Publikation

Privacy Perceiver: Using Social Network Posts to Derive Users' Privacy Measures

Frederic Raber, Antonio Krüger

In: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization. International Conference on User Modeling, Adaptation, and Personalization (UMAP) New York, NY, USA Seiten 227-232 UMAP '18 ISBN 978-1-4503-5784-5 ACM 2018.

Abstrakt

Current research has shown that a person’s personality can be derived from written text on Facebook or Twitter, as well as the amount of information shared on their personal social network sites. So far, there has been no further investigation on whether a person’s privacy measures can be extracted from these informa- tion sources. We conducted an explorative online user study with 100 participants; the results indicate that privacy concerns can be derived from written text, with a prediction precision similar to personality. At the end of the discussion, we give specific guidelines on the choice of the correct data source for the derivation of the different privacy measures and the possible applications of those.

Weitere Links

proceedings.pdf (pdf, 510 KB)

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence