State-of-art approaches for review spammer detection: a survey

Author:

Dewang Rupesh KumarORCID,Singh Anil Kumar

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Information Systems,Software

Reference48 articles.

1. Akoglu, L., Chandy, R., & Faloutsos, C. (2013). Opinion fraud detection in online reviews by network effects. ICWSM, 13, 2–11.

2. Aye, C.M., & Oo, K.M. (2014). Review spammer detection by using behaviors based scoring methods. In Proceedings of international conference on advances in engineering and technology.

3. Baeza-Yates, R.A., Castillo, C., López, V., & Telefónica, C. (2005). Pagerank increase under different collusion topologies. In AIRWeb (Vol. 5, pp. 25–32).

4. Berger, P, Hennig, P., Schoenberg, M., & Meinel, C. (2015). Blog, forum or newspaper? Web genre detection using svms. In 2015 IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology (WI-IAT) (pp. 364–68). IEEE.

5. Carpinter, J., & Hunt, R. (2006). Tightening the net: a review of current and next generation spam filtering tools. Computers & Security, 25(8), 566–578.

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