Abstract
Spam reviewers are becoming more professional. The common approach in spam reviewer detection is mainly based on the similarities among reviews or ratings on the same products. Applying this approach to professional spammer detection has some difficulties. First, some of the review systems start to set some limitations, e.g., duplicate submissions from a same id on one product are forbidden. Second, the professional spammers also greatly improve their writing skills. They are consciously trying to use diverse expressions in reviews. In this paper, we present a novel model for detecting professional spam reviewers, which combines posting frequency and text sentiment strength by analyzing the writing and behavior styles. Specifically, we first introduce an approach for counting posting frequency based on a sliding window. We then evaluate the sentiment strength by calculating the sentimental words in the text. Finally, we present a linear combination model. Experimental results on a real dataset from Dianping.com demonstrate the effectiveness of the proposed method.
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References
Cohen, J.: A coefficient of agreement for nominal scales. Educational and Psychological Measurement 20(1), 37–46 (1960)
Cohen, J.: Weighted kappa: Nominal scale agreement with provision for scaled disagreement or partial credit. Psychological Bulletin 70(4), 213–220 (1968)
Dong, Z., Dong, Q.: http://www.keenage.com/html/c_index.html
Eason, G., Noble, B., Sneddon, I.N.: On certain integrals of Lipschitz-Hankel type involving products of Bessel functions. Phil. Trans. Roy. Soc. London A247, 529–551 (1955)
Feng, S., Xing, L., Gogar, A., Choi, Y.: Distributional Footprints of Deceptive Product Reviews. In: ICWSM (2012)
Gilbert, E., Karahalios, K.: Understanding Deja Reviewers. In: Proc. of ACM CSCW, pp. 225–228. ACM, New York (2010)
Hu, M., Liu, B.: Mining and summarizing customer reviews. In: Proc. of KDD, pp. 168–177 (2004)
Jindal, N., Liu, B.: Review spam detection. In: Proc. of WWW (Poster), pp. 1189–1190. ACM (2007)
Jindal, N., Liu, B.: Opinion spam and analysis. In: Proc. of WSDM, pp. 219–230. ACM (2008)
Jindal, N., Liu, B., Lim, E.-P.: Finding Unusual Review Patterns Using Unexpected Rules. In: Proc. of CIKM (2010)
Järvelin, K., Kekäläinen, J.: IR evaluation methods for retrieving highly relevant documents. In: Proc. of SIGIR, pp. 41–48. ACM, New York (2000)
Landis, J.R., Koch, G.G.: The measurement of observer agreement for categorical data. Biometrics 33(1), 159–174 (1977)
Li, F., Huang, M., Yang, Y., Zhu, X.: Learning to Identify Review Spam. In: Proc. of IJCAI, pp. 2488–2493 (2011)
Lim, E.P., Nguyen, V.A., Jindal, N., et al.: Detecting Product Review Spammers Using Rating Behaviors. In: Proc. of the 19th CIKM, pp. 939–948. ACM, New York (2010)
Mukherjee, A., Liu, B., Glance, N.: Spotting Fake Reviewer Groups in Consumer Reviews. In: Proc. of WWW, pp. 191–200 (2012)
Ott, M., Cardie, C., Hancock, J.: Estimating the prevalence of deception in online review communities. In: Proc. of WWW (2012)
Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: Finding Deceptive Opinion Spam by Any Stretch of the Imagination. In: Proc. of ACL, pp. 309–319 (2011)
Wang, G., Xie, S., Liu, B., Yu, P.S.: Review Graph based Online Store Review Spammer Detection. In: Proc. of ICDM (2011)
Xie, S., Wang, G., Lin, S., Yu, P.S.: Review spam detection via temporal pattern discovery. In: Proc. of KDD (2012)
Yoo, K.H., Gretzel, U.: Comparison of Deceptive and Truthful Travel Reviews. In: Information and Communication Technologies in Tourism, pp. 37–47 (2009)
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Huang, J., Qian, T., He, G., Zhong, M., Peng, Q. (2013). Detecting Professional Spam Reviewers. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8347. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53917-6_26
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DOI: https://doi.org/10.1007/978-3-642-53917-6_26
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