Applications for In-Situ Feedback on Social Network Notifications

Frederic Raber; Antonio Krüger

In: David Lamas; Fernando Loizides; Lennart Nacke; Helen Petrie; Marco Winckler; Panayiotis Zaphiris (Hrsg.). Human-Computer Interaction -- INTERACT 2019. IFIP Conference on Human-Computer Interaction (INTERACT-2019), September 2-6, Paphos, Greece, Pages 626-629, ISBN 978-3-030-29390-1, Springer International Publishing, 2019.


In social networks, it often arises that a post is shared with a broader audience than intended, which is often finally noticed when one of the unintentionally included friends likes or comments on the post. We present an approach for privacy setting adaptation based on in-situ feedback on such social network update notifications. We implemented a smartphone application that allows users to give positive or negative feedback using two buttons integrated into Facebook's update notifications. We collected qualitative feedback from focus groups to find out what impact of in-situ feedback on privacy settings is expected by users. Our findings indicate that there is no general rule of thumb on how the privacy settings should be adapted. Nevertheless, the discussion led to a new approach that allows users to manage and adapt her privacy settings, and which is also capable of performing content elicitation and filtering for social network sites.


submission.pdf (pdf, 3 MB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence