Predicting Depression with Social Media Images

Stankevich Maxim

, Nikolay Ignatiev

and Ivan Smirnov

1,2

Federal Research Center ”Computer Science and Control” of RAS, Moscow, Russia

RUDN University, Moscow, Russia

Keywords:

Machine Learning, Classiﬁcation, Depression, Social Media, Image Recognition.

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.

1 INTRODUCTION

Depression is one of the most common mental disor-

ders in the world and it can signiﬁcantly affect the life

quality of individuals. According to the World Health

Organization, millions of people around the world

suffer from different forms of depression (Moussavi

et al., 2007). People affected by depression often

hide or ignore this fact of mental disorder presence,

and as a consequence, the large percentage of depres-

sion cases are left without professional and appropri-

ate treatment, which in the worst case can lead to sui-

cide. At the same time, there are effective psycholog-

ical and pharmacological treatments for depression.

Considering both facts, developing methods that can

detect signs of depression in population is of great in-

terest.

Social networks considered by researches as an in-

exhaustible source of data that can be used to study

human behavior in modern society. There are a grow-

ing amount of studies devoted to the task of as-

sessing mental health, personality traits and socio-

demographic characteristic of peoples by analyzing

social media data. Currently, this problem mostly rep-

resented as a machine learning task. Even if most

studies analyses text data, there is possibility to use

images posted by users to address the problem.

The study describes the task of predicting de-

pression of users by analyzing different types of im-

ages posted on social media. We formed a dataset

that matches 398 Back Depression Inventory screen-

ings and 485121 images posted by users in Russian-

speaking social media Vkontakte. We separated our

data on 3 parts: proﬁle photos, images attached to

users’ posts and custom albums. The data were pro-

cessed to retrieve 80 object classes by utilizing Faster

R-CNN trained on the COCO dataset (Lin et al.,

2014) and color properties of images. To perform on

the depression detection task, we evaluated 3 different

sets of users’ images and retrieved features by train-

ing various machine learning methods.

2 RELATED WORK

Social networks are considered, by researchers, as

unique sources of information about individuals and

their social relationships, and modern methods of data

analysis allow us to build accurate prediction mod-

els of human behavior. Numerous studies show that

the analysis of personal pages of a social network

user can be a source of information not only about

the socio-demographic characteristics of the user but

also about their personality traits, psychological pref-

erences, and current psychological states. For exam-

ple, to solve the problem of classifying users based on

the ﬁve-factor model of human personality traits, an

analysis of a large corpus of text messages from Face-

book users was performed (Schwartz et al., 2013). It

is important to note that neuroticism and extroversion

Maxim, S., Ignatiev, N. and Smirnov, I.

Predicting Depression with Social Media Images.

DOI: 10.5220/0009168602350240

In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2020), pages 235-240

ISBN: 978-989-758-397-1; ISSN: 2184-4313

235

personality traits can serve as predictors of depres-

sion (Widiger and Mullins-Sweatt, 2010). In another

study, researchers evaluated the possibility of iden-

tifying user personal traits and socio-demographic

characteristics based on information about the users

liked content (Kosinski et al., 2013). Data about

music preference, from the online resource last.fm,

was used to identify reliable correlations between user

personality features and music preferences (Ferwerda

et al., 2017). It is worth noting that the most popular

approach in such studies is to create a dataset, consist-

ing of user data from a social network and the result

of a specialized questionnaire.

Currently, researchers are most interested in the

task of assessing mental health, based on informa-

tion gathered from social networks and other online

resources (Shatte et al., 2019; Kursuncu et al., 2019).

For example, for CLEF/eRisk 2018, project partici-

pants were provided with a collection of text mes-

sages from Reddit, for the purpose of detecting de-

pression and anorexia among its users (Losada et al.,

2018). The task was presented in the form of a bi-

nary classiﬁcation, where participants were supposed

to build a prediction model based on the training data

that was given to them. According to the results of

the project, the best F1-score for the task of detecting

depression was 64%, and the best F1- score for the de-

tection of anorexia was 85%. Another study proposed

a dynamic assessment of the severity of the 9 major

symptoms of depression based on a semi-supervised

machine learning method. (Yazdavar et al., 2017).

The approach was tested on a dataset consisting of 23

million Twitter posts and showed a 68% average pre-

diction accuracy of 9 symptoms of depression. The

following symptoms were determined with the great-

est accuracy: loss of interest, depressed mood, and

eating disorders.

Even though most of these types of classiﬁca-

tions are done using data gathered from text, there

are several studies that use data gathered from im-

ages (Wongkoblap et al., 2017). Images from the

popular social network Instagram were used to iden-

tify depression among its users (Reece and Dan-

forth, 2017). With the help of Amazons Mechanical

Turk crowd-sourcing platform (MTurk), a dataset was

gathered, consisting of 43950 photos from 166 volun-

teers. Workers sourced from MTurk were tasked with

classifying depression in users based on the images in

the dataset. These classiﬁcations were used to com-

pare the efﬁcacy of the proposed machine learning

model against the efﬁcacy of humans performing the

same task. To train the model, the following features

were extracted from the collected data: indicators of

activity on the social network, color parameters of the

photo, the presence of color ﬁlters, and the number of

faces in the images. The proposed model was able to

classify depression with an F1-score of around 65%.

The most signiﬁcant predictors of depression in this

model were: hue, saturation, brightness, face count,

face presence, whether or not a ﬁlter was used, and

what type of ﬁlter was used.

According to research, emotions and mental char-

acteristics of a person have certain connections to

their color preferences (Nolan et al., 1995; Valdez and

Mehrabian, 1994). In turn, the connection between

Flickr user personalities and the color characteristics

of their uploaded photos were discovered based on

32056 photos and the results of a standard question-

naire of personality traits (Wieloch et al., 2018). It

is also worth noting the study where researchers were

looking for correlations between personality traits and

the frequency of encounters of certain groups of ob-

jects, that were classiﬁed with the help of Googles

Vision API (Ferwerda and Tkalcic, 2018).

Data gathered from Flickr was used in the creation

of regression models, capable of predicting the sever-

ity of certain user personality traits (Segalin et al.,

2016). The authors analyzed 60000 favorite images

(200 from each of the 300 users) and extracted many

color, composition, and texture characteristics, that

were used as features in the training model. A total

of 2 separate experiments were conducted: predicting

personality traits, using the results of a questionnaire

as the target parameter, and the prediction of person-

ality traits using the scores judgment from other users,

that evaluated user personality traits based on the pho-

tos that those users uploaded. Even if the second ex-

periment was successful, the ﬁrst experiment demon-

strated a determination coefﬁcient R2 of less than 0.1

among all personality traits. This study was contin-

ued in another work, where the authors set the task of

binary classiﬁcation between high and low levels of

displayed personality traits on the same dataset, using

a pre-trained convolutional neural network determin-

ing the feature set (Segalin et al., 2017). The classiﬁ-

cation accuracy in this experiment ranged from 61%

to 69% for various personality traits.

In another study, researchers used data from Twit-

ter proﬁles to ﬁnd correlations between the images

that users posted and whether the users posting those

images were depressed or anxious (Guntuku et al.,

2019). The authors used a sample of 28749 Face-

book users to build a language prediction model for

depression and anxiety. This model was used to pre-

dict depression and anxiety in a different set of 4132

Twitter users. The researchers extracted data from

these twitter users posted and proﬁle pictures. This

dataset included HSV (Hue-Saturation-Value) data,

ICPRAM 2020 - 9th International Conference on Pattern Recognition Applications and Methods

236

Table 1: Data statistics.

Set Image count Mean Std median User count

All

Avatars 4098 10.29 10.63 10

398Posts 23678 62.00 81.00 60

Albums 457345 1149.10 1707.09 1170

Control

Avatars 2081 10.35 10.49 10

201Posts 12015 59.78 79.28 55

Albums 227786 1133.26 1849.21 988

Depression

Avatars 2017 10.23 10.72 10

197Posts 11663 59.20 81.84 45

Albums 229559 1165.27 1825.97 1006

image aesthetics data, image content data, and face

and emotion data. The authors of this study have

found that depressed users tend to post more photos

that suppress positive emotions (rather than exhibiting

negative emotions); photos that are less aesthetically

pleasing than photos of non-depressed users; photos

which are not sharp, and which do not contain faces;

gray-scale photos.

Researchers in another study created a prediction

model using posts data from Instagram containing

hashtags related to depression (Huang et al., 2019).

The authors of this study chose this approach because

it allows for a cheaper and faster way of collecting

data about depressed users, compared to the more

common use of the depression questionnaires. Tex-

tual, behavioral, and image features were extracted

from Instagram data and a CNN (convolutional neural

network) was used to score depression on photos in

the dataset. The researchers employed transfer learn-

ing from ImageNet in order to speed up and improve

the performance of their predictive model. The best

image features based model in their experiments was

able to classify depression with an F1-score of 77%.

3 METHODS

3.1 Data Collection

The data for the study was collected from popular

Russian-speaking social media Vkontakte. To collect

the data we built a web-application that allows vol-

unteers to authorize via Vkontakte API. The volun-

teers were requested for permission to access the data

from their personal proﬁles in social media. Then

we asked them to fulﬁll a Russian adaptation of de-

pression questionnaire based on Beck Depression In-

ventory (Beck et al., 1996). The results of the ques-

tionnaire represent a depression score which is inte-

ger value on the 0-63 scale. Vkontakte has a com-

plex structure in comparison to Instagram and Flickr.

There are several different sources of users’ images

that we can retrieve from proﬁles.

Avatars. All images that were used by users as the

main proﬁle photo.

Posts. Images that were attached to users’ posts in

their proﬁles including images attached to reposts

(similar to retweets).

Albums. Vkontakte users can create their own al-

bums and ﬁll them with any type of images. We col-

lect all images from users’ custom albums which are

not closed.

