Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Artificial Intelligence Review
Published in: Artificial Intelligence Review 2/2020

30-03-2019

Deceptive consumer review detection: a survey

Authors: Dushyanthi U. Vidanagama, Thushari P. Silva, Asoka S. Karunananda

Published in: Artificial Intelligence Review | Issue 2/2020

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

Consumer reviews are considered to be of utmost significance in the field of e-commerce, for they have a stronghold in deciding the revenue of a business. When arriving at a purchasing decision, a majority of online consumers rely on reviews since they offer credible means of mining opinions of other consumers regarding a particular product. The trustworthiness of online reviews directly affects a company’s reputation and profitability, which is why certain business owners pay fraudsters to generate deceptive reviews. Such generation of deceptive reviews which manipulate the purchasing decision of consumers is a persistent and harmful issue. Hence, developing methods to assist businesses and consumers by distinguishing between credible reviews and deceptive reviews remains to be a crucial, yet challenging task. In view of that, this paper unravels prominent techniques that have been proposed to solve the issue of deceptive review detection. Accordingly, the primary goal of this paper is to provide an in-depth analysis of current research on detecting deceptive reviews and to identify the characteristics, strengths, and bottlenecks of those methodologies which may need further improvements.
previous article Online AdaBoost-based methods for multiclass problems
next article Attribute reducts of multi-granulation information system

