž ํ–‰๋™์€ ์‚ฌ์šฉ์ž์™€ ํ”Œ๋žซํผ ๊ตฌ์„ฑ ์š”์†Œ ๊ฐ„์˜ ๊ด€๊ณ„๋กœ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๋‹ค. ์‚ฌ์šฉ์ž์˜ ๊ตฌ๋งค๋Š” ์‚ฌ์šฉ์ž์™€ ์ƒํ’ˆ ๊ฐ„์˜ ๊ด€๊ณ„๋กœ, ์‚ฌ์šฉ์ž์˜ ์ฒดํฌ์ธ์€ ์‚ฌ์šฉ์ž์™€ ์žฅ์†Œ ๊ฐ„์˜ ๊ด€๊ณ„๋กœ ๋‚˜ํƒ€๋‚ด์ง„๋‹ค. ์—ฌ๊ธฐ์— ํ–‰๋™์˜ ์‹œ๊ฐ„๊ณผ ๋ ˆ์ดํŒ…, ํƒœ๊ทธ ๋“ฑ์˜ ์ •๋ณด๊ฐ€ ํฌํ•จ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‘ ํ”Œ๋žซํผ์—์„œ ์ •์˜๋œ ์‚ฌ์šฉ์ž์˜ ํ–‰๋™ ๊ทธ๋ž˜ํ”„์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ž ์žฌ ๋„คํŠธ์›Œํฌ๋ฅผ ํŒŒ์•…ํ•˜๋Š” ์—ฐ๊ตฌ๋ฅผ ์ œ์‹œํ•œ๋‹ค. ์œ„์น˜ ๊ธฐ๋ฐ˜์˜ ์†Œ์…œ ๋„คํŠธ์›Œํฌ ์„œ๋น„์Šค์˜ ๊ฒฝ์šฐ ํŠน์ • ์žฅ์†Œ์— ๋ฐฉ๋ฌธํ•˜๋Š” ์ฒดํฌ์ธ ํ˜•์‹์œผ๋กœ ๋งŽ์€ ํฌ์ŠคํŠธ๊ฐ€ ๋งŒ๋“ค์–ด์ง€๋Š”๋ฐ, ์‚ฌ์šฉ์ž์˜ ์žฅ์†Œ ๋ฐฉ๋ฌธ์€ ์‚ฌ์šฉ์ž ๊ฐ„์— ์‚ฌ์ „์— ์กด์žฌํ•˜๋Š” ์นœ๊ตฌ ๊ด€๊ณ„์— ์˜ํ•ด ์˜ํ–ฅ์„ ํฌ๊ฒŒ ๋ฐ›๋Š”๋‹ค. ์‚ฌ์šฉ์ž ํ™œ๋™ ๋„คํŠธ์›Œํฌ์˜ ์ €๋ณ€์— ์ž ์žฌ๋œ ์‚ฌ์šฉ์ž ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ํŒŒ์•…ํ•˜๋Š” ๊ฒƒ์€ ํ™œ๋™ ์˜ˆ์ธก์— ๋„์›€์ด ๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, ์ด๋ฅผ ์œ„ํ•ด ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋น„์ง€๋„ํ•™์Šต ๊ธฐ๋ฐ˜์œผ๋กœ ํ™œ๋™ ๋„คํŠธ์›Œํฌ๋กœ๋ถ€ํ„ฐ ์‚ฌ์šฉ์ž ๊ฐ„ ์‚ฌํšŒ์  ๊ด€๊ณ„๋ฅผ ์ถ”์ถœํ•˜๋Š” ์—ฐ๊ตฌ๋ฅผ ์ œ์•ˆํ•˜์˜€๋‹ค. ๊ธฐ์กด์— ์—ฐ๊ตฌ๋˜์—ˆ๋˜ ๋ฐฉ๋ฒ•๋“ค์€ ๋‘ ์‚ฌ์šฉ์ž๊ฐ€ ๋™์‹œ์— ๋ฐฉ๋ฌธํ•˜๋Š” ํ–‰์œ„์ธ co-visitation์„ ์ค‘์ ์ ์œผ๋กœ ๊ณ ๋ คํ•˜์—ฌ ์‚ฌ์šฉ์ž ๊ฐ„์˜ ๊ด€๊ณ„๋ฅผ ์˜ˆ์ธกํ•˜๊ฑฐ๋‚˜, ๋„คํŠธ์›Œํฌ ์ž„๋ฒ ๋”ฉ ๋˜๋Š” ๊ทธ๋ž˜ํ”„ ์‹ ๊ฒฝ๋ง(GNN)์„ ์‚ฌ์šฉํ•˜์—ฌ ํ‘œํ˜„ ํ•™์Šต์„ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ์ด๋Ÿฌํ•œ ์ ‘๊ทผ ๋ฐฉ์‹์€ ์ฃผ๊ธฐ์ ์ธ ๋ฐฉ๋ฌธ์ด๋‚˜ ์žฅ๊ฑฐ๋ฆฌ ์ด๋™ ๋“ฑ์œผ๋กœ ๋Œ€ํ‘œ๋˜๋Š” ์‚ฌ์šฉ์ž์˜ ํ–‰๋™ ํŒจํ„ด์„ ์ž˜ ํฌ์ฐฉํ•˜์ง€ ๋ชปํ•œ๋‹ค. ํ–‰๋™ ํŒจํ„ด์„ ๋” ์ž˜ ํ•™์Šตํ•˜๊ธฐ ์œ„ํ•ด, ANES๋Š” ์‚ฌ์šฉ์ž ์ปจํ…์ŠคํŠธ ๋‚ด์—์„œ ์‚ฌ์šฉ์ž์™€ ๊ด€์‹ฌ ์ง€์ (POI) ๊ฐ„์˜ ์ธก๋ฉด(Aspect) ์ง€ํ–ฅ ๊ด€๊ณ„๋ฅผ ํ•™์Šตํ•œ๋‹ค. ANES๋Š” User-POI ์ด๋ถ„ ๊ทธ๋ž˜ํ”„์˜ ๊ตฌ์กฐ์—์„œ ์‚ฌ์šฉ์ž์˜ ํ–‰๋™์„ ์—ฌ๋Ÿฌ ๊ฐœ์˜ ์ธก๋ฉด์œผ๋กœ ๋‚˜๋ˆ„๊ณ , ๊ฐ๊ฐ์˜ ๊ด€๊ณ„๋ฅผ ๊ณ ๋ คํ•˜์—ฌ ํ–‰๋™ ํŒจํ„ด์„ ์ถ”์ถœํ•˜๋Š” ์ตœ์ดˆ์˜ ๋น„์ง€๋„ํ•™์Šต ๊ธฐ๋ฐ˜ ์ ‘๊ทผ ๋ฐฉ์‹์ด๋‹ค. ์‹ค์ œ LBSN ๋ฐ์ดํ„ฐ์—์„œ ์ˆ˜ํ–‰๋œ ๊ด‘๋ฒ”์œ„ํ•œ ์‹คํ—˜์—์„œ, ANES๋Š” ๊ธฐ์กด์— ์ œ์•ˆ๋˜์—ˆ๋˜ ๊ธฐ๋ฒ•๋“ค๋ณด๋‹ค ๋†’์€ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ค€๋‹ค. ์œ„์น˜ ๊ธฐ๋ฐ˜ ์†Œ์…œ ๋„คํŠธ์›Œํฌ์™€๋Š” ๋‹ค๋ฅด๊ฒŒ, ์ด์ปค๋จธ์Šค์˜ ๋ฆฌ๋ทฐ ์‹œ์Šคํ…œ์—์„œ๋Š” ์‚ฌ์šฉ์ž๋“ค์ด ๋Šฅ๋™์ ์ธ ํŒ”๋กœ์šฐ/ํŒ”๋กœ์ž‰ ๋“ฑ์˜ ํ–‰์œ„๋ฅผ ์ˆ˜ํ–‰ํ•˜์ง€ ์•Š๊ณ ๋„ ํ”Œ๋žซํผ์— ์˜ํ•ด ์„œ๋กœ์˜ ์ •๋ณด๋ฅผ ์ฃผ๊ณ ๋ฐ›๊ณ  ์˜ํ–ฅ๋ ฅ์„ ํ–‰์‚ฌํ•˜๊ฒŒ ๋œ๋‹ค. ์ด์™€ ๊ฐ™์€ ์‚ฌ์šฉ์ž๋“ค์˜ ํ–‰๋™ ํŠน์„ฑ์€ ๋ฆฌ๋ทฐ ์ŠคํŒธ์— ์˜ํ•ด ์‰ฝ๊ฒŒ ์•…์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ฆฌ๋ทฐ ์ŠคํŒธ์€ ์‹ค์ œ ์‚ฌ์šฉ์ž์˜ ์˜๊ฒฌ์„ ์ˆจ๊ธฐ๊ณ  ํ‰์ ์„ ์กฐ์ž‘ํ•˜์—ฌ ์ž˜๋ชป๋œ ์ •๋ณด๋ฅผ ์ „๋‹ฌํ•˜๋Š” ๋ฐฉ์‹์œผ๋กœ ์ด๋ฃจ์–ด์ง„๋‹ค. ๋‚˜๋Š” ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด ์‚ฌ์šฉ์ž ๋ฆฌ๋ทฐ ๋ฐ์ดํ„ฐ์—์„œ ์‚ฌ์šฉ์ž ๊ฐ„ ์‚ฌ์ „ ๊ณต๋ชจ์„ฑ(Collusiveness)์˜ ๊ฐ€๋Šฅ์„ฑ์„ ์ฐพ๊ณ , ์ด๋ฅผ ์ŠคํŒธ ํƒ์ง€์— ํ™œ์šฉํ•œ ๋ฐฉ๋ฒ•์ธ SC-Com์„ ์ œ์•ˆํ•œ๋‹ค. SC-Com์€ ํ–‰๋™์˜ ๊ณต๋ชจ์„ฑ์œผ๋กœ๋ถ€ํ„ฐ ์‚ฌ์šฉ์ž ๊ฐ„ ๊ณต๋ชจ ์ ์ˆ˜๋ฅผ ๊ณ„์‚ฐํ•˜๊ณ  ํ•ด๋‹น ์ ์ˆ˜๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ „์ฒด ์‚ฌ์šฉ์ž๋ฅผ ์œ ์‚ฌํ•œ ์‚ฌ์šฉ์ž๋“ค์˜ ์ปค๋ฎค๋‹ˆํ‹ฐ๋กœ ๋ถ„๋ฅ˜ํ•œ๋‹ค. ๊ทธ ํ›„ ์ŠคํŒธ ์œ ์ €์™€ ์ผ๋ฐ˜ ์œ ์ €๋ฅผ ๊ตฌ๋ณ„ํ•˜๋Š” ๋ฐ์— ์ค‘์š”ํ•œ ๊ทธ๋ž˜ํ”„ ๊ธฐ๋ฐ˜์˜ ํŠน์ง•์„ ์ถ”์ถœํ•˜์—ฌ ๊ฐ๋… ํ•™์Šต ๊ธฐ๋ฐ˜์˜ ๋ถ„๋ฅ˜๊ธฐ์˜ ์ž…๋ ฅ ๋ฐ์ดํ„ฐ๋กœ ํ™œ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ์ œ์‹œํ•œ๋‹ค. SC-Com์€ ๊ณต๋ชจ์„ฑ์„ ๊ฐ–๋Š” ์ŠคํŒธ ์œ ์ €์˜ ์ง‘ํ•ฉ์„ ํšจ๊ณผ์ ์œผ๋กœ ํƒ์ง€ํ•œ๋‹ค. ์‹ค์ œ ๋ฐ์ดํ„ฐ์…‹์„ ์ด์šฉํ•œ ์‹คํ—˜์—์„œ, SC-Com์€ ๊ธฐ์กด ๋…ผ๋ฌธ๋“ค ๋Œ€๋น„ ์ŠคํŒธ ํƒ์ง€์— ๋›ฐ์–ด๋‚œ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์ฃผ์—ˆ๋‹ค. ์œ„ ๋…ผ๋ฌธ์—์„œ ๋‹ค์–‘ํ•œ ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•ด ์—ฐ๊ตฌ๋œ ์•”์‹œ์  ์—ฐ๊ฒฐ๋ง ํƒ์ง€ ๋ชจ๋ธ์€ ๋ ˆ์ด๋ธ”์ด ์—†๋Š” ๋ฐ์ดํ„ฐ์— ๋Œ€ํ•ด์„œ๋„ ์‚ฌ์ „์— ์—ฐ๊ฒฐ๋˜์—ˆ์„ ๊ฐ€๋Šฅ์„ฑ์ด ๋†’์€ ์‚ฌ์šฉ์ž๋“ค์„ ์˜ˆ์ธกํ•˜๋ฏ€๋กœ, ์‹ค์‹œ๊ฐ„ ์œ„์น˜ ๋ฐ์ดํ„ฐ๋‚˜, ์•ฑ ์‚ฌ์šฉ ๋ฐ์ดํ„ฐ ๋“ฑ์˜ ๋‹ค์–‘ํ•œ ๋ฐ์ดํ„ฐ์—์„œ ํ™œ์šฉํ•  ์ˆ˜ ์žˆ๋Š” ์œ ์šฉํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜์—ฌ ๊ด‘๊ณ  ์ถ”์ฒœ ์‹œ์Šคํ…œ์ด๋‚˜, ์•…์„ฑ ์œ ์ € ํƒ์ง€ ๋“ฑ์˜ ๋ถ„์•ผ์—์„œ ๊ธฐ์—ฌํ•  ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€ํ•œ๋‹ค.Following the exploding usage on online services, people are connected with each other more broadly and widely. In online platforms, people influence each other, and have tendency to reflect their opinions in decision-making. Social Network Services (SNSs) and E-commerce are typical example of online platforms. User behaviors in online platforms can be defined as relation between user and platform components. A user's purchase is a relationship between a user and a product, and a user's check-in is a relationship between a user and a place. Here, information such as action time, rating, tag, etc. may be included. In many studies, platform user behavior is represented in graph form. At this time, the elements constituting the nodes of the graph are composed of objects such as users and products and places within the platform, and the interaction between the platform elements and the user can be expressed as two nodes being connected. In