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Understanding Privacy Dichotomy in Twitter

Published: 03 July 2018 Publication History

Abstract

Balancing personalization and privacy is one of the challenges marketers commonly face. The privacy dilemmas associated with personalized services are particularly concerning in the context of social networking websites, wherein the privacy dichotomy problem is widely observed. To prevent potential privacy violations, businesses need to employ multiple safeguards beyond the current privacy settings of users. As a possible solution, companies can utilize user social footprints to detect user privacy preferences. To take a step towards this goal, we first ran a series of experiments to examine if the privacy preference attribute is homophilous in social media. As a result, we found a set of clues that users' privacy preferences are similar to the privacy behaviour of their social contacts, signaling that privacy homophily exists in social networks. We further studied users located in different neighbourhoods with varying degrees of privacy and found a set of characteristics that are specific to public users located in private neighbourhoods. These identified features can be used in a predictive model to identify public user accounts that are intended to be private, supporting companies to make an informed decision whether or not to exploit one's publicly available data for personalization purposes.

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Cited By

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  • (2023)Perspectives of non-expert users on cyber security and privacyComputers and Security10.1016/j.cose.2022.103008125:COnline publication date: 1-Feb-2023
  • (2023)Privacy Measurement Based on Social Network Properties and StructureBig Data and Security10.1007/978-981-99-3300-6_38(537-551)Online publication date: 31-May-2023
  • (2021)Petit mode d’emploi des médias sociaux à l’usage des personnes malveillantesShort instructions for villains on how to use social mediaBreve manual sobre el uso de los medios sociales por personas malintencionadasRevue d'anthropologie des connaissances10.4000/rac.1972715:1Online publication date: 1-Mar-2021
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cover image ACM Conferences
HT '18: Proceedings of the 29th on Hypertext and Social Media
July 2018
266 pages
ISBN:9781450354271
DOI:10.1145/3209542
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 03 July 2018

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Author Tags

  1. preference detection
  2. social network analysis
  3. social privacy

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  • Research-article

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  • MITACS Accelerate Program

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HT '18 Paper Acceptance Rate 19 of 69 submissions, 28%;
Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

View all
  • (2023)Perspectives of non-expert users on cyber security and privacyComputers and Security10.1016/j.cose.2022.103008125:COnline publication date: 1-Feb-2023
  • (2023)Privacy Measurement Based on Social Network Properties and StructureBig Data and Security10.1007/978-981-99-3300-6_38(537-551)Online publication date: 31-May-2023
  • (2021)Petit mode d’emploi des médias sociaux à l’usage des personnes malveillantesShort instructions for villains on how to use social mediaBreve manual sobre el uso de los medios sociales por personas malintencionadasRevue d'anthropologie des connaissances10.4000/rac.1972715:1Online publication date: 1-Mar-2021
  • (2021)Digital Inequality Through the Lens of Self-DisclosureProceedings on Privacy Enhancing Technologies10.2478/popets-2021-00522021:3(373-393)Online publication date: 27-Apr-2021
  • (2020)Do My Emotions Influence What I Share? Analysing the Effects of Emotions on Privacy Leakage in Twitter2020 IEEE 19th International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)10.1109/TrustCom50675.2020.00165(1228-1235)Online publication date: Dec-2020
  • (2020)Using User Behavior to Measure Privacy on Online Social NetworksIEEE Access10.1109/ACCESS.2020.30007808(108387-108401)Online publication date: 2020
  • (2018)A Villain’s Guide to Social Media and Interactive Digital StorytellingInteractive Storytelling10.1007/978-3-030-04028-4_4(50-61)Online publication date: 21-Nov-2018

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