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Literature
Aghakhani H, Machiry A, Nilizadeh S, Kruegel C, Vigna G (2018) Detecting deceptive reviews using generative adversarial networks. In: 1st deep learning and security workshop co-located with the 39th IEEE symposium on security and privacy. arXiv:​1805.​10364 [cs.CR]
Badresiya A, Vohra S, Teraiya J (2014) Performance analysis of supervised techniques for review spam detection. Int J Adv Netw Appl Special Issue:21–24
Banerjee S, Chua A, Kim, J (2015) Using supervised learning to classify authentic and fake online reviews. In: International conference on ubiquitous information management and communication. ACM, New York, pp 1–6
Bhattarai A, Dasgupta D (2012) A self-supervised approach to comment spam detection based on content analysis. In: Nemati HR (ed) Privacy solutions and security frameworks in information protection, 1st edn. IGI Global, pp 1–253
Chavan A, Darekar O, Kulkarni O, Jain Y (2017) Spam reviews detection using Hadoop. Int J Eng Comput Sci 6(2):20320–20323
Christopher S, Rahulnath H (2016) Review authenticity verification using supervised learning and reviewer personality traits. In: 2016 International conference on emerging technological trends
Crawford M, Khoshgoftaar TM, Prusa JD, Richter AN, Najada HA (2015) Survey of review spam detection using machine learning techniques. J Big Data 2(1):2–23CrossRef
Daiyan M, Tiwari S, Alam M (2014) Mining product reviews for spam detection using supervised technique. Int J Emerg Technol Adv Eng 4(8):619–623
Farooq S, Khanday H (2016) Opinion spam detection: a review. Int J Eng Res Dev 12(4):1–8
Feng S, Xing L, Gogar A, Choi Y (2012) Distributional footprints of deceptive product reviews. In: Proceedings of the 6th international AAAI conference on weblogs and social media. Association for the Advancement of Artificial Intelligence, pp 98–105
Hai Z, Zhao P, Cheng P, Yang P, Li X, Li G (2016) Deceptive review spam detection via exploiting task relatedness and unlabeled data. In: Proceedings of the 2016 conference on empirical methods in natural language processing. Association for Computational Linguistics, Texas, pp 1817–1826
Jain G, Manisha, Agarwal B (2016) An overview of RNN and CNN techniques for spam detection in social media. Int J Adv Res Comput Sci Softw Eng 6(10):126–132
Jindal N, Liu B (2007) Analyzing and detecting review spam. In: 7th IEEE international conference on data mining (ICDM 2007), pp 547–552
Jindal N, Liu B (2008) Opinion spam and analysis. In: WSDM. ACM, New York, pp 219–230
Jindal N, Liu B, Lim E (2010) Finding unusual review patterns using unexpected rules. In: Proceedings of the 19th ACM international conference on information and knowledge management—CIKM’10, pp 1549–1552
Kolhe N, Joshi M, Jadhav A, Abhang P (2014) Fake reviewer groups detection system. IOSR J Comput Eng 16(1):06–09
Lai C, Xu K, Lau R, Li Y, Song D (2010a) High-order concept associations mining and inferential language modeling for online review spam detection. In: 2010 IEEE international conference on data mining workshops. IEEE, Sydney, pp 1120–1127
Lai C, Xu K, Lau R, Li Y, Jing L (2010b) Toward a language modeling approach for consumer review spam detection. In: IEEE international conference on e-business engineering. IEEE, New York, pp 1–8
Lau RY, Liao SY, Kwok RCW, Xu K, Xia Y, Li Y (2011) Text mining and probabilistic language modeling for online review spam detecting. ACM Trans Manag Inf Syst 2(4):1–30CrossRef
Li F, Huang M, Yang Y, Zhu X (2011) Learning to identify review spam. In: IJCAI proceedings-international joint conference on artificial intelligence, vol 22(3), pp 2488–2493
Li H, Liu B, Mukherjee A, Shao J (2014) Spotting fake reviews using positive-unlabeled learning. Comput Syst 18(3):467–475
Li H, Chen Z, Mukherjee A, Liu B, Shao J (2015) Analyzing and detecting opinion spam on a large-scale dataset via temporal and spatial patterns. In: Proceedings of the 9th international AAAI conference on web and social media
Li H, Fei G, Wang S, Liu B, Shao W, Mukherjee A, Shao J (2016) Modeling review spam using temporal patterns and co-bursting Behaviors. Preprint. arXiv:​1611.​06625
Li J, Cardie C, Li S (2017) TopicSpam: a topic-model-based approach for spam detection. In: Proceedings of the 51st annual meeting of the association for computational linguistics. Association for Computational Linguistics, Bulgeria, pp 217–221
Lim EP, Nguyen VA, Jindal N, Liu B, Lauw HW (2010) Detecting product review spammers using rating behaviors. In: CIKM’10 proceedings of the 19th ACM international conference on information and knowledge management, pp 939–948
Lin Y, Zhu T, Wang X, Zhang J, Zhou A (2014) Towards online review spam detection. In: Proceedings of the 23rd international conference on world wide web—WWW’14 companion
