Predicting depression with social media images

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. Copyright © 2020 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

Авторы
Maxim S.1 , Ignatiev N. 2 , Smirnov I. 1, 2
Издательство
SciTePress
Язык
Английский
Страницы
235-240
Статус
Опубликовано
Год
2020
Организации
  • 1 Federal Research Center”Computer Science and Control” of RAS, Moscow, Russian Federation
  • 2 RUDN University, Moscow, Russian Federation
Ключевые слова
Classification; Depression; Image Recognition; Machine Learning; Social Media
Дата создания
02.11.2020
Дата изменения
02.11.2020
Постоянная ссылка
https://repository.rudn.ru/ru/records/article/record/65419/