Automatic Detection of Sensitive Information in Educative Social Networks

[EN] Detecting sensitive information with privacy in mind is a relevant issue on Social Networks. It is often difficult for users to manage the privacy associated with their posts on social networks taking into account their possible consequences. The main objective of this work is to provide users information about the sensitivity of the information they will share when they decide to publish a message in online media. For this purpose, an assistant agent to detect sensitive information based on different types of categories detected in the message (i.e., location, personal data, health, personal attacks, emotions, etc.) is proposed. Entity recognition libraries, ontologies, dictionaries, and sentiment analysis will be used to detect the different categories. This agent is integrated into the social network Pesedia, aimed for children and teenagers, and through a soft-paternalism mechanism provides information to users about the sensitivity of certain content and help them in making decisions about its publication. The agent decision process will be evaluated with a dataset elaborated from messages of the social network Twitter.This work is supported by the Spanish Government project TIN2017-89156-R.Botti-Cebriá, V.; Del Val Noguera, E.; García-Fornes, A. (2020). Automatic Detection of Sensitive Information in Educative Social Networks. Springer. 184-194. https://doi.org/10.1007/978-3-030-57805-3_18S184194Official legal text. https://gdpr-info.eu/Aghasian, E., Garg, S., Gao, L., Yu, S., Montgomery, J.: Scoring users’ privacy disclosure across multiple online social networks. 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Time Poverty Thresholds and Rates for the US Population

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