Predicting Depression with Text, Image, and Profile Data from Social Media

N. Ignatiev, N. Ignatiev, I. Smirnov, I. Smirnov, M. Stankevich

2022

Abstract

In this study, we focused on the task of identifying depressed users based on their digital media on a social network. We processed over 60,000 images, 95,000 posts, and 9,000 subscription items related to 619 user profiles on the VKontakte social media network. Beck Depression Inventory screenings were used to assess the presence of depression among these users and divide them into depression and control groups. We retrieved 6 different text based feature sets, images, and general profile data. The experimental evaluation was designed around using all available data from user profiles and creating a prediction pipeline that can process data samples regardless of the availability of text or image data in the user profile. The best result achieved a 69% F1-score with a stacking classifier approach.

Paper Citation

in Harvard Style

Ignatiev N., Smirnov I. and Stankevich M. (2022). Predicting Depression with Text, Image, and Profile Data from Social Media. In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-549-4, pages 753-760. DOI: 10.5220/0010986100003122

in Bibtex Style

@conference{icpram22,
author={N. Ignatiev and I. Smirnov and M. Stankevich},
title={Predicting Depression with Text, Image, and Profile Data from Social Media},
booktitle={Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2022},
pages={753-760},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010986100003122},
isbn={978-989-758-549-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Predicting Depression with Text, Image, and Profile Data from Social Media
SN - 978-989-758-549-4
AU - Ignatiev N.
AU - Smirnov I.
AU - Stankevich M.
PY - 2022
SP - 753
EP - 760
DO - 10.5220/0010986100003122