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With 19%-28% of Internet users participating in online health discussions, it became imperative to be able to detect and analyze posted personal health information (PHI). In this work we introduce two semantic-based methods for mining PHI on social networks which will warn the users about potential privacy breaches. One method uses WordNet as a source of health-related knowledge, another -an ontology of personal relations. We use Twitter data to empirically evaluate our methods. We also apply Machine Learning to demonstrate advantages of our extraction procedure when tweets containing PHI have to be automatically identified among other tweets.

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