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2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2012)
Istanbul Turkey
Aug. 26, 2012 to Aug. 29, 2012
ISBN: 978-1-4673-2497-7
pp: 1065-1070
A. Malhotra , Indraprastha Inst. of Inf. Technol., New Delhi, India
L. Totti , Dept. de Cienc. da Comput., Univ. Fed. de Minas Gerais, Belo Horizonte, Brazil
W. Meira , Dept. de Cienc. da Comput., Univ. Fed. de Minas Gerais, Belo Horizonte, Brazil
P. Kumaraguru , Indraprastha Inst. of Inf. Technol., New Delhi, India
V. Almeida , Dept. de Cienc. da Comput., Univ. Fed. de Minas Gerais, Belo Horizonte, Brazil
ABSTRACT
With the growing popularity and usage of online social media services, people now have accounts (some times several) on multiple and diverse services like Facebook, Linked In, Twitter and You Tube. Publicly available information can be used to create a digital footprint of any user using these social media services. Generating such digital footprints can be very useful for personalization, profile management, detecting malicious behavior of users. A very important application of analyzing users' online digital footprints is to protect users from potential privacy and security risks arising from the huge publicly available user information. We extracted information about user identities on different social networks through Social Graph API, Friend Feed, and Profilactic, we collated our own dataset to create the digital footprints of the users. We used username, display name, description, location, profile image, and number of connections to generate the digital footprints of the user. We applied context specific techniques (e.g. Jaro Winkler similarity, Word net based ontologies) to measure the similarity of the user profiles on different social networks. We specifically focused on Twitter and Linked In. In this paper, we present the analysis and results from applying automated classifiers for disambiguating profiles belonging to the same user from different social networks. User ID and Name were found to be the most discriminative features for disambiguating user profiles. Using the most promising set of features and similarity metrics, we achieved accuracy, precision and recall of 98%, 99%, and 96%, respectively.
INDEX TERMS
Measurement, Vectors, LinkedIn, Twitter, Accuracy, Ontologies
CITATION

V. Almeida, P. Kumaraguru, W. Meira, L. Totti and A. Malhotra, "Studying User Footprints in Different Online Social Networks," 2012 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining(ASONAM), Istanbul, 2012, pp. 1065-1070.
doi:10.1109/ASONAM.2012.184
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