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New Technology of Library and Information Service  2014, Vol. 30 Issue (2): 63-71    DOI: 10.11925/infotech.1003-3513.2014.02.09
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Review of Product Review Spams Detection
Nie Hui, Wang Jiajia
School of Information Management, Sun Yat-Sen University, Guangzhou 510006, China
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Abstract  

[Objective] The paper cards existing study about online product review spam, summarizes research status, and puts forward future research direction. [Coverage] Almost 50 papers at home and abroad are searched via review/opinion spam as keywords from CNKI and Google scholar. [Methods] By literature analysis, the concept of product review spam is defined. The research area that review spam study belongs to is specified and key issues and challenges are presented in the paper. [Results] Product review spam refers to the untruthful reviews written for the purpose of inflating or damaging given products excessively or low quality reviews not being able to provide any help to customers. Due to the lack of reliable ground truth label of fake/no-fake review data, the analysis for reviewers' behavior is highlighted since it can be employed to solve the problem of fake review identification effectively if being combined with the features of review contents. [Limitations] Further study should be conducted on the creditability analysis for product review spam combined with fake review identification. [Conclusions] Product review spam detection is a kind of application studies corresponding to review creditability. Not only the review content specific features but also reviewers corresponding features should be fully explored for fake reviews detection. Moreover, the features with significant impact on fake review identification need to be highlighted specifically with the consideration of independence of feature variables.

Key wordsReview spam      Credibility of review      Helpfulness of review     
Received: 15 November 2013      Published: 06 March 2014
:  TP391  

Cite this article:

Nie Hui, Wang Jiajia. Review of Product Review Spams Detection. New Technology of Library and Information Service, 2014, 30(2): 63-71.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.1003-3513.2014.02.09     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2014/V30/I2/63

[1] 中国互联网信息中心. 第32次中国互联网络发展状况统计报告[R/OL].[2013-09-30]. http://www.cnnic.net.cn/hlwfzyj/hlwxzbg/hlwtjbg/201307/t20130717_40664.htm. (China Inte-rnet Network Information Center. The 32nd Statistic Report of China Internet Development State[R/OL].[2013-09-30]. http://www.cnnic.net. cn/hlwfzyj/hlwxzbg/hlwtjbg/201307/t2 0130717_40664.htm.)
[2] 蒋涛, 张彬. Web Spam技术研究综述[J]. 情报探索, 2007(7): 66-68. (Jiang Tao, Zhang Bin. A Survey of Web Spam Technologies[J]. Information Research, 2007(7): 66-68.)
[3] 贾志洋, 崔博文, 王勇刚, 等. 搜索引擎垃圾网页技术分析[J]. 情报探索, 2011(7): 94-96. (Jia Zhiyang, Cui Bowen, Wang Yonggang, et al. Analyzing Technology of Search Engine Spam Web Pages[J]. Information Research, 2011(7): 94-96.)
[4] 王斌, 潘文锋. 基于内容的垃圾邮件过滤技术综述[J]. 中文信息学报, 2004, 19(5): 1-10. (Wang Bin, Pan Wenfeng. A Survey of Content-based Anti-spam Email Filtering[J]. Journal of Chinese Information Processing, 2004, 19(5): 1-10.)
[5] 何建昭, 梁晓诚, 郭红宾. 基于行为模式识别的反垃圾邮件技术[J]. 电脑知识与技术, 2007, 1(4): 957-958. (He Jianzhao, Liang Xiaocheng, Guo Hongbin. Anti-spam Technology Based on Discriminating Action Pattern[J]. Computer Knowledge and Technology, 2007, 1(4): 957-958.)
[6] 李霄, 丁晟春. 垃圾商品评论信息的识别研究[J]. 现代图书情报技术, 2013(1): 63-68. (Li Xiao, Ding Shengchun. Research on Review Spam Recognition[J]. New Technology of Library and Information Service, 2013(1): 63-68.)
