Inferring social media users' demographics from profile pictures: A Face++ analysis on twitter users

Soon Gyo Jung, Jisun An, Haewoon Kwak, Joni Salminen, Bernard Jansen

Research output: Contribution to journalConference article

2 Citations (Scopus)

Abstract

In this research, we evaluate the applicability of using facial recognition of social media account profile pictures to infer the demographic attributes of gender, race, and age of the account owners leveraging a commercial and well-known image service, specifically Face++. Our goal is to determine the feasibility of this approach for actual system implementation. Using a dataset of approximately 10,000 Twitter profile pictures, we use Face++ to classify this set of images for gender, race, and age. We determine that about 30% of these profile pictures contain identifiable images of people using the current state-of-the-art automated means. We then employ human evaluations to manually tag both the set of images that were determined to contain faces and the set that was determined not to contain faces, comparing the results to Face++. Of the thirty percent that Face++ identified as containing a face, about 80% are more likely than not the account holder based on our manual classification, with a variety of issues in the remaining 20%. Of the images that Face++ was unable to detect a face, we isolate a variety of likely issues preventing this detection, when a face actually appeared in the image. Overall, we find the applicability of automatic facial recognition to infer demographics for system development to be problematic, despite the reported high accuracy achieved for image test collections.

Original languageEnglish
Pages (from-to)140-145
Number of pages6
JournalProceedings of the International Conference on Electronic Business (ICEB)
Volume2017-December
Publication statusPublished - 1 Jan 2017
Event17th International Conference on Electronic Business: Smart Cities, ICEB 2017 - Al Barsha, Dubai, United Arab Emirates
Duration: 4 Dec 20178 Dec 2017

Fingerprint

Social media
Demographics
Twitter
System development
Owners
Evaluation
System implementation
Tag
Test collections

Keywords

  • Demographic inference
  • Demographics
  • Face++
  • Personas
  • Social media
  • Twitter
  • User attributes

ASJC Scopus subject areas

  • Business, Management and Accounting(all)
  • Computer Science(all)

Cite this

Inferring social media users' demographics from profile pictures : A Face++ analysis on twitter users. / Jung, Soon Gyo; An, Jisun; Kwak, Haewoon; Salminen, Joni; Jansen, Bernard.

In: Proceedings of the International Conference on Electronic Business (ICEB), Vol. 2017-December, 01.01.2017, p. 140-145.

Research output: Contribution to journalConference article

@article{61a27d93a4f64c19b57dc1243ab87058,
title = "Inferring social media users' demographics from profile pictures: A Face++ analysis on twitter users",
abstract = "In this research, we evaluate the applicability of using facial recognition of social media account profile pictures to infer the demographic attributes of gender, race, and age of the account owners leveraging a commercial and well-known image service, specifically Face++. Our goal is to determine the feasibility of this approach for actual system implementation. Using a dataset of approximately 10,000 Twitter profile pictures, we use Face++ to classify this set of images for gender, race, and age. We determine that about 30{\%} of these profile pictures contain identifiable images of people using the current state-of-the-art automated means. We then employ human evaluations to manually tag both the set of images that were determined to contain faces and the set that was determined not to contain faces, comparing the results to Face++. Of the thirty percent that Face++ identified as containing a face, about 80{\%} are more likely than not the account holder based on our manual classification, with a variety of issues in the remaining 20{\%}. Of the images that Face++ was unable to detect a face, we isolate a variety of likely issues preventing this detection, when a face actually appeared in the image. Overall, we find the applicability of automatic facial recognition to infer demographics for system development to be problematic, despite the reported high accuracy achieved for image test collections.",
keywords = "Demographic inference, Demographics, Face++, Personas, Social media, Twitter, User attributes",
author = "Jung, {Soon Gyo} and Jisun An and Haewoon Kwak and Joni Salminen and Bernard Jansen",
year = "2017",
month = "1",
day = "1",
language = "English",
volume = "2017-December",
pages = "140--145",
journal = "Proceedings of the International Conference on Electronic Business (ICEB)",
issn = "1683-0040",

}

TY - JOUR

T1 - Inferring social media users' demographics from profile pictures

T2 - A Face++ analysis on twitter users

AU - Jung, Soon Gyo

AU - An, Jisun

AU - Kwak, Haewoon

AU - Salminen, Joni

AU - Jansen, Bernard

PY - 2017/1/1

Y1 - 2017/1/1

N2 - In this research, we evaluate the applicability of using facial recognition of social media account profile pictures to infer the demographic attributes of gender, race, and age of the account owners leveraging a commercial and well-known image service, specifically Face++. Our goal is to determine the feasibility of this approach for actual system implementation. Using a dataset of approximately 10,000 Twitter profile pictures, we use Face++ to classify this set of images for gender, race, and age. We determine that about 30% of these profile pictures contain identifiable images of people using the current state-of-the-art automated means. We then employ human evaluations to manually tag both the set of images that were determined to contain faces and the set that was determined not to contain faces, comparing the results to Face++. Of the thirty percent that Face++ identified as containing a face, about 80% are more likely than not the account holder based on our manual classification, with a variety of issues in the remaining 20%. Of the images that Face++ was unable to detect a face, we isolate a variety of likely issues preventing this detection, when a face actually appeared in the image. Overall, we find the applicability of automatic facial recognition to infer demographics for system development to be problematic, despite the reported high accuracy achieved for image test collections.

AB - In this research, we evaluate the applicability of using facial recognition of social media account profile pictures to infer the demographic attributes of gender, race, and age of the account owners leveraging a commercial and well-known image service, specifically Face++. Our goal is to determine the feasibility of this approach for actual system implementation. Using a dataset of approximately 10,000 Twitter profile pictures, we use Face++ to classify this set of images for gender, race, and age. We determine that about 30% of these profile pictures contain identifiable images of people using the current state-of-the-art automated means. We then employ human evaluations to manually tag both the set of images that were determined to contain faces and the set that was determined not to contain faces, comparing the results to Face++. Of the thirty percent that Face++ identified as containing a face, about 80% are more likely than not the account holder based on our manual classification, with a variety of issues in the remaining 20%. Of the images that Face++ was unable to detect a face, we isolate a variety of likely issues preventing this detection, when a face actually appeared in the image. Overall, we find the applicability of automatic facial recognition to infer demographics for system development to be problematic, despite the reported high accuracy achieved for image test collections.

KW - Demographic inference

KW - Demographics

KW - Face++

KW - Personas

KW - Social media

KW - Twitter

KW - User attributes

UR - http://www.scopus.com/inward/record.url?scp=85050642516&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85050642516&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:85050642516

VL - 2017-December

SP - 140

EP - 145

JO - Proceedings of the International Conference on Electronic Business (ICEB)

JF - Proceedings of the International Conference on Electronic Business (ICEB)

SN - 1683-0040

ER -