An empirical approach for fake user detection in location-based social networks

Article type: Research Article

Authors: Melià-Seguí, Joan^{a; b; *; **} | Bart, Eugene^c | Zhang, Rui^c | Brdiczka, Oliver^c

Affiliations: [a] Estudis d’Informàtica, Multimèdia i Telecomunicació, Universitat Oberta de Catalunya, Rambla del Poblenou 156, 08018 Barcelona, Spain | [b] Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya, Av. Carl Friedrich Gauss 5, 08860 Castelldefels, Spain | [c] Palo Alto Research Center (PARC, a Xerox Company), 3333 Coyote Hill Road, Palo Alto, CA 94304, USA

Correspondence: [*] Corresponding author. E-mail: melia@uoc.edu.

Note: [**] Joan Melià-Seguí developed parts of this work at the Palo Alto Research Center (PARC), and at the Department of Information and Communication Technologies, Universitat Pompeu Fabra.

Abstract: Location-based social networks are becoming a unique platform for understanding user behaviors and providing pervasive services in intelligent environments. However, fake users or accounts can undermine user analytics and lower the value of the applications and services intended for real users. Mining a large Foursquare dataset and related Twitter accounts, we tested different user features with the goal of classifying fake users. Experiments demonstrate an accuracy over 95% in detecting fake users. Filtering out these fake users reduces the error rate of a location-based activity predictor by a 4.4% and avoids wasting 35% of coupons or promotion codes delivery if applied to a recommender system.

Keywords: Location-based social networks (LBSN), LBSN mining, user modeling, context awareness, ubiquitous computing

DOI: 10.3233/AIS-170464

Journal: Journal of Ambient Intelligence and Smart Environments, vol. 9, no. 6, pp. 643-657, 2017