Mitigating authorship attribution in social media texts: A privacy by translation approach

The exponential growth of social media usage has raised significant privacy concerns, especially regarding adversarial authorship attribution. Social media texts pose distinctive privacy risks for authorship attribution due to their informal, high-variance style. We study Privacy by Translation (PbT) for this setting and its evaluated hybrid with Differential Privacy (PbT+DP) to mitigate authorship identification while preserving utility for social media mental-health classification. Our method transforms social media texts into semantically equivalent forms that obscure authorial style. PbT is formulated in a two-step process (translation and learning) to optimize the trade-off between privacy and utility. We assess the effectiveness of our method across various languages and compare it with existing methods. Our findings reveal that a hybrid approach combining DP with PbT outperforms standalone PbT, DP, and existing methods in balancing privacy and utility in social media mental health classification. The hybrid method reduces adversarial authorship classification by 89.1% and maintains an efficient privacy-utility ratio of 0.40. We also investigate the transformation of linguistic features, including stylistic, syntactic, and semantic elements, to understand their roles in authorship attribution.