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User identification across online social networks in practice: pitfalls and solutions

Research Output: Contribution to journal Article Peer-review

Abstract

To take advantage of the full range of services that online social networks (OSNs) offer, people commonly open several accounts on diverse OSNs where they leave lots of different types of profile information. The integration of these pieces of information from various sources can be achieved by identifying individuals across social networks. In this article, we address the problem of user identification by treating it as a classification task. Relying on common public attributes available through the official application programming interface (API) of social networks, we propose different methods for building negative instances that go beyond usual random selection so as to investigate the effectiveness of each method in training the classifier. Two test sets with different levels of discrimination are set up to evaluate the robustness of our different classifiers. The effectiveness of the approach is measured in real conditions by matching profiles gathered from Google+, Facebook and Twitter.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 377-391

Journal (Volume, Issue Number)

Journal of Information Science (Volume 44, Issue 3)

Publication milestones

  • Published - 30/06/2018

Publication status

Published - 30/06/2018

ISSN

0165-5515

External Publication IDs

  • handle.net: 10547/626568
  • Scopus: 85056823411

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