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

TL;DR

This paper demonstrates that social media metadata can uniquely identify users with high accuracy using machine learning, and that obfuscation strategies are largely ineffective against such identification.

Contribution

It quantifies the identifiability of users from metadata and evaluates the robustness of obfuscation methods, highlighting challenges in protecting user privacy.

Findings

96.7% accuracy in user identification with 10,000 users

99.22% accuracy when considering top 10 candidates

Obfuscation by perturbing 60% of data remains largely ineffective

Abstract

Metadata are associated to most of the information we produce in our daily interactions 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 the identification of a user from the content of a message. In this paper, we use Twitter as a case study to quantify the uniqueness of the association between metadata and user identity and to understand the effectiveness of potential obfuscation strategies. More specifically, we analyze atomic fields in the metadata and systematically combine them in an effort to classify new tweets as belonging to an account using different machine learning algorithms of increasing complexity. We demonstrate that through the application of a supervised learning algorithm, we are able to identify 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 likely candidates we increase the accuracy of the model to 99.22%. We also found that data obfuscation is hard and ineffective for this type of data: even after perturbing 60% of the training data, it is still possible to classify users with an accuracy higher than 95%. These results have strong implications in terms of the design of metadata obfuscation strategies, for example for data set release, not only for Twitter, but, more generally, for most social media platforms.