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In the field of social networking services, finding similar users based onprofile data is common practice. Smartphones harbor sensor and personal contextdata that can be used for user profiling. Yet, one vast source of personaldata, that is text messaging data, has hardly been studied for user profiling.We see three reasons for this: First, private text messaging data is not shareddue to their intimate character. Second, the definition of an appropriateprivacy-preserving similarity measure is non-trivial. Third, assessing thequality of a similarity measure on text messaging data representing apotentially infinite set of topics is non-trivial. In order to overcome theseobstacles we propose affinity, a system that assesses the similarity betweentext messaging histories of users reliably and efficiently in aprivacy-preserving manner. Private texting data stays on user devices and datafor comparison is compared in a latent format that neither allows toreconstruct the comparison words nor any original private plain text. Weevaluate our approach by calculating similarities between Twitter histories of60 US senators. The resulting similarity network reaches an average 85.0%accuracy on a political party classification task.

affinity: A System for Latent User Similarity Comparison on Texting Data