Abstract
This study delves into the privacy risks associated with user interactions in complex networks such as those generated on social media platforms. In such networks, potentially sensitive information can be extracted and/or inferred from explicitly user-generated content and its (often uncontrolled) dissemination. Hence, this preliminary work first studies an unsupervised model generating a privacy risk score for a given user, which considers both sensitive information released directly by the user and content propagation in the complex network. In addition, a supervised model is studied, which identifies and incorporates features related to privacy risk. The results of both multi-class and binary privacy risk classification for both models are presented, using the Twitter platform as a scenario, and a publicly accessible purpose-built dataset.
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Data Availability
The labeled dataset generated and used in this work is available on request from the corresponding author.
Notes
- 1.
https://www.nytimes.com/2023/08/03/technology/twitter-x-tweets-elon-musk.html, accessed on September 1, 2023.
- 2.
This can happen, for example, when a tweet is retweeted or mentioned by users with a large following, thus amplifying its reach.
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Acknowledgements
This work was supported in part by project SERICS (PE00000014) under the NRRP MUR program funded by the EU - NGEU, by project KURAMi (20225WTRFN) under the PRIN 2022 MUR program, and by the EC under grants MARSAL (101017171) and GLACIATION (101070141).
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Livraga, G., Olzojevs, A., Viviani, M. (2024). Unveiling the Privacy Risk: A Trade-Off Between User Behavior and Information Propagation in Social Media. In: Cherifi, H., Rocha, L.M., Cherifi, C., Donduran, M. (eds) Complex Networks & Their Applications XII. COMPLEX NETWORKS 2023. Studies in Computational Intelligence, vol 1144. Springer, Cham. https://doi.org/10.1007/978-3-031-53503-1_23
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