Details
| Original language | English |
|---|---|
| Title of host publication | User Modeling, Adaptation and Personalization - 23rd International Conference, UMAP 2015, Proceedings |
| Editors | Kalina Bontcheva, Francesco Ricci, Owen Conlan, Séamus Lawless |
| Publisher | Springer Verlag |
| Pages | 104-117 |
| Number of pages | 14 |
| ISBN (electronic) | 9783319202662 |
| Publication status | Published - 11 Jun 2015 |
| Event | 23rd International Conference on User Modeling, Adaptation and Personalization, UMAP 2015 - Dublin, Ireland Duration: 29 Jun 2015 → 3 Jul 2015 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 9146 |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
Social networks provide a platform for people to connect and share information and moments of their lives. With the increasing engagement of users in such platforms, the volume of personal information that is exposed online grows accordingly. Due to carelessness, unawareness or difficulties in defining adequate privacy settings, private or sensitive information may be exposed to a wider audience than intended or advisable, potentially with serious problems in the private and professional life of a user. Although these causes usually receive public attention when it involves companies’ higher managing staff, athletes, politicians or artists, the general public is also subject to these issues. To address this problem, we envision a mechanism that can suggest users the appropriate privacy setting for their posts taking into account their profiles. In this paper, we present a thorough analysis of privacy settings in Facebook posts and evaluate prediction models that can anticipate the desired privacy settings with high accuracy, making use of the users’ previous posts and preferences.
Keywords
- Facebook, Privacy, Social networks
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
- General Computer Science
Cite this
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User Modeling, Adaptation and Personalization - 23rd International Conference, UMAP 2015, Proceedings. ed. / Kalina Bontcheva; Francesco Ricci; Owen Conlan; Séamus Lawless. Springer Verlag, 2015. p. 104-117 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9146).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Analyzing and predicting privacy settings in the social web
AU - Naini, Kaweh Djafari
AU - Altingovde, Ismail Sengor
AU - Kawase, Ricardo
AU - Herder, Eelco
AU - Niederée, Claudia
PY - 2015/6/11
Y1 - 2015/6/11
N2 - Social networks provide a platform for people to connect and share information and moments of their lives. With the increasing engagement of users in such platforms, the volume of personal information that is exposed online grows accordingly. Due to carelessness, unawareness or difficulties in defining adequate privacy settings, private or sensitive information may be exposed to a wider audience than intended or advisable, potentially with serious problems in the private and professional life of a user. Although these causes usually receive public attention when it involves companies’ higher managing staff, athletes, politicians or artists, the general public is also subject to these issues. To address this problem, we envision a mechanism that can suggest users the appropriate privacy setting for their posts taking into account their profiles. In this paper, we present a thorough analysis of privacy settings in Facebook posts and evaluate prediction models that can anticipate the desired privacy settings with high accuracy, making use of the users’ previous posts and preferences.
AB - Social networks provide a platform for people to connect and share information and moments of their lives. With the increasing engagement of users in such platforms, the volume of personal information that is exposed online grows accordingly. Due to carelessness, unawareness or difficulties in defining adequate privacy settings, private or sensitive information may be exposed to a wider audience than intended or advisable, potentially with serious problems in the private and professional life of a user. Although these causes usually receive public attention when it involves companies’ higher managing staff, athletes, politicians or artists, the general public is also subject to these issues. To address this problem, we envision a mechanism that can suggest users the appropriate privacy setting for their posts taking into account their profiles. In this paper, we present a thorough analysis of privacy settings in Facebook posts and evaluate prediction models that can anticipate the desired privacy settings with high accuracy, making use of the users’ previous posts and preferences.
KW - Facebook
KW - Privacy
KW - Social networks
UR - http://www.scopus.com/inward/record.url?scp=84937396159&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-20267-9_9
DO - 10.1007/978-3-319-20267-9_9
M3 - Conference contribution
AN - SCOPUS:84937396159
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 104
EP - 117
BT - User Modeling, Adaptation and Personalization - 23rd International Conference, UMAP 2015, Proceedings
A2 - Bontcheva, Kalina
A2 - Ricci, Francesco
A2 - Conlan, Owen
A2 - Lawless, Séamus
PB - Springer Verlag
T2 - 23rd International Conference on User Modeling, Adaptation and Personalization, UMAP 2015
Y2 - 29 June 2015 through 3 July 2015
ER -