Skip to main content
SpringerLink
Log in

Rule-Based Classifiers for Identifying Fake Reviews in E-commerce: A Deep Learning System

  • Chapter
  • First Online:
Fuzzy, Rough and Intuitionistic Fuzzy Set Approaches for Data Handling

Part of the book series: Forum for Interdisciplinary Mathematics ((FFIM))

Abstract

The way in which people purchase online products or services has greatly improved because of the development of Internet technologies. Customer purchase behavior has also modified as more people write reviews about items procured. As previously mentioned, when buyers intend to acquire products from online e-commerce websites, they often go through the details of the products such as ratings and initial posted reviews, instead of selecting a product based purely on price. Furthermore, they also give preference to the products that have higher score ratings and a high positive feedback. An online product with a higher percentage of positive reviews has a better chance than one which has a higher percentage of negative reviews. Unfortunately, some persons take advantage of the importance and value of posting online reviews by attempting to generate fake reviews, either to increase the promotion of specific products or to deceive customers into purchasing low-quality products. Skillful paid persons called “fraudsters” or “fake reviewers” aim to write deceptive positive and/or negative reviews. However, the problem of fake reviews can financially influence online business companies and primarily might be used to mislead customers into making wrong decisions, purchasing undesired products. Recently, fake reviews detection has captivated the attention of e-commerce, media, and governments. In the present paper, the researchers focus on detecting and classifying the online products reviews into fake or truthful via rules-based classifiers. For that purpose, the proposed methodology contains various phases, namely collection of dataset acquired from the Amazon platform, preprocessing, feature extraction, labeling process performed on extracted features, and finally machine learning as well as deep learning-based models adopted for the task of classification. When comparing the results obtained from the experiments carried out in this research work, it has been observed that the Convolutional Neural Networks incorporated in Bidirectional Long Short-Term Memory (CNN-BiLSTM) technique provides better performance than Random Forest (RF) technique in terms of accuracy metric.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Appseconnect: https://www.appseconnect.com/importance-of-product-reviews-in-ecommerce (2017). Accessed date 8 Sept 2021

  2. Ahmed, H., Traore, I., Saad, S.: Detecting opinion spams and fake news using text classification. Secur. Priv. 1(1), 9 (2018)

    Article  Google Scholar 

  3. Algur, S.P., Ayachit, N.H., Biradar, J.G.: Exponential distribution model for review spam detection. Int. J. Adv. Res. Comput. Sci. 938–947

    Google Scholar 

  4. Alsubari, S.N., Shelke, M.B., Deshmukh, S.N.: Fake reviews identification based on deep computational linguistic. Int. J. Adv. Sci. Technol. 29, 3846–3856 (2020)

    Google Scholar 

  5. Alsubari, S.N., Deshmukh, S.N., Al-Adhaileh, M.H., Alsaade, F.W., Aldhyani, T.H.: Development of integrated neural network model for identification of fake reviews in E-commerce using multidomain datasets. Appl. Bionics Biomech. (2021)

    Google Scholar 

  6. Alsubari, S. N., Deshmukh, S. N., Alqarni, A.A, Alsharif, N.H.T, Aldhyani T.H, Alsaade, F.W, Khalaf, O.I: Data analytics for the identification of fake reviews using supervised learning. Comput, Mater. Contin. 70 (2), 3189–3204 (2022)

    Google Scholar 

  7. Anderson, E.W.: Customer satisfaction and word of mouth. J. Serv. Res. 1(1), 5–17 (1998)

    Article  Google Scholar 

  8. Banerjee, S., Chua, A.Y.: A study of manipulative and authentic negative reviews. In: Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication, p. 76 (2014)

    Google Scholar 

  9. Chen, L., Jiang, T., Li, W., Geng, S., Hussain, S.: Who should pay for online reviews? Design of an online user feedback mechanism. Electron. Commer. Res. Appl. 23, 38–44 (2017)

    Article  Google Scholar 

  10. Chen, Y., Xie, J.: Online consumer review: word-of-mouth as a new element of marketing communication mix. Manag. Sci. 54, 477–491 (2008)

