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• W2534493406 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." @default.
• W2534493406 created "2016-10-28" @default.
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• W2534493406 date "2016-10-01" @default.
• W2534493406 modified "2023-09-26" @default.
• W2534493406 title "User identification across online social networks in practice: Pitfalls and solutions" @default.
• W2534493406 cites W1969890816 @default.
• W2534493406 cites W2056835025 @default.
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• W2534493406 doi "https://doi.org/10.1177/0165551516673480" @default.
• W2534493406 hasPublicationYear "2016" @default.
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