Mukherjee A, Venkataraman V (2014) Opinion spam detection: an unsupervised approach using generative models. Technical report, UH
Mukherjee A, Liu B, Glance N (2012) Spotting fake reviewer groups in consumer reviews. In: Proceedings of the 21st international conference on world wide web—WWW’12
Mushtaq S, Faisal C, Iqbal K, Shah P, Mehmood I, Akram ABU (2016) Review spam detection using sentiments and novel features. Int J Comput Sci Inf Secur (IJCSIS) 14(10):324–328
Noekhah S, Fouladfar E, Salim N, Ghorashi S, Hozhabri A (2014) A novel approach for opinion spam detection in e-commerce. In: 8th international conference on e-commerce with focus on e-trust. IEEE, Iran
Ott M, Choi Y, Cardie C, Hancock J (2011) Finding deceptive opinion spam by any stretch of the imagination. In: HLT’11 proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies. Association for Computational Linguistics, Oregon, pp 309–319
Peng Q, Zhong M (2014) Detecting spam review through sentiment analysis. Int J Softw Inf 8:2065–2072
Ren Y, Ji D (2017) Neural networks for deceptive opinion spam detection: an empirical study. J Inf Sci 385:213–224CrossRef
Ren Y, Zhang Y (2016) Deceptive opinion spam detection using neural network. In: Proceedings of COLING 2016, the 26th international conference on computational linguistics: technical papers, Japan, pp 140–150
Ren Y, Ji D, Hang H (2014) Positive unlabeled learning for deceptive reviews detection. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp 488–498
Rout J, Singh S, Jena S, Bakshi S (2016) Deceptive review detection using labeled and unlabeled data. Multimed Tools Appl 76(3):3187–3211CrossRef
Rout J, Dalmia A, Choo K, Bakshi S, Jena S (2017) Revisiting semi-supervised learning for online deceptive review detection. IEEE Access 5:1319–1327CrossRef
Santosh KC, Mukherjee A (2016) On the temporal dynamics of opinion spamming: case studies on yelp. In: WWW 2016, pp 369–379
Shehnepoor S, Salehi M, Farahbakhsh R, Crespi N (2017) NetSpam: a network-based spam detection framework for reviews in online social media. IEEE Trans Inf Forensics Secur 12(7):1585–1595CrossRef
Shivagangadhar K, Sagar H, Vanipriya C, Sathyan S (2015) Fraud detection in online reviews using machine learning techniques. Int J Comput Eng Res (IJCER) 5(5):52–56
Shojaee S, Murad M, Azman A, Sharef N, Nadali S (2013) Detecting deceptive reviews using lexical and syntactic features. In: 2013 13th international conference on intelligent systems design and applications, pp 53–58
Singh S (2015) Improved techniques for online review spam detection. MTech, National Institute of Technology, Odisha
Wu G, Greene D, Cunningham P (2010a) Merging multiple criteria to identify suspicious reviews. In: Proceedings of the 4th ACM conference on recommender systems—RecSys’10
Wu G, Greene D, Smyth B, Cunningham P (2010b) Distortion as a validation criterion in the identification of suspicious reviews. In: Proceedings of the 1st workshop on social media analytics—SOMA’10
Wang W, Xie S, Liu B, Yu PS (2011) Review graph based online store review spammer detection. In: 11th IEEE international conference on data mining
Wu T, Liu S, Zhang J, Xiang Y (2017) Twitter spam detection based on deep learning. In: Proceedings of the Australasian computer science week multi-conference on—ACSW’17
Xie S, Wang G, Lin S, Yu P (2012) Review spam detection via temporal pattern discovery. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining—KDD’12
Xu Q, Zhao H (2012) Using deep linguistic features for finding deceptive opinion spam. In: Proceedings of COLING 2012: posters, pp 1341–1350
Xu Y, Shi B, Tian W, Lam W (2015) A unified model for unsupervised opinion spamming detection incorporating text generality. In: IJCAI’15 proceedings of the 24th international conference on artificial intelligence. AAAI Press, Argentina, pp 725–731
Xue H, Li F, Seo H, Pluretti R (2015) Trust-aware review spam detection. In: Trustcom/BigDataSE/ISPA, 2015 IEEE. IEEE, Helsinki, pp 726–733
Metadata
Title
Deceptive consumer review detection: a survey
Authors
Dushyanthi U. Vidanagama
Thushari P. Silva
Asoka S. Karunananda
Publication date
30-03-2019
Publisher
Springer Netherlands
Published in
Artificial Intelligence Review / Issue 2/2020
Print ISSN: 0269-2821
Electronic ISSN: 1573-7462
DOI
https://doi.org/10.1007/s10462-019-09697-5

Other articles of this Issue 2/2020

Artificial Intelligence Review 2/2020 Go to the issue

OriginalPaper

Online AdaBoost-based methods for multiclass problems

OriginalPaper

Expert finding in community question answering: a review

OriginalPaper

A review of unsupervised feature selection methods

OriginalPaper

Covering based multigranulation fuzzy rough sets and corresponding applications

OriginalPaper

Multi-agent system for microgrids: design, optimization and performance

OriginalPaper

A study on features of social recommender systems

Premium Partner

    Image Credits