    Article  Google Scholar 

  11. Chen, Y.R., Chen, H.H.: Opinion spam detection in web forum: a real case study. In: Proceedings of the 24th International Conference on World Wide Web, International World Wide Web Conferences Steering Committee (2014)

    Google Scholar 

  12. Floyd, K., Freling, R., Alhoqail, S., Cho, H.Y., Freling, T.: How online product reviews affect retail sales: a meta-analysis. J. Retail. 90(2), 217–232 (2014)

    Article  Google Scholar 

  13. Hajek, P., Barushka, A., Munk, M.: Fake consumer review detection using deep neural networks integrating word embeddings and emotion mining. Neural Comput. Appl. 32(23), 17259–17274 (2020)

    Article  Google Scholar 

  14. https://msdn.microsoft.com/en-us/magazine/mt846470.aspx (2018). Accessed

  15. Hu, N., Bose, I., Gao, Y., Liu, L.: Manipulation in digital word-of-mouth: a reality check for book reviews. Decis. Support Syst. 50(3), 627–635 (2011)

    Article  Google Scholar 

  16. Jindal, N., Liu, B.: Review spam detection. In: Proceedings of the 16th International Conference on World Wide Web, pp. 1189–1190 (2007)

    Google Scholar 

  17. Kamerer, D.: Understanding the Yelp review filter: an exploratory study. First Monday (2014)

    Google Scholar 

  18. Kim, S., Chang, H., Lee, S., Yu, M., Kang, J.: Deep semantic frame-based deceptive opinion spam analysis. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1131–1140 (2015)

    Google Scholar 

  19. Kouzis-Loukas, D.: Learning scappy. Packet Publishing Ltd. England (2016)

    Google Scholar 

  20. Lai, S., Liu, K., He, S., Zhao, J.: How to generate a good word embedding. IEEE Intell. Syst. 31(6), 5–14 (2016)

    Article  Google Scholar 

  21. Lappas, T.: Fake reviews: the malicious perspective. In: Proceedings of the International Conference on Applications of Natural Language Processing and Information Systems, pp. 23–34 (2012)

    Google Scholar 

  22. Liu, B.: Sentiment analysis: mining opinions, sentiments, and emotions. Cambridge University Press, Cambridge, England (2015)

    Book  Google Scholar 

  23. Louppe, G., Wehenkel, L., Sutera, A., Geurts, P.: Understanding variable importances in forests of randomized trees. Adv. Neural. Inf. Process. Syst. 26, 431–439 (2013)

    Google Scholar 

  24. Luca, M., Zervas, G.: Fake it till you make it: reputation, competition, and Yelp review fraud. Manage. Sci. 62(12), 3412–3427 (2016)

    Article  Google Scholar 

  25. Mayzlin, D.: Promotional chat on the Internet. Mark. Sci. 25(2), 155–163 (2006)

    Article  Google Scholar 

  26. Mcauley, J., Pandey, R., Leskovec, J.: Inferring networks of substitutable and complementary products. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 785–794 (2015)

    Google Scholar 

  27. Mikolov, T., Chen, K., Corrado, G. and Dean, J.: Efficient estimation of word representations in vector space. Proceedings of Workshop at ICLR. arXiv:1301.3781v1 (2013)

    Google Scholar 

  28. Mukherjee, A., Liu, B., Glance, N.: Spotting fake reviewer groups in consumer reviews. In: WWW 2012, pp. 191–200. ACM (2012)

    Google Scholar 

  29. Mukherjee, A., Kumar, A., Liu, B., Wang, J., Hsu, M., Castellanos, M., Ghosh, R.: Spotting opinion spammers using behavioral footprints. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 632–640 (2013)

    Google Scholar 

  30. Mukherjee, A., Venkataraman, V., Liu, B., Glance, N.: What Yelp fake review filter might be doing? In: Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media (2013)

