Large-scale Correlation of Accounts Across Social Networks
Title | Large-scale Correlation of Accounts Across Social Networks |
Publication Type | Technical Report |
Year of Publication | 2013 |
Authors | Goga, O., Perito D., Lei H., Teixeira R., & Sommer R. |
Other Numbers | 3419 |
Abstract | Organizations are increasingly mining the personal data users generate as they carry out muchof their day?to?day activities online. A range of new business models specifically exploit whatusers publish on their social network profiles, including services performing background checksand analytics providers who, e.g., associate demographics with consumer behavior. In this workwe set out to understand the capabilities of machine learning techniques for linkingindependent accounts that users maintain on different social networks, based solely on theinformation people explicitly and publicly provide in their profiles. We perform a large scalestudy that assesses a range of correlation approaches for matching accounts between fivepopular social networks: Twitter, Facebook, Google+, Myspace, and Flickr. Our results show forinstance that by exploiting usernames, real names, locations, and photos, we can robustly |
Acknowledgment | This work was partially supported by funding provided to ICSI through National Science Foundation grant CNS?1065240. Any opinions, findings, and conclusions or recommendations expressed in this material are those of theauthors or originators and do not necessarily reflect the views of the National Science Foundation. |
URL | http://www.icsi.berkeley.edu/pubs/techreports/ICSI_TR-13-002.pdf |
Bibliographic Notes | ICSI Technical Report TR-13-002, Berkeley, California |
Abbreviated Authors | O. Goga, D. Perito, H. Lei, R. Teixeira, and R. Sommer |
ICSI Research Group | Networking and Security |
ICSI Publication Type | Technical Report |