Connecting Users across Social Media Sites: A Behavioral-Modeling Approach

author: Reza Zafarani, School of Computing, Informatics and Decision Systems Engineering, Arizona State University
published: Sept. 27, 2013,   recorded: August 2013,   views: 6676


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People use various social media for different purposes. The information on an individual site is often incomplete. When sources of complementary information are integrated, a better pro le of a user can be built to improve online services such as verifying online information. To integrate these sources of information, it is necessary to identify individuals across social media sites. This paper aims to address the cross-media user identifi cation problem. We introduce a methodology (MOBIUS) for finding a mapping among identities of individuals across social media sites. It consists of three key components: the first component identiti es users' unique behavioral patterns that lead to information redundancies across sites; the second component constructs features that exploit information redundancies due to these behavioral patterns; and the third component employs machine learning for e ffective user identi cation. We formally defi ne the cross-media user identi fication problem and show that MOBIUS is e ffective in identifying users across social media sites. This study paves the way for analysis and mining across social media sites, and facilitates the creation of novel online services across sites.

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