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Authors: Paolo Cappellari 1 ; Soon Chun 1 and Christopher Costello 2

Affiliations: 1 College of Staten Island, City University of New York, New York and U.S.A. ; 2 Macaulay Honors College, City University of New York, New York and U.S.A.

ISBN: 978-989-758-318-6

Keyword(s): Privacy, Text Analytics, Machine Learning, Social Media.

Abstract: Social network platforms are changing the way people interact not just with each other but also with companies and institutions. In sharing information on these platforms, users often underestimate potential consequences, especially when such information discloses personal information. For such reason, actionable privacy awareness and protection mechanisms are becoming of paramount importance. In this paper we propose an approach to assess the privacy content of the social posts with the goal of: protecting the users from inadvertently disclosing sensitive information, and rising awareness about privacy in online behavior. We adopt a machine learning approach based on a crowd-sourced definition of privacy that can assess whether messages are disclosing sensitive information. Our approach can automatically detect messages carrying sensitive information, so to warn users before sharing a post, and provides a set of analysis to rise users awareness about online behavior related to privac y disclosure. (More)

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Paper citation in several formats:
Cappellari P., Chun S. and Costello C. (2018). Detecting and Analyzing Privacy Leaks in Tweets.In Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-318-6, pages 265-275. DOI: 10.5220/0006845602650275

@conference{data18,
author={Paolo Cappellari and Soon Chun and Christopher Costello},
title={Detecting and Analyzing Privacy Leaks in Tweets},
booktitle={Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2018},
pages={265-275},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006845602650275},
isbn={978-989-758-318-6},
}

TY - CONF

JO - Proceedings of the 7th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Detecting and Analyzing Privacy Leaks in Tweets
SN - 978-989-758-318-6
AU - Cappellari P.
AU - Chun S.
AU - Costello C.
PY - 2018
SP - 265
EP - 275
DO - 10.5220/0006845602650275

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