this study, I present studies to identify potential networks that affect the user's behavior graph defined on the two platforms. In ANES, I focus on representation learning for social link inference based on user trajectory data. While traditional methods predict relations between users by considering hand-crafted features, recent studies first perform representation learning using network/node embedding or graph neural networks (GNNs) for downstream tasks such as node classification and link prediction. However, those approaches fail to capture behavioral patterns of individuals ingrained in periodical visits or long-distance movements. To better learn behavioral patterns, this paper proposes a novel scheme called ANES (Aspect-oriented Network Embedding for Social link inference). ANES learns aspect-oriented relations between users and Point-of-Interests (POIs) within their contexts. ANES is the first approach that extracts the complex behavioral pattern of users from both trajectory data and the structure of User-POI bipartite graphs. Extensive experiments on several real-world datasets show that ANES outperforms state-of-the-art baselines. In contrast to active social networks, people are connected to other users regardless of their intentions in some platforms, such as online shopping websites and restaurant review sites. They do not have any information about each other in advance, and they only have a common point which is that they have visited or have planned to visit same place or purchase a product. Interestingly, users have tendency to be influenced by the review data on their purchase intentions. Unfortunately, this instinct is easily exploited by opinion spammers. In SC-Com, I focus on opinion spam detection in online shopping services. In many cases, my decision-making process is closely related to online reviews. However, there have been threats of opinion spams by hired reviewers increasingly, which aim to mislead potential customers by hiding genuine consumers opinions. Opinion spams should be filed up collectively to falsify true information. Fortunately, I propose the way to spot the possibility to detect them from their collusiveness. In this paper, I propose SC-Com, an optimized collusive community detection framework. It constructs the graph of reviewers from the collusiveness of behavior and divides a graph by communities based on their mutual suspiciousness. After that, I extract community-based and temporal abnormality features which are critical to discriminate