Publication
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, Pages 227-232, UMAP '18, ISBN 978-1-4503-5784-5, ACM, 2018.
Abstract
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.