Domain-independent data cleaning via analysis of entity-relationship graph
ACM Transactions on Database Systems (TODS)
Correlating user profiles from multiple folksonomies
Proceedings of the nineteenth ACM conference on Hypertext and hypermedia
User identification for cross-system personalisation
Information Sciences: an International Journal
Semantic Modelling of User Interests Based on Cross-Folksonomy Analysis
ISWC '08 Proceedings of the 7th International Conference on The Semantic Web
Applying Semantic Social Graphs to Disambiguate Identity References
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
Linking social networks on the web with FOAF: a semantic web case study
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Large Online Social Footprints--An Emerging Threat
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 03
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
Abusing social networks for automated user profiling
RAID'10 Proceedings of the 13th international conference on Recent advances in intrusion detection
How unique and traceable are usernames?
PETS'11 Proceedings of the 11th international conference on Privacy enhancing technologies
The minimum description length principle in coding and modeling
IEEE Transactions on Information Theory
What's in a name?: an unsupervised approach to link users across communities
Proceedings of the sixth ACM international conference on Web search and data mining
@i seek 'fb.me': identifying users across multiple online social networks
Proceedings of the 22nd international conference on World Wide Web companion
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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.