Predicting Depression with Social Media Images

Stankevich Maxim, Nikolay Ignatiev, Ivan Smirnov, Ivan Smirnov

2020

Abstract

The study is focused on the task of depression detection by analyzing images related to social media users. We formed a dataset that consists of 485,121 images from profiles of 398 volunteers that provided access to their data in popular Russian-speaking social media Vkontakte. The results of the depression questionnaire were used to distinguish depression and control groups and set the binary classification task. We observed 3 types of users’ images: profile photos, images from posts, and albums. We applied object detection methods to retrieve object features that determine the presence of 80 different object classes on users’ images. To aim the task, the different machine learning algorithms were trained on the objects and color features. Our models achieved up to 65.5% F1-score for the task of revealing depressed users.

Paper Citation

in Harvard Style

Maxim S., Ignatiev N. and Smirnov I. (2020). Predicting Depression with Social Media Images. In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-397-1, pages 235-240. DOI: 10.5220/0009168602350240

in Bibtex Style

@conference{icpram20,
author={Stankevich Maxim and Nikolay Ignatiev and Ivan Smirnov},
title={Predicting Depression with Social Media Images},
booktitle={Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2020},
pages={235-240},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009168602350240},
isbn={978-989-758-397-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Predicting Depression with Social Media Images
SN - 978-989-758-397-1
AU - Maxim S.
AU - Ignatiev N.
AU - Smirnov I.
PY - 2020
SP - 235
EP - 240
DO - 10.5220/0009168602350240