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Metadata are associated to most of the information we produce in our dailyinteractions and communication in the digital world. Yet, surprisingly,metadata are often still catergorized as non-sensitive. Indeed, in the past,researchers and practitioners have mainly focused on the problem of theidentification of a user from the content of a message.  In this paper, we use Twitter as a case study to quantify the uniqueness ofthe association between metadata and user identity and to understand theeffectiveness of potential obfuscation strategies. More specifically, weanalyze atomic fields in the metadata and systematically combine them in aneffort to classify new tweets as belonging to an account using differentmachine learning algorithms of increasing complexity. We demonstrate thatthrough the application of a supervised learning algorithm, we are able toidentify any user in a group of 10,000 with approximately 96.7% accuracy.Moreover, if we broaden the scope of our search and consider the 10 most likelycandidates we increase the accuracy of the model to 99.22%. We also found thatdata obfuscation is hard and ineffective for this type of data: even afterperturbing 60% of the training data, it is still possible to classify userswith an accuracy higher than 95%. These results have strong implications interms of the design of metadata obfuscation strategies, for example for dataset release, not only for Twitter, but, more generally, for most social mediaplatforms.

You are your Metadata: Identification and Obfuscation of Social Media Users using Metadata Information