Detecting Spam Review through Spammer’s Behavior Analysis
Bilah Samping Artikel
ISSN online:
2255-2863
Prefijo Doi:
10.14201
Isi Artikel Utama
Vol. 8 No. 2 (2019), Articles, pages 61-71
Abstract
Online reviews about the purchase of a product or services provided have become the main source of user opinions. To gain profit or fame usually spam reviews are written to promote or demote some target products or services. This practice is known as review spamming. In the last few years, different methods have been suggested to solve the problem of review spamming but there is still a need to introduce new spam review detection method to improve accuracy results. In this work, researchers have studied six different spammer behavioral features and analyzed the proposed spam review detection method using weight method. An experimental evaluation was conducted on a benchmark dataset and achieved 84.5% accuracy.
Keywords:
Online Product Reviews, Spam Reviews, Spam Review Detection, Opinion Spam, Customer Reviews, Spammer Behavioral features
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References
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Asadi, R., Kareem, S. A., Asadi, M., & Asadi, S. (2015). A single-layer semi-supervised feed forward neural network clustering method. Malaysian Journal of Computer Science, 28(3), (pp.189-212)
Pudaruth, S., Moheeputh, S., Permessur, N., & Chamroo, A. (2018). Sentiment Analysis from Facebook Comments using Automatic Coding in NVivo 11, ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal Regular Issue, Vol. 7 N. 1, (pp. 41-48)
Bajaj, S., Garg, N., & Singh, S.
K. (2017). A Novel User-based Spam Review Detection. Procedia Computer Science, 122, (pp. 1009-1015).
Biradar, J. G. (2017). The exponential Distribution model for Review Spam Detection. International Journal of
Advanced Research in Computer Science, 8(3), pp. 938-947.
Mukherjee, A., Venkataraman, V., Liu, B., & Glance, N. S. (2013). What yelp fake review filter might be doing? In: International Conference on Web and Social Media (pp. 409-418).
Fusilier, D. H., Montes-y-Gómez, M., Rosso, P., & Cabrera, R. G. (2015). Detecting positive and negative deceptive opinions using PU-learning. Information processing & management, 51(4), 433-443.
Ong, T., Mannino, M., & Gregg, D. (2014). Linguistic characteristics of shill reviews. Electronic Commerce Research and Applications, 13(2), (pp.69-78).
Dematis, I., Karapistoli, E., & Vakali, A. (2018). Fake Review Detection via Exploitation of Spam Indicators and Reviewer Behavior Characteristics. In: International Conference on Current Trends in Theory and
Practice of Informatics (pp. 581-595). Edizioni Della Normale, Cham.
Jindal, N., & Liu, B. (2008, February). Opinion spam and analysis. In Proceedings of the 2008 international conference on web search and data mining (pp. 219-230). ACM..
Liu, B., & Zhang, L. (2012). A survey of opinion mining and sentiment analysis. In: Mining text data (pp. 415- 463). Springer US.
Zhou, S., Qiao, Z., Du, Q., Wang, G. A., Fan, W., & Yan, X. (2018). Measuring Customer Agility from Online Reviews Using Big Data Text Analytics. Journal of Management Information Systems, 35(2),
(pp.510-539).
Chakraborty, M., Pal, S., Pramanik, R., & Chowdary, C. R. (2016). Recent developments in social spam detection and combating techniques: A survey. Information Processing & Management, 52(6),
(pp.1053-1073).
Mukherjee, A., Kumar, A., Liu, B., Wang, J., Hsu, M., Castellanos, M., & Ghosh, R. (2013). Spotting opinion
spammers using behavioral footprints. In Proceedings of the 19th ACM SIGKDD international
conference on Knowledge discovery and data mining (pp. 632-640). ACM..
Heydari, A., Tavakoli, M., & Salim, N. (2016). Detection of fake opinions using time series. Expert Systems with Applications, 58, (pp. 83-92).
KC, S., & Mukherjee, A. (2016). On the temporal dynamics of opinion spamming: Case studies on Yelp. In Proceedings of the 25th International Conference on World Wide Web (pp. 369-379). International World Wide Web Conferences Steering Committee.
Li, H., Fei, G., Wang, S., Liu, B., Shao, W., Mukherjee, A., & Shao, J. (2017). Bimodal distribution and co-bursting in review spam detection. In Proceedings of the 26th International Conference on World Wide Web (pp. 1063-1072).
Kaghazgaran, P., Caverlee, J., & Alfifi, M. (2017, May). Behavioral Analysis of Review Fraud: Linking Malicious Crowdsourcing to Amazon and Beyond. In Eleventh International AAAI Conference on Web and Social Media.
Viviani, M., & Pasi, G. (2017). Quantifier guided aggregation for the veracity assessment of online reviews. International Journal of Intelligent Systems, 32(5), 481-501.
Wang, Zhuo, Songmin Gu, and Xiaowei Xu. "GSLDA: LDA-based group acc detection in product reviews." Applied Intelligence 48.9 (2018): 3094-3107.
Kaghazgaran, P., Caverlee, J., & Squicciarini, A. (2018,February). Combating crowdsourced review
manipulators: A neighborhood-based approach. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (pp. 306-314). ACM.
Ganesan, K., & Zhai, C. (2012). Opinion-based entity ranking. Information retrieval, 15(2), 116-150.
Hazim, M., Anuar, N. B., Ab Razak, M. F., & Abdullah, N. A. (2018). Detecting opinion spams through supervised boosting approach. PloS one, 13(6), e0198884.
Zhou, W., Liu, M., & Zhang, Y. (2017, December). Detecting Spammer Communities Using Network Structural Features. In International Conference on Collaborative Computing: Networking, Applications
and Work-sharing (pp. 670-679). Springer, Cham
Asadi, R., Kareem, S. A., Asadi, M., & Asadi, S. (2015). A single-layer semi-supervised feed forward neural network clustering method. Malaysian Journal of Computer Science, 28(3), (pp.189-212)
Pudaruth, S., Moheeputh, S., Permessur, N., & Chamroo, A. (2018). Sentiment Analysis from Facebook Comments using Automatic Coding in NVivo 11, ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal Regular Issue, Vol. 7 N. 1, (pp. 41-48)