Wei, X orcid.org/0000-0002-6064-7290 and Stillwell, D (2017) How smart does your profile image look? Estimating intelligence from social network profile images. In: WSDM 2017 - Proceedings of the 10th ACM International Conference on Web Search and Data Mining. WSDM 2017, 06-10 Feb 2017 Association for Computing Machinery (ACM) , pp. 33-40. ISBN 9781450346757
Abstract
Profile images on social networks are users' opportunity to present themselves and to affect how others judge them. We examine what Facebook images say about users' perceived and measured intelligence. 1,122 Facebook users completed a matrices intelligence test and shared their current Facebook profile image. Strangers also rated the images for perceived intelligence. We use automatically extracted image features to predict both measured and perceived intelligence. Intelligence estimation from images is a difficult task even for humans, but experimental results show that human accuracy can be equalled using computing methods. We report the image features that predict both measured and perceived intelligence, and highlight misleading features such as "smiling'' and "wearing glasses'' that are correlated with perceived but not measured intelligence. Our results give insights into inaccurate stereotyping from profile images and also have implications for privacy, especially since in most social networks profile images are public by default.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in WSDM '17 Proceedings of the Tenth ACM International Conference on Web Search and Data Mining: http://dx.doi.org/10.1145/3018661.3018663 |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Business (Leeds) > Management Division (LUBS) (Leeds) > Management Division Decision Research (LUBS) |
Depositing User: | Symplectic Publications |
Date Deposited: | 04 Jul 2019 10:42 |
Last Modified: | 05 Jul 2019 08:35 |
Status: | Published |
Publisher: | Association for Computing Machinery (ACM) |
Identification Number: | 10.1145/3018661.3018663 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:148114 |