spammers from other genuine users. I show that my method detects collusive opinion spam reviewers effectively and precisely from their collective behavioral patterns. In the real-world dataset, my approach showed prominent performance while only considering primary data such as time and ratings. These implicit network inference models studied on various data in this thesis predicts users who are likely to be pre-connected to unlabeled data, so it is expected to contribute to areas such as advertising recommendation systems and malicious user detection by providing useful information.Chapter 1 Introduction 1 Chapter 2 Social link Inference in Location-based check-in data 5 2.1 Background 5 2.2 Related Work 12 2.3 Location-based Social Network Service Data 15 2.4 Aspect-wise Graph Decomposition 18 2.5 Aspect-wise Graph learning 19 2.6 Inferring Social Relation from User Representation 21 2.7 Performance Analysis 23 2.8 Discussion and Implications 26 2.9 Summary 34 Chapter 3 Detecting collusiveness from reviews in Online platforms and its application 35 3.1 Background 35 3.2 Related Work 39 3.3 Online Review Data 43 3.4 Collusive Graph Projection 44 3.5 Reviewer Community Detection 47 3.6 Review Community feature extraction and spammer detection 51 3.7 Performance Analysis 53 3.8 Discussion and Implications 55 3.9 Summary 62 Chapter 4 Conclusion 63๋ฐ•

    Fake review detection using time series

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    Todayโ€™s e-commerce is highly depended on online customersโ€™ reviews posted in opinion sharing websites that are growing incredibly. These reviews are important not only effect on potential customersโ€™ purchase decision but also for manufacturers and business holders to reshape and customize their products and manage competition with rivals throughout the market place. Moreover opinion mining techniques that analyze customer reviews obtained from opinion sharing websites for different purposes could not reveal accurate results for combination of spam reviews and truthful reviews in datasets. Thus employing review spam detection techniques in review websites are highly essential in order to provide reliable resources for customers, manufacturers and researchers. This study aims to detect spam reviews using time series. To achieve this, the novel proposed method detects suspicious time intervals with high number of reviews. Then a combination of three features, i.e. rating of reviews, similarity percentage of review contexts and number