2018/03/27 by Beatrice Perez, Mirco Musolesi, Perez, Beatrice +3 · 1 voice · 2 citations
Computer Science · Social Sciences · #Artificial Intelligence (cs.AI) #Authorship Attribution and Profiling #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Misinformation and Its Impacts #Social and Information Networks (cs.SI) #Spam and Phishing Detection #cs.AI #cs.CR #cs.SI
paper · pdf · doi:10.48550/arxiv.1803.10133
11 pages, 13 figures. Published in the Proceedings of the 12th International AAAI Conference on Web and Social Media (ICWSM 2018). June 2018. Stanford, CA, USA
openalex publication_date 2018/03/27 · arxiv published 2018/03/27 · arxiv created 2018/05/14 · arxiv updated 2018/05/15 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.