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IJSTR >> Volume 9 - Issue 1, January 2020 Edition

International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616

Exposure Of Fake Reviews In Mass Media

[Full Text]



K.Swetha, P.S.K Santosh, K.Yasolashmi, B. MK Bharath.



social media and network Net spam, spammer and spam reviewer, dissimilar data networks.



Today the main aim of everyone is to trust on the matter of social media such as feedback and also opinion upon the topic or product. Spammers compose a spam survey about product for liability that everyone takes off about products and services for `issue by researchers and number of studies state that there is a narrow difference between spammer and its features. Here recent investigation states a new method names as NET spam that uses spam importance for demonstrating review datasets as unmatched information network for designing, detecting, and classifying such networks. Spam types supports to acquire improved effects regarding dissimilar metrics on reviewed datasets. This result gives the ultimate outcome of Net spam for existing methods between 4 categories such as reviewed and user behaviour and linguistic type of features but under that features only first type gives better performance than other categories. The contribution work is when user search query it will display all top-k products as well as recommendation of the product.



[1] Ch. Xu and J. Zhang. Combating product review spam campaigns via multiple heterogeneous pairwise features. In SIAM International Conference on Data Mining, 2014.
[2] G. Fei, A. Mukherjee, B. Liu, M. Hsu, M. Castellanos, and R. Ghosh. Exploiting burstiness in reviews for review spammer detection. In ICWSM, 2013.
[3] A. j. Minnich, N. Chavoshi, A. Mueen, S. Luan, and M. Faloutsos. Trueview: Harnessing the power of multiple review sites. In ACM WWW, 2015.
[4] B. Viswanath, M. Ahmad Bashir, M. Crovella, S. Guah, K. P. Gummadi, B. Krishnamurthy, and A. Mislove. Towards detecting anomalous user behavior in online social networks. In USENIX, 2014.
[5] H. Li, Z. Chen, B. Liu, X. Wei, and J. Shao. Spotting fake reviews via collective PU learning. In ICDM, 2014.
[6] International Journal of Innovative Technology and Exploring Engineering 8(6), pp, 869-872, 2019, “A Novel Technique for Secure Routing in Wireless Sensor Networks” , Swetha,K, Sowmya V, Srihitha K, Adithya D
[7] F. Li, M. Huang, Y. Yang, and X. Zhu. Learning to identify review spam. Proceedings of the 22nd International Joint Conference on Artificial Intelligence; IJCAI, 2011.
[8] L.Akoglu, R. Chandy, and C. Faloutsos. Opinion fraud detection in online reviews bynetwork effects.In ICWSM, 2013.
[9] International Journal of Engineering and Advanced Technology 7(3), pp 50-53, 2018 “ A Secured and efficient biometric cryptographic authentication in Pervasive computing”, Swetha K, Pavan kumar Reddy, PSA Maneesha Durga S, Gautham AV, .
[10] M. Salehi, R. Sharma, M. Marzolla, M. Magnani, P. Siyari, and D. Mon-
a. tesi. Spreading processes in multilayer networks. In IEEE Transactions
b. on Network Science and Engineering. 2(2):65–83, 2015.
[11] K. Weise. A Lie Detector Test for Online Reviewers. http://bloom.bg/1KAxzhK. Accessed: 2016-12-16.
[12] E. D. Wahyuni and A. Djunaidy. Fake Review Detection From a Product
a. Review Using Modified Method of Iterative Computation Framework. In
b. Proceeding MATEC Web of Conferences. 2016.
[13] Information technology and control,2018, “A data hiding technique by mixing MFPVD and LSB substitution in a pixel”,Dr G Swain,