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Anomaly Detection based on Review Burstness and Ranking Fraud Discovery

by Anpu Alexander, Rahila N. A., P. Mohamed Shameem
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 169 - Number 9
Year of Publication: 2017
Authors: Anpu Alexander, Rahila N. A., P. Mohamed Shameem
10.5120/ijca2017914883

Anpu Alexander, Rahila N. A., P. Mohamed Shameem . Anomaly Detection based on Review Burstness and Ranking Fraud Discovery. International Journal of Computer Applications. 169, 9 ( Jul 2017), 44-47. DOI=10.5120/ijca2017914883

@article{ 10.5120/ijca2017914883,
author = { Anpu Alexander, Rahila N. A., P. Mohamed Shameem },
title = { Anomaly Detection based on Review Burstness and Ranking Fraud Discovery },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2017 },
volume = { 169 },
number = { 9 },
month = { Jul },
year = { 2017 },
issn = { 0975-8887 },
pages = { 44-47 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume169/number9/28017-2017914883/ },
doi = { 10.5120/ijca2017914883 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:17:00.210530+05:30
%A Anpu Alexander
%A Rahila N. A.
%A P. Mohamed Shameem
%T Anomaly Detection based on Review Burstness and Ranking Fraud Discovery
%J International Journal of Computer Applications
%@ 0975-8887
%V 169
%N 9
%P 44-47
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nowadays everyone is using smart phone. Many applications are in smart phone. To download an application user visit App store such as Google play store, Apple play store etc, then he or she is able to see the different application lists. User has no awareness about the application. So user looks at the list and download the application from App Store based on the mobile app rank. App developers use different ways to promote their Apps in order to get top position in App store for example, high rating and good reviews are given about the mobile app i.e. there is fraud behavior occur it. To detect fraud behavior first identify the active periods of mobile app, namely leading session of mobile apps. In the existing system the leading event and leading session of an app identified from the collected historical records. Then ranking based evidence, rating based evidence and review based evidence were collected from the historical records. These evidence score value is used to detect fraud behavior occur in the mobile app. In proposed system from the reviews of mobile app it identifies if it is a fake review or not.

References
  1. Hengshu Zhu, Young Ge, and Enhong Chen. 2015. Discovery of Ranking Fraud.
  2. Banerjee, S., and Chua, A. Y. K. 2014. A linguistic framework to distinguish between genuine and deceptive online reviews.
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  5. Banerjee, Snehasish, Alton YK Chua, and Jung-Jae Kim. 2015. "Using supervised learning to classify authentic and fake online reviews."
  6. Hancock, J. T, Curry, L. E., Goorha, S., and Woodworth, M. 2008. On lying and being lied to: A linguistic analysis of deception in computer-mediated communication.
  7. Ott, M., Choi, Y., Cardie, C., and Hancock, J. T. 2011. Finding deceptive opinion spam by any stretch of the imagination. In Annual Meeting of the Association for Computational Linguistic.
  8. Rayson, P., Wilson, A., and Leech, G. 2001. Grammatical word class variation within the British National Corpus.
  9. Tausczik, Y. R., and Pennebaker, J. W. 2010. The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29, 24-54.
  10. Yoo, K. H., and Gretzel, U. 2009. Comparison of deceptive and truthful travel reviews.
  11. Zuckerman, M., De Paulo, B. M., and Rosenthal, R. 1981. Verbal and nonverbal communication of deception. In Advances in Experimental Social Psychology, L. Berkowitz, Ed., Academic Press, New York.
Index Terms

Computer Science
Information Sciences

Keywords

Aggregation Leading session SVM.

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