2013/01/29 by Anshu Malhotra, Malhotra, Anshu, Luam Totti +9 · 1 citation
Computer Science · Decision Sciences · Physics and Astronomy · #Complex Network Analysis Techniques #Data Quality and Management #FOS: Computer and information sciences #Social and Information Networks (cs.SI) #Web Data Mining and Analysis #cs.SI
paper · pdf · doi:10.48550/arxiv.1301.6870
The paper is already published in ASONAM 2012
arxiv created 2013/01/29 · openalex publication_date 2013/01/29 · arxiv updated 2013/01/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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, LinkedIn, Twitter and YouTube. 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, FriendFeed, 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, Wordnet based ontologies) to measure the similarity of the user profiles on different social networks. We specifically focused on Twitter and LinkedIn. 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. UserID 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.