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Multitask Learning for Sparse Failure Prediction Read chapter Read first chapter

2019 | OriginalPaper | Chapter

Unsupervised User Behavior Representation for Fraud Review Detection with Cold-Start Problem

Authors : Qian Li, Qiang Wu, Chengzhang Zhu, Jian Zhang, Wentao Zhao

Published in: Advances in Knowledge Discovery and Data Mining

Publisher: Springer International Publishing

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Abstract

Detecting fraud review is becoming extremely important in order to provide reliable information in cyberspace, in which, however, handling cold-start problem is a critical and urgent challenge since the case of cold-start fraud review rarely provides sufficient information for further assessing its authenticity. Existing work on detecting cold-start cases relies on the limited contents of the review posted by the user and a traditional classifier to make the decision. However, simply modeling review is not reliable since reviews can be easily manipulated. Also, it is hard to obtain high-quality labeled data for training the classifier. In this paper, we tackle cold-start problems by (1) using a user’s behavior representation rather than review contents to measure authenticity, which further (2) consider user social relations with other existing users when posting reviews. The method is completely (3) unsupervised. Comprehensive experiments on Yelp data sets demonstrate our method significantly outperforms the state-of-the-art methods.

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previous chapter BAB-QA: A New Neural Model for Emotion Detection in Multi-party Dialogue
next chapter Gated Convolutional Encoder-Decoder for Semi-supervised Affect Prediction
Footnotes
3
The pre-trained word embedding can be downloaded from: http://​nlp.​stanford.​edu/​data/​glove.​6B.​zip.
 
Literature
1.
Chen, J., Saad, Y.: Dense subgraph extraction with application to community detection. TKDE 24(7), 1216–1230 (2012)
2.
3.
Fei, G., Mukherjee, A., Liu, B., Hsu, M., Castellanos, M., Ghosh, R.: Exploiting burstiness in reviews for review spammer detection. In: ICWSM 2013, pp. 175–184 (2013)
4.
Feng, S., Xing, L., Gogar, A., Choi, Y.: Distributional footprints of deceptive product reviews. In: ICWSM 2012, pp. 98–105 (2012)
5.
Hooi, B., Shin, K., Song, H.A., Beutel, A., Shah, N., Faloutsos, C.: Graph-based fraud detection in the face of camouflage. TKDD 11(4), 44 (2017)CrossRef
6.
Hooi, B., Song, H.A., Beutel, A., Shah, N., Shin, K., Faloutsos, C.: FRAUDAR: bounding graph fraud in the face of camouflage. In: ACM SIGKDD, pp. 895–904. ACM (2016)
7.
Hovy, D.: The enemy in your own camp: how well can we detect statistically-generated fake reviews-an adversarial study. In: ACL, vol. 2, pp. 351–356 (2016)
8.
Jindal, N., Liu, B.: Opinion spam and analysis. In: WSDM, pp. 219–230. ACM (2008)
9.
10.
Li, H., Chen, Z., Liu, B., Wei, X., Shao, J.: Spotting fake reviews via collective positive-unlabeled learning. In: ICDM, pp. 899–904. IEEE (2014)
11.
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. In: ICWSM, pp. 634–637 (2015)
12.
13.
Liu, S., Hooi, B., Faloutsos, C.: HoloScope: topology-and-spike aware fraud detection. In: CIKM, pp. 1539–1548. ACM (2017)
14.
Luca, M., Zervas, G.: Fake it till you make it: reputation, competition, and yelp review fraud. Manag. Sci. 62(12), 3412–3427 (2016)CrossRef
15.
van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. JMLR 9(Nov), 2579–2605 (2008)MATH
16.
Mukherjee, A., et al.: Spotting opinion spammers using behavioral footprints. In: ACM SIGKDD, pp. 632–640. ACM (2013)
17.
Mukherjee, A., Liu, B., Wang, J., Glance, N., Jindal, N.: Detecting group review spam. In: WWW, pp. 93–94. ACM (2011)
18.
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 (2013)
19.
Mukherjee, A., Venkataraman, V., Liu, B., Glance, N.S.: What yelp fake review filter might be doing? In: ICWSM (2013)
20.
Ott, M., Choi, Y., Cardie, C., Hancock, J.T.: Finding deceptive opinion spam by any stretch of the imagination. In: ACL HLT, pp. 309–319. Association for Computational Linguistics (2011)
21.
Pennington, J., Socher, R., Manning, C.: GloVe: global vectors for word representation. In: EMNLP, pp. 1532–1543 (2014)
22.
Rayana, S., Akoglu, L.: Collective opinion spam detection: bridging review networks and metadata. In: ACM SIGKDD, pp. 985–994. ACM (2015)
23.
Rayana, S., Akoglu, L.: Collective opinion spam detection using active inference. In: ICDM, pp. 630–638. SIAM (2016)
24.
Wang, G., Xie, S., Liu, B., Philip, S.Y.: Review graph based online store review spammer detection. In: ICDM, pp. 1242–1247. IEEE (2011)
25.
Wang, X., Liu, K., Zhao, J.: Handling cold-start problem in review spam detection by jointly embedding texts and behaviors. In: ACL, vol. 1, pp. 366–376 (2017)
26.
Wu, L., Hu, X., Morstatter, F., Liu, H.: Adaptive spammer detection with sparse group modeling. In: ICWSM, pp. 319–326 (2017)
27.
28.
You, Z., Qian, T., Liu, B.: An attribute enhanced domain adaptive model for cold-start spam review detection. In: COLING, pp. 1884–1895 (2018)
Metadata
Title
Unsupervised User Behavior Representation for Fraud Review Detection with Cold-Start Problem
Authors
Qian Li
Qiang Wu
Chengzhang Zhu
Jian Zhang
Wentao Zhao
Copyright Year
2019
Book
Advances in Knowledge Discovery and Data Mining

Print ISBN: 978-3-030-16147-7

Electronic ISBN: 978-3-030-16148-4

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-16148-4_18

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