research-article

VeilMe: An Interactive Visualization Tool for Privacy Configuration of Using Personality Traits

Authors:

Michelle X. Zhou,

Hernan BadenesAuthors Info & Claims

CHI '15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

Pages 817 - 826

https://doi.org/10.1145/2702123.2702293

Published: 18 April 2015 Publication History

Abstract

With the recent advances in using data analytics to automatically infer one's personality traits from their social media data, users are facing a growing tension between the use of the technology to aid self development in workplace and the privacy concerns of such use. Given the richness of personality data that can be derived today and the varied sensitivity of revealing such data, it is a non-trivial task for users to configure their privacy settings for sharing and protecting their derived personality data. Here we present the design, development, and evaluation of an interactive visualization tool, VeilMe, which helps users configure the privacy settings for the use of their personality portraits derived from social media. Unlike other privacy configuration tools, our tool offers two distinct advantages. First, it presents a novel and intuitive visual interface that aids users in understanding and exploring their own personality traits derived from their social media data, and configuring their privacy preferences. Second, our tool helps users to jump start their privacy settings by suggesting initial sharing strategies based on a set of factors, including the users' personality and target audience. We have evaluated the use of our tool with 124 participants in an enterprise context. Our results show that VeilMe effectively supports various user privacy configuration tasks, and also suggest several design implications, including the approaches to personalized privacy configurations.

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VeilMe: An Interactive Visualization Tool for Privacy Configuration of Using Personality Traits

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cover image ACM Conferences

CHI '15: Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems

April 2015

4290 pages

ISBN:9781450331456

DOI:10.1145/2702123

General Chairs:
Bo Begole
Huawei, USA
,
Jinwoo Kim
Yonsei University, Korea
,
Program Chairs:
Kori Inkpen
Microsoft Research, USA
,
Woontack Woo
KAIST, Korea

Copyright © 2015 ACM.

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Published: 18 April 2015

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Musick GGilman EDuan WMcNeese NKnijnenburg BO'Neill T(2023)Knowing Unknown Teammates: Exploring Anonymity and Explanations in a Teammate Information-Sharing Recommender SystemProceedings of the ACM on Human-Computer Interaction10.1145/36100757:CSCW2(1-34)Online publication date: 4-Oct-2023
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