research-article

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

Authors:

Frederic Raber,

Antonio KrügerAuthors Info & Claims

UMAP '18: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization

Pages 227 - 232

https://doi.org/10.1145/3213586.3225228

Published: 02 July 2018 Publication History

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 information 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.

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Cited By

González-Pizarro FFigueroa ALópez CAragon C(2022)Regional Differences in Information Privacy Concerns After the Facebook-Cambridge Analytica Data ScandalComputer Supported Cooperative Work (CSCW)10.1007/s10606-021-09422-3Online publication date: 14-Feb-2022
https://doi.org/10.1007/s10606-021-09422-3
Raber FKrüger A(2021)Transferring recommendations through privacy user models across domainsUser Modeling and User-Adapted Interaction10.1007/s11257-021-09307-632:1-2(25-90)Online publication date: 8-Nov-2021
https://doi.org/10.1007/s11257-021-09307-6
González FFigueroa ALópez CAragon CGilbert EKarahalios K(2019)Information Privacy Opinions on TwitterCompanion Publication of the 2019 Conference on Computer Supported Cooperative Work and Social Computing10.1145/3311957.3359501(190-194)Online publication date: 9-Nov-2019
https://dl.acm.org/doi/10.1145/3311957.3359501
Show More Cited By

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

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cover image ACM Conferences

UMAP '18: Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization

July 2018

349 pages

ISBN:9781450357845

DOI:10.1145/3213586

General Chairs:
Tanja Mitrovic
University of Canterbury, New Zealand
,
Jie Zhang
Nanyang Technological University, Singapore
,
Program Chairs:
Li Chen
Hong Kong Baptist University, China
,
David Chin
University of Hawaii, USA

Copyright © 2018 ACM.

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Published: 02 July 2018

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July 8 - 11, 2018

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Cited By

González-Pizarro FFigueroa ALópez CAragon C(2022)Regional Differences in Information Privacy Concerns After the Facebook-Cambridge Analytica Data ScandalComputer Supported Cooperative Work (CSCW)10.1007/s10606-021-09422-3Online publication date: 14-Feb-2022
https://doi.org/10.1007/s10606-021-09422-3
Raber FKrüger A(2021)Transferring recommendations through privacy user models across domainsUser Modeling and User-Adapted Interaction10.1007/s11257-021-09307-632:1-2(25-90)Online publication date: 8-Nov-2021
https://doi.org/10.1007/s11257-021-09307-6
González FFigueroa ALópez CAragon CGilbert EKarahalios K(2019)Information Privacy Opinions on TwitterCompanion Publication of the 2019 Conference on Computer Supported Cooperative Work and Social Computing10.1145/3311957.3359501(190-194)Online publication date: 9-Nov-2019
https://dl.acm.org/doi/10.1145/3311957.3359501
Raber FKruger A(2018)Deriving Privacy Settings for Location Sharing: Are Context Factors Always the Best Choice?2018 IEEE Symposium on Privacy-Aware Computing (PAC)10.1109/PAC.2018.00015(86-94)Online publication date: Sep-2018
https://doi.org/10.1109/PAC.2018.00015

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