[7] Wang G, Xie S H, Liu B, et al. Review Graph Based Online Store Review Spammer Detection[C]. In: Proceedings of the 11th International Conference on Data Mining. Washington, DC, USA: IEEE Computer Society, 2011: 1242-1247.
[8] 邱云飞, 王建坤, 邵良杉, 等. 基于用户行为的产品垃圾评论者检测研究[J]. 计算机工程, 2012, 38(11): 254-257, 261. (Qiu Yunfei, Wang Jiankun, Shao Liangshan, et al. Research on Product Review Spammer Detection Based on Users' Behavior[J]. Computer Engineering, 2012, 38(11): 254-257, 261.)
[9] 孙升芸, 田萱, 何军. 基于评论行为的商品垃圾评论的识别研究[J]. 计算机工程与设计, 2012, 33(11): 4315-4319. (Sun Shengyun, Tian Xuan, He Jun. Research on Product Review Spam Detection Based on Review Behavior[J]. Computer Engineering and Design, 2012, 33(11): 4315-4319.)
[10] 吴敏, 何珑. 融合多特征的产品垃圾评论识别[J]. 微型机与应用, 2012, 31(22): 85-87, 90. (Wu Min, He Long. Fuse Multi-features to Identify Product Review Spam[J]. Microcomputer & Its Applications, 2012, 31(22): 85-87, 90.)
[11] Li F T, Huang M, Yang Y, et al. Learning to Identify Review Spam[C]. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence. AAAI Press, 2011: 2488-2493.
[12] Ott M, Choi Y J, Cardie C, et al. Finding Deceptive Opinion Spam by Any Stretch of the Imagination[C]. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Stroudsburg, PA, USA: Association for Computational Linguistics, 2011: 309-319.
[13] Jindal N, Liu B. Review Spam Detection[C]. In: Proceedings of the 16th International Conference on World Wide Web. New York, NY, USA: ACM, 2007: 1189-1190.
[14] Jindal N, Liu B. Analyzing and Detecting Review Spam[C]. In: Proceedings of the 7th International Conference on Data Mining. Washington, DC, USA: IEEE Computer Society, 2007: 547-552.
[15] Jindal N, Liu B. Opinion Spam and Analysis[C]. In: Proceedings of the 2008 International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2008: 219-230.
[16] 于兵兵. Web Spam检测及网页排序算法的研究[D]. 西安: 西安电子科技大学, 2012. (Yu Bingbing. Research on Web Spam Detection and Web Page Sorting[D]. Xi'an: Xidian University, 2012.)
[17] 铁翠香. 基于信任和感知价值的网络口碑效应研究[D]. 武汉: 华中科技大学, 2011. (Tie Cuixiang. Study on the eWOM Impact: A Mediating Role of Trust and Consumer Perceived Value[D]. Wuhan: Huazhong University of Science and Technology, 2011.)
[18] 李念武, 岳蓉. 网络口碑可信度及其对购买行为之影响的实证研究[J]. 图书情报工作, 2009, 53(22): 123-137. (Li Nianwu, Yue Rong. An Empirical Study on Credibility of Online Word-of-Mouth and Its Effects on Consumers' Purchase Behavior[J]. Library and Information Service, 2009, 53(22): 123-137.)
[19] 凌昌梅. 网络口碑可信度影响因素的实证研究[D]. 北京: 北京邮电大学, 2012. (Ling Changmei. An Empirical Analysis of Factors Influencing the Credibility of Online Word-of-Mouth[D]. Beijing: Beijing University of Posts and Telecommunications, 2012.)
[20] 艾青. 中国网络口碑可信度的影响因素探索研究[D]. 成都: 西南财经大学, 2008. (Ai Qing. The Exploratory Research of Influence Factor for the Credibility of Online Word-of-Mouth[D]. Chengdu: Southwestern University of Finance and Economics, 2008.)
[21] 郭国庆, 陈凯, 何飞. 消费者在线评论可信度的影响因素研究[J]. 当代经济管理, 2010, 32(10): 17-23. (Guo Guoqing, Chen Kai, He Fei. An Empirical Study on the Influence of Perceived Credibility of Online Consumer Reviews[J]. Contemporary Economy & Management, 2010, 32(10): 17-23.)
[22] 宫明亮. 网络口碑传播信任度影响因素研究[D]. 上海: 东华大学, 2011. (Gong Mingliang. The Research on Internet Word of Mouth Dissemination Credibility[D]. Shanghai: Donghua University, 2011.)
[23] 孙春华, 刘业政. 网络口碑信息可信度的实验研究[J]. 财经论丛, 2009(4): 97-102. (Sun Chunhua, Liu Yezheng. An Experimental Study on Message Credibility of eWOM[J]. Collected Essays on Finance and Economics, 2009(4): 97-102.)
[24] 丁学君. 网络环境下口碑信息可信度影响因素的实证研究[J]. 中国经贸导刊, 2012(29): 75-77. (Ding Xuejun. Empirical Study on Influence Factors of Word of Mouth in Internet Environment[J]. China Economic & Trade Herald, 2012(29): 75-77.)
[25] 咸晓静. 基于TAM的网络口碑信任度研究[D]. 上海: 上海交通大学, 2010. (Xian Xiaojing. A Research on the Trust of Electronic Word of Mouth Based on TAM[D]. Shanghai: Shanghai Jiaotong University, 2010.)
[26] 刘逶迤, 逯万辉, 丁晟春. 商品评论信息可信度研究[J]. 情报科学, 2012, 30(10): 1556-1565. (Liu Weiyi, Lu Wanhui, Ding Shengchun. Research on the Credibility of Commodity Reviews Information[J]. Information Science, 2012, 30(10): 1556-1565.)
[27] Kusumasondjaja S, Shanka T, Marchegiani C. Credibility of Online Reviews and Initial Trust: The Roles of Reviewer's Identity and Review Valence[J]. Journal of Vacation Marketing, 2012, 18(3): 185-195.
[28] Pan L Y, Chiou J S. How Much Can You Trust Online Information? Cues for Perceived Trustworthiness of Consumer-generated Online Information[J]. Journal of Interactive Marketing, 2011, 25(2): 67-74.
[29] Qiu L Y, Pang J, Lim K H. Effects of Conflicting Aggregated Rating on eWOM Review Credibility and Diagnosticity: The Moderating Role of Review Valence[J]. Decision Support Systems, 2012, 54(1): 631-643.
[30] Cheung M Y, Luo C, Sia C L, et al. How do People Evaluate Electronic Word-of-Mouth? Informational and Normative Based Determinants of Perceived Credibility of Online Consumer Recommendations in China[C]. In: Proceedings of the 11th Pacific Asia Conference on Information Systems. 2007: 69-73.
[31] Cheung M Y. Do People Believe Electronic Word-of-Mouth?: A Study on Factors Affecting Readers' Perceived Credibility of Online Consumer Reviews[D]. Hongkong: City University of Hong Kong, 2006.
[32] Cheung M Y, Cindy M Y, Sia C L, et al. Is This Review Believable? A Study of Factors Affecting the Credibility of Online Consumer Reviews from an ELM Perspective[J]. Journal of the Association for Information Systems, 2012, 13(8): 618-635.
[33] Lim E P, Nguyen V A, Jindal N, et al. Detecting Product Review Spammers Using Rating Behaviors[C]. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2010: 939-948.
[34] Danescu-Niculescu-Mizil C, Kossinets G, Kleinberg J, et al. How Opinions are Received by Online Communities: A Case Study on Amazon. com Helpfulness Votes[C]. In: Proceedings of the 18th International Conference on World Wide Web. New York, NY, USA: ACM, 2009: 141-150.
[35] 薛博召. 图书领域消费者在线评论的有用性影响因素研究[D]. 哈尔滨: 哈尔滨工业大学, 2011. (Xue Bozhao. Research on Online Impact Factors of Customer Reviews Usefulness Based on Book Reviews Data[D]. Harbin: Harbin Institute of Technology, 2011.)