of other reviews written by the reviewer of current review, will be used to score each review. Finally a threshold defined for total scores assigned to reviews will be the border line between spam and genuine reviews. Evaluation of obtained results reveals that the proposed method is highly effective in distinguishing spam and non-spam reviews. Furthermore combination of all features used in this research exposed the best results. This fact represents the effectiveness of each feature

    Review spam detection via temporal pattern discovery

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    Online reviews play a crucial role in todayโ€™s electronic com-merce. It is desirable for a customer to read reviews of products or stores before making the decision of what or from where to buy. Due to the pervasive spam reviews, customers can be misled to buy low-quality products, while decent stores can be defamed by malicious reviews. We ob-serve that, in reality, a great portion (> 90 % in the data we study) of the reviewers write only one review (singleton re-view). These reviews are so enormous in number that they can almost determine a storeโ€™s rating and impression. How-ever, existing methods did not examine this larger part of the reviews. Are most of these singleton reviews truthful ones? If not, how to detect spam reviews in singleton reviews? We call this problem singleton review spam detection. To address this problem, we observe that the normal re-viewers โ€™ arrival pattern is stable and uncorrelated to their rating pattern temporally. In contrast, spam attacks are usually bursty and either positively or negatively correlated to the rating. Thus, we propose to detect such attacks via unusually correlated temporal patterns. We identify and construct multidimensional time series based on aggregate statistics, in order to depict and mine such correlations. In this way, the singleton review spam detection problem is mapped to a abnormally correlated pattern detection prob-lem. We propose a hierarchical algorithm to robustly detect the time windows where such attacks are likely to have hap-pened. The algorithm also pinpoints such windows in dif-ferent time resolutions to facilitate faster human inspection. Experimental results show that the proposed method is ef-fective in detecting singleton review attacks. We discover that singleton review is a significant source of spam reviews and largely affects the ratings of online stores
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