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Other Times, Other Values: Leveraging Attribute History to Link User Profiles across Online Social Networks

Authors: Paridhi Jain, Ponnurangam Kumaraguru, and Anupam Joshi

Book Title: Proceedings of the 26th ACM Conference on Hypertext & Social Media (HT15)

Date: August 24, 2015

Abstract: Profile linking is the ability to connect profiles of a user on different social networks. Linked profiles can help companies like Disney to build psychographics of potential customers and segment them for targeted marketing in a cost-effective way. Existing methods link profiles by observing high similarity between most recent (current) values of the attributes like name and username. However, for a section of users observed to evolve their attributes over time and choose dissimilar values across their profiles, these current values have low similarity. Existing methods then falsely conclude that profiles refer to different users. To reduce such false conclusions, we suggest to gather rich history of values assigned to an attribute over time and compare attribute histories to link user profiles across networks. We believe that attribute history highlights user preferences for creating attribute values on a social network. Co-existence of these preferences across profiles on different social networks result in alike attribute histories that suggests profiles potentially refer to a single user. Through a focused study on username, we quantify the importance of username history for profile linking on a dataset of real-world users with profiles on Twitter, Facebook, Instagram and Tumblr. We show that username history correctly links 44% more profile pairs with non-matching current values that are incorrectly unlinked by existing methods. We further explore if factors such as longevity and availability of username history on either profiles affect linking performance. To the best of our knowledge, this is the first study that explores viability of using an attribute history to link profiles on social networks.

Type: InProceedings

Publisher: ACM

Pages: 247-255

Tags: social media

Google Scholar: search

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