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Multi-domain alias matching using machine learning
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computing Science. (Security)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems. (Security)
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computer Systems. (Security)
2016 (English)In: Proc. 3rd European Network Intelligence Conference, IEEE, 2016Conference paper (Refereed)
Abstract [en]

We describe a methodology for linking aliases belonging to the same individual based on a user’s writing style (stylometric features extracted from the user generated content) and her time patterns (time-based features extracted from the publishing times of the user generated content). While most previous research on social media identity linkage relies on matching usernames, our methodology can also be used for users who actively try to choose dissimilar usernames when creating their aliases. In our experiments on a discussion forum dataset and a Twitter dataset, we evaluate the performance of three different classifiers. We use the best classifier (AdaBoost) to evaluate how well it works on different datasets using different features. Experiments show that combining stylometric and time-based features yield good results on our synthetic datasets and a small-scale evaluation on real-world blog data confirm these results, yielding a precision over 95%. The use of emotion-related and Twitter-related features yield no significant impact on the results.

Place, publisher, year, edition, pages
IEEE, 2016.
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:uu:diva-306944OAI: oai:DiVA.org:uu-306944DiVA: diva2:1044820
Conference
ENIC 2016, September 5–7, Wroclaw, Poland
Available from: 2016-11-07 Created: 2016-11-07 Last updated: 2016-11-11

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