Connecting Users across Social Media Sites: A Behavioral-Modeling Approach
published: Sept. 27, 2013, recorded: August 2013, views: 483
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
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 prole 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 identification 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 identities 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 effective user identication. We formally define the cross-media user identification problem and show that MOBIUS is effective 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.
Download slides: kdd2013_zafarani_behavioral_modeling_01.pdf (5.1 MB)
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !