NDLI: A model-based approach for identifying spammers in social networks

Content Provider	IEEE Xplore Digital Library
Author	Fathaliani, F. Bouguessa, M.
Copyright Year	2015
Description	Author affiliation: Dept. of Comput. Sci., Univ. of Quebec at Montreal, Montreal, QC, Canada (Fathaliani, F.; Bouguessa, M.)
Abstract	In this paper, we view the task of identifying spammers in social networks from a mixture modeling perspective, based on which we devise a principled unsupervised approach to detect spammers. In our approach, we first represent each user of the social network with a feature vector that reflects its behaviour and interactions with other participants. Next, based on the estimated users feature vectors, we propose a statistical framework that uses the Dirichlet distribution in order to identify spammers. The proposed approach is able to automatically discriminate between spammers and legitimate users, while existing unsupervised approaches require human intervention in order to set informal threshold parameters to detect spammers. Furthermore, our approach is general in the sense that it can be applied to different online social sites. To demonstrate the suitability of the proposed method, we conducted experiments on real data extracted from Instagram and Twitter.
Sponsorship	IEEE Comput. Intell. Soc.
Starting Page	1
Ending Page	9
File Size	1040338
Page Count	9
File Format	PDF
ISBN	9781467382724
DOI	10.1109/DSAA.2015.7344843
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-19
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Uniform resource locators Maximum likelihood estimation Unsolicited electronic mail Mixture models Mixture model Twitter Maximum likelihood Unsupervised learning Principal component analysis Spammers identification Dirichlet distribution
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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