Abstract
The emergence of microblogging-based social networks shows how important it is for common people to share information worldwide. In this environment, Twitter has set it apart from the rest of competitors. Users publish text messages containing opinions and information about a wide range of topics, including personal ones. Previous works have shown that these publications can be analyzed to extract useful information for the society but also to characterize the users who generate them and, hence, to build personal profiles. This latter situation poses a serious threat to users’ privacy. In this paper, we present a new privacy-preserving scheme that distorts the real user profile in front of automatic profiling systems applied to Twitter. This is done while keeping user publications intact in order to interfere the least with her followers. The method has been tested using Twitter publications gathered from renowned users, showing that it effectively obfuscates users’ profiles.
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Viejo, A., Sánchez, D., Castellà-Roca, J. (2012). Using Profiling Techniques to Protect the User’s Privacy in Twitter. In: Torra, V., Narukawa, Y., López, B., Villaret, M. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2012. Lecture Notes in Computer Science(), vol 7647. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34620-0_16
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DOI: https://doi.org/10.1007/978-3-642-34620-0_16
Publisher Name: Springer, Berlin, Heidelberg
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