[36] 廖成林, 蔡春江, 李忆. 电子商务中在线评论有用性影响因素实证研究[J]. 软科学, 2013, 27(5): 46-50. (Liao Chenglin, Cai Chunjiang, Li Yi. An Empirical Study on Influence Factors of Online Reviews' Helpfulness in E-commerce[J]. Soft Science, 2013, 27(5): 46-50.)
[37] Mudambi S M, Schuff D. What Makes a Helpful Online Review? A Study of Customer Reviews on Amazon. com[J]. MIS Quarterly, 2010, 34(1): 185-200.
[38] 严建援, 张丽, 张蕾. 电子商务中在线评论内容对评论有用性影响的实证研究[J]. 情报科学, 2012, 30(5): 713-719. (Yan Jianyuan, Zhang Li, Zhang Lei. An Empirical Study of the Impact of Review Content on Online Reviews Helpfulness in E-commerce[J]. Information Science, 2012, 30(5): 713-719.)
[39] 郝媛媛, 叶强, 李一军. 基于影评数据的在线评论有用性影响因素研究[J]. 管理科学学报, 2010, 13(8): 78-96. (Hao Yuanyuan, Ye Qiang, Li Yijun. Research on Online Impact Factors of Customer Reviews Usefulness Based on Movie Reviews Data[J]. Journal of Management Sciences in China, 2010, 13(8): 78-96.)
[40] 姜巍, 张莉, 戴翼, 等. 面向用户需求获取的在线评论有用性分析[J]. 计算机学报, 2013, 36(1): 119-131. (Jiang Wei, Zhang Li, Dai Yi, et al. Analyzing Helpfulness of Online Reviews for User Requirements Elicitation[J]. Chinese Journal of Computers, 2013, 36(1): 119-131.)
[41] Mukherjee A, Venkataraman V. What Yelp Fake Review Filter Might Be Doing?[C]. In: Proceedings of the 7th International Conference on Weblogs and Social Media. Palo Alto: AAAI Press, 2013: 409-418.
[42] Wu G, Greene D, Smyth B, et al. Distortion as a Validation Criterion in the Identification of Suspicious Reviews[C]. In: Proceedings of the 1st Workshop on Social Media Analytics. New York, NY, USA: ACM, 2010: 10-13.
[43] 陆军, 洪宇, 陆剑江, 等. 基于全局用户意图的评论自动估价方法研究[J]. 中文信息学报, 2012, 26(5): 79-87. (Lu Jun, Hong Yu, Lu Jianjiang, et al. Automatic Reviews Quality Evaluation Based on Global User Intent[J]. Journal of Chinese Information Processing, 2012, 26(5): 79-87.)
[44] Mukherjee A, Kumar A, Liu B, et al. Spotting Opinion Spammers Using Behavioral Footprints[C]. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2013: 632-640.
[45] Jindal N, Liu B, Lim E P. Finding Unusual Review Patterns Using Unexpected Rules[C]. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2010: 1549-1552.
[46] Mukherjee A, Liu B, Wang J H, et al. Detecting Group Review Spam[C]. In: Proceedings of the 20th International Conference Companion on World Wide Web. New York, NY, USA: ACM, 2011: 93-94.
[47] Mukherjee A, Liu B, Glance N. Spotting Fake Reviewer Groups in Consumer Reviews[C]. In: Proceedings of the 21st International Conference on World Wide Web. New York, NY, USA: ACM, 2012: 191-200.
[48] Hu N, Zhang J, Pavlou P A. Overcoming the J-shaped Distribution of Product Reviews[J]. Communications of the ACM, 2009, 52(10): 144-147.
[49] Feng S, Xing L F, Gogar A, et al. Distributional Footprints of Deceptive Product Reviews[C]. In: Proceedings of the 6th International AAAI Conference on Weblogs and Social Media. Palo Alto: AAAI Press, 2013: 98-105.
[50] Fei G, Mukherjee A, Liu B, et al. Exploiting Burstiness in Reviews for Review Spammer Detection[C]. In: Proceedings of the 7th International Conference on Weblogs and Social Media. Palo Alto: AAAI Press, 2013: 175-184.

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