NDLI: Machine prediction of personality from Facebook profiles

Content Provider	IEEE Xplore Digital Library
Author	Wald, R. Khoshgoftaar, T. Sumner, C.
Copyright Year	2012
Description	Author affiliation: Online Privacy Foundation, USA (Sumner, C.) \|\| Florida Atlantic University, USA (Wald, R.; Khoshgoftaar, T.)
Abstract	An increasing number of Americans use social networking sites such as Facebook, but few fully appreciate the amount of information they share with the world as a result. Although studies exist on the sharing of specific types of information (photos, posts, etc.), one area that has been less explored is how Facebook profiles can share personality information in a broad, machine-readable fashion. In this study, we apply data-mining and machine learning techniques to predict users' personality traits (specifically, the traits of the Big Five personality model) using only demographic and text-based attributes extracted from their profiles. We then use these predictions to rank individuals in terms of the five traits, predicting which users will appear in the top or bottom 5% or 10% of these traits. Our results show that when using certain models, we can find the top 10% most Open individuals with nearly 75% accuracy, and across all traits and directions, we can predict the top 10% with at least 34.5% accuracy (exceeding 21.8%, which is the best accuracy when using just the best-performing profile attribute). These results have privacy implications in terms of allowing advertisers and other groups to focus on a specific subset of individuals based on their personality traits.
Starting Page	109
Ending Page	115
File Size	296734
Page Count	7
File Format	PDF
ISBN	9781467322829
e-ISBN	9781467322843
e-ISBN	9781467322836
DOI	10.1109/IRI.2012.6302998
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-08-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Facebook Numerical models Predictive models Privacy Data mining Humans personality prediction Big Five privacy data mining
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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