Abstract
A variety of machine-learning techniques have been proposed, over the last decade, to build spam identification models. However, most of these models depend entirely on the extracted features and perform more efficiently when used by large datasets. This paper proposes a temporal spam identification algorithm, which makes use of time series, to filter suspicious reviews from a Yelp review dataset. Based on those labelled suspicious reviews, this algorithm employs feature-engineering techniques. We use a combination of behavioral, review-centric features and word and character n-grams. We classify spam and ham reviews, by using a support vector machine. The proposed method can be used in real-time spam detection systems. A comparison with two other approaches indicates that the algorithm proposed in this paper achieves a higher accuracy (94%). Our proposed algorithm reduces the scope of searching, and huge computations, required for spam detection in large datasets.
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References
Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: Finding deceptive opinion spam by any stretch of the imagination. Presented at the proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, vol. 1, Portland, Oregon (2011)
Li, H., Chen, Z., Mukherjee, A., Liu, B., Shao, J.: Analyzing and detecting opinion spam on a large-scale dataset via temporal and spatial patterns (2015)
Li, H., Fei, G., Wang, S., Liu, B., Shao, W., Mukherjee, A., et al.: Modeling Review Spam Using Temporal Patterns and Co-bursting Behaviors. arXiv preprint arXiv:1611.06625 (2016)
Jindal, N., Liu, B.: Opinion spam and analysis. In: Proceedings of the 2008 International Conference on Web Search and Data Mining, pp. 219–230 (2008)
Li, J., Ott, M., Cardie, C., Hovy, E.: Towards a general rule for identifying deceptive opinion spam. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1566–1576 (2014)
Fusilier, D.H., Montes-y-Gómez, M., Rosso, P., Cabrera, R.G.: Detection of opinion spam with character n-grams. In: International Conference on Intelligent Text Processing and Computational Linguistics, pp. 285–294 (2015)
Fusilier, D.H., Montes-y-Gómez, M., Rosso, P., Cabrera, R.G.: Detecting positive and negative deceptive opinions using PU-learning. Inf. Process. Manage. 51, 433–443 (2015)
Heydari, A., Tavakoli, M., Salim, N.: Detection of fake opinions using time series. Expert Syst. Appl. 58, 83–92 (2016)
Crawford, M., Khoshgoftaar, T.M., Prusa, J.D., Richter, A.N., Al Najada, H.: Survey of review spam detection using machine-learning techniques. J. Big Data 2, 23 (2015)
Lau, R.Y., Liao, S., Kwok, R.C.-W., Xu, K., Xia, Y., Li, Y.: Text mining and probabilistic language modeling for online review spam detection. ACM Trans. Manage. Inf. Syst. (TMIS) 2, 25 (2011)
Mukherjee, A., Liu, B., Glance, N.: Spotting fake reviewer groups in consumer reviews. In: Proceedings of the 21st International Conference on World Wide Web, pp. 191–200 (2012)
Jindal, N., Liu, B., Lim, E.-P.: Finding unusual review patterns using unexpected rules. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, pp. 1549–1552 (2010)
Mukherjee, A., Venkataraman, V., Liu, B., Glance, N.S.: What Yelp fake review filter might be doing? In: ICWSM (2013)
Almeida, T.A., Hidalgo, J.M.G., Yamakami, A.: Contributions to the study of SMS spam-filtering: new collection and results. In: Proceedings of the 11th ACM Symposium on Document Engineering, pp. 259–262 (2011)
Dewang, R.K., Singh, P., Singh, A.K.: Finding of review spam through Corleone, review genre, writing style and review text detail features. In: Proceedings of the Second International Conference on Information and Communication Technology for Competitive Strategies, p. 23 (2016)
Mukherjee, A., Kumar, A., Liu, B., Wang, J., Hsu, M. Castellanos, M., et al.: 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)
Bakhshi, S., Kanuparthy, P., Shamma, D.A.: Understanding online reviews: funny, cool or useful? In: Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work & Social Computing, pp. 1270–1276 (2015)
Kc, S., Mukherjee, A.: On the temporal dynamics of opinion spamming. In: Proceedings of the 25th International Conference on World Wide Web—WWW 16 (2016)
Mukherjee, A., Venkataraman, V., Liu, B., Glance, N.: Fake review detection: classification and analysis of real and pseudo reviews. Technical Report UIC-CS-2013–03, University of Illinois at Chicago, Technical Report (2013)
Yelp: Yelp, 2017. [Online]. Available: http://www.Yelp.com. Accessed 06 Dec 2017
Zhang, Y., Zhang, H., Zhang, M., Liu, Y., Ma, S.: Do users rate or review?: boost phrase-level sentiment labeling with review-level sentiment classification. In: Proceedings of the 37th International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 1027–1030 (2014)
PeakUtils 1.1.0: Python Package Index. [Online]. Available: https://pypi.python.org/pypi/PeakUtils. Accessed 07 Dec 2017
Natural Language Toolkit: Natural Language Toolkit—NLTK 3.2.5 documentation. [Online]. Available: http://www.nltk.org/. Accessed 07 Dec 2017
Scikit-learn: Scikit-learn: machine-learning in Python—scikit-learn 0.19.1 documentation. [Online]. Available: http://scikit-learn.org/stable/. Accessed 07 Dec 2017
Fei, G., Mukherjee, A., Liu, B., Hsu, M., Castellanos, M., Ghosh, R.: Exploiting burstiness in reviews for review spammer detection. In: Icwsm, vol. 13, pp. 175–184 (2013)
Dematis, I., Karapistoli, E., Vakali, A.: 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 (2018)
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Muhammad, I., Qamar, U., Khan, F.H. (2019). Temporal Spam Identification: A Multifaceted Approach to Identifying Review Spam. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2018. Advances in Intelligent Systems and Computing, vol 869. Springer, Cham. https://doi.org/10.1007/978-3-030-01057-7_58
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