    Google Scholar 

  31. Nam, S., Manchanda, P., Chintagunta, P.K.: The effect of signal quality and contiguous word of mouth on customer acquisition for a video-on-demand service. Mark. Sci. 29(4), 690–700 (2010)

    Article  Google Scholar 

  32. Newman, M. L., Pennebaker, J. W., Berry, D. S., Richards, J. M.: Lying words: Predicting deception from linguistic styles. Pers Soc Psychol Bull. 29(5), 665–675 (2003)

    Google Scholar 

  33. Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, vol. 1, pp. 309–319 (2011)

    Google Scholar 

  34. Öğüt, H., Taş, B.K.O.: The influence of internet customer reviews on the online sales and prices in hotel industry. Serv. Ind. J. 32(2), 197–214 (2012)

    Article  Google Scholar 

  35. Shasha, D., Pengzhu, Z., Xiaoyan, Z., Xinmiao, L.: Deception detection based on fake linguistic cues. J. Syst. Manag. 23, 263–270 (2014)

    Google Scholar 

  36. Smith, D.A.: Amazon Reviewers Brought to Book (2004). Available online: https://www.theguardian.com/technology/2004/feb/15/books.booksnews

  37. Wang, Y., Lu, X., Tan, Y.: Impact of product attributes on customer satisfaction: an analysis of online reviews for washing machines. Electron. Commer. Res. Appl. 29, 1–11 (2018)

    Article  Google Scholar 

  38. Xu, C.: Detecting collusive spammers in online review communities. In: Proceedings of the Sixth Workshop on Ph.D. Students in Information and Knowledge Management, pp. 33–40 (2013)

    Google Scholar 

  39. Xu, H., Liu, D., Wang, H., Stavrou, A.: E-commerce reputation manipulation: the emergence of reputation-escalation-as-a-service. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1296–1306 (2015)

    Google Scholar 

  40. Zhang, D., Zhou, L., Kehoe, J.L., Kilic, I.Y.: What online reviewer behaviors really matter? Effects of verbal and nonverbal behaviors on detection of fake online reviews. J. Manag. Inf. Syst. 33(2), 45 (2016)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Information Technology, Dr. Babasaheb Ambedkar, Marathwada University, Aurangabad, India

    Saleh Nagi Alsubari & Sachin N. Deshmukh

  2. Applied College in Abqaiq, King Faisal University, 31982, Al-Ahsa, Saudi Arabia

    Theyazn H. H. Aldhyani

  3. Department of Quantitaive Methods, School of Business, King Faisal University, Al-Ahsa, 31982, Saudi Arabia

    Abdullah H. Al Nefaie & Melfi Alrasheedi

Authors
  1. Saleh Nagi Alsubari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sachin N. Deshmukh
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Theyazn H. H. Aldhyani
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Abdullah H. Al Nefaie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Melfi Alrasheedi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Melfi Alrasheedi .

Editor information

Editors and Affiliations

  1. Department of Mathematical Sciences, Indian Institute of Technology BHU, Varanasi, India

    Tanmoy Som

  2. Computer Science at the Graduate Division, Tijuana Institute of Technology, Tijuana, Mexico

    Oscar Castillo

  3. Department of Computer Science and Information Technology, Central University of Haryana, Mahendergarh, Haryana, India

    Anoop Kumar Tiwari

  4. Department of Mathematics, School of Basic and Applied Sciences, Harcourt Butler Technical University, Kanpur, Uttar Pradesh, India

    Shivam Shreevastava

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Alsubari, S.N., Deshmukh, S.N., Aldhyani, T.H.H., Al Nefaie, A.H., Alrasheedi, M. (2023). Rule-Based Classifiers for Identifying Fake Reviews in E-commerce: A Deep Learning System. In: Som, T., Castillo, O., Tiwari, A.K., Shreevastava, S. (eds) Fuzzy, Rough and Intuitionistic Fuzzy Set Approaches for Data Handling. Forum for Interdisciplinary Mathematics. Springer, Singapore. https://doi.org/10.1007/978-981-19-8566-9_14

Download citation

Publish with us

Policies and ethics

Search

Navigation