Detecting opinion spammer groups and spam targets through community discovery and sentiment analysis-Reference-Cited by-同舟云学术

Detecting opinion spammer groups and spam targets through community discovery and sentiment analysis

Published:2017-05-29 Issue:3 Volume:25 Page:283-318
ISSN:1875-8924
Container-title:Journal of Computer Security
language:
Short-container-title:JCS

Author:

Choo Euijin¹,Yu Ting¹,Chi Min²

Affiliation:

1. Qatar Computing Research Institute, Qatar. E-mails: echoo@hbku.edu.qa, tyu@hbku.edu.qa

2. North Carolina State University, Raleigh, NC, USA. E-mail: mchi@ncsu.edu

Publisher

IOS Press

Subject

Computer Networks and Communications,Hardware and Architecture,Safety, Risk, Reliability and Quality,Software

Reference43 articles.

1. J. Abernethy, O. Chapelle and C. Castillo, Web spam identification through content and hyperlinks, in: Proc. of the 4th Int’l Workshop on Adversarial Information Retrieval on the Web, ACM, 2008, pp. 41–44.

2. M. Bendersky and W.B. Croft, Finding text reuse on the web, in: Proc. of 2nd ACM Int’l Conf. on Web Search and Data Mining, ACM, 2009, pp. 262–271.

3. C. Castillo, D. Donato, A. Gionis, V. Murdock and F. Silvestri, Know your neighbors: Web spam detection using the web topology, in: Proc. of the 30th Annual Int’l ACM SIGIR Conf. on Research and Development in Information Retrieval, ACM, 2007, pp. 423–430.

4. E. Choo, T. Yu, M. Chi and Y. Sun, Revealing and incorporating implicit communities to improve recommender systems, in: Proc. of the 15th ACM Conf. on Economics and Computation, ACM, 2014, pp. 489–506.

5. G. Fei, A. Mukherjee, B. Liu, M. Hsu, M. Castellanos and R. Ghosh, Exploiting burstiness in reviews for review spammer detection, in: 7th Int’l AAAI Conf. on Weblogs and Social Media, 2013.

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