Overall, data was collected from more than 1000

Vkontakte users. To aim binary classiﬁcation task we

deﬁned depression and control groups by using top

and bottom quartiles of Beck Depression Inventory

scores. All users with a score that less than bottom

quartile value were annotated as a control group and

all users with a score that more than top quartile value

as a depression group. Users with middle scores were

removed from observation as well as users with less

than 5 images in at least one of the sets. These steps

yielded a dataset that consists of data from 197 de-

pressed and 201 non-depressed users. The similar

approach of splitting data into two groups according

to questionnaire scores was also implemented in re-

lated works, for example in (Iacobelli et al., 2011) and

(De Choudhury et al., 2013). The general statistics on

the data presented in Table 1.

3.2 Object Features

To retrieve features from users’ images we formed

vectors that characterize the presence of different ob-

jects on them. The number of human faces on images

was utilized as a feature for depression detection in

(Reece and Danforth, 2017). We decided to extend

this idea with other types of objects. Faster R-CNN

model (Ren et al., 2015) was trained on the COCO

Predicting Depression with Social Media Images

237

dataset (Lin et al., 2014) to be capable of detecting

the presence of 80 different object types. The thresh-

old value that determines the minimal required prob-

ability yielded by the model to count this object has

been set to 0.6 in our experiments. We implemented

different strategies to calculate objects vector. As (I)

strategy we calculated the probability of meeting the

object on the photo. As (II) strategy the vectors were

formed from the objects presence frequency, which

was calculated on the basis of the probability values

given by the detector where these values were more

than the threshold. The objects vectors with (III)

strategy were formed by calculating object frequency

as well, but instead of using raw probability values we

rounded them to 1. Computed values were divided by

a number of images provided by users (except (I)).

Objects with overall sum by all users did not exceed

0.0001 were removed from data.

3.3 Color Features

Other features were retrieved from the color proper-

ties of images. We utilized OpenCV library (Bradski

and Kaehler, 2008) to compute components of follow-

ing color spaces RGB, HSV, XYZ, and LAB. We used

averaged values of these properties and standard de-

viation to form color features for all sets.

4 RESULTS

To perform on depression detection task we tested

following machine learning algorithms: Logistic

Regression (LR), Support Vector Machine (SVM),

Multi-layer Perceptron (MLP), Random Forest (RF),

Naive Bias (NB), k-Nearest Neighbors (KNN), Cat-

Boost (CAT) (Dorogush et al., 2018), and random

based classiﬁer (RAND). Regardless of the exper-

iment and observed features, we split each of the

avatars, posts, and albums sets on 80% for train data

and 20% for test data. All hyperparameters of clas-

siﬁcation models were tuned by grid-search with 5-

fold cross-validation on train data. We also included

the number of feature dimensions yielded by principal

component analysis (PCA) performed on our sets as

an additional hyperparameter for grid-search. All re-

sults presented as a F1-score for depression class. As

a ﬁrst step we evaluated the best strategies for each

of avatars, posts, and albums sets. We trained all of

the mentioned classiﬁer algorithms and outlined best

performances in Table 2,

According to Table 2, the choice of strategy did

not affect the quality of classiﬁcation with avatars im-

ages. For the posts set the (I) strategy performed

Table 2: Result of experiments using different strategies.

Image set (I) (II) (III)

Avatars .6554 .6554 .6554

Posts .6554 .6408 .6361

Albums .5572 .5481 .6518

with the highest F1-score. Surprisingly, classiﬁcation

based on the object features from albums set achieved

poor results with (I) and (II) strategies compared to

(III), which we link to the chosen threshold value and

big amount of image data in this set. On the next

step, we performed classiﬁcation with color features

and different combinations of object and color fea-

tures using the best strategies for each image source

(see Table 3).

Overall, Multi-layer Perceptron, CatBoost, and

Naive Bias performed better than other models. The

best result with objects only features achieved on

Avatars (obj), Posts (obj) sets with 65.54% F1-score

by MLP classiﬁer. According to the results, color fea-

tures demonstrate inferior results comparing to ob-

jects and yielded 62.33% of F1-score with All sets

(col), which is a concatenated vector of color fea-

tures from all sets. We observed several classiﬁcation

runs by CatBoost model with avatars and posts ob-

jects without PCA processing to retrieve feature im-

portance that was computed trough the training pro-

cess (see Table 4). It is interesting to note, that ob-

ject person has a high feature importance value since

it corresponds to the analysis reported in (Reece and

Danforth, 2017) where the number of human faces on

photos was also applied as a valuable feature.

By analyzing related works we came to the con-

clusion that it is hard to strictly compare our work

with other studies. We observed two related work:

(Huang et al., 2019) and (Reece and Danforth, 2017).

Both of them are based on Instagram data, which

mostly consist of real photos uploaded by users, and

this differs from Vkontakte format. The posts set

might be considered as most similar to Instagram

data, but it also contained images from reposts, which

are usually pictures and photos that are only indirectly

related to user.

Authors of (Huang et al., 2019) followed the idea

presented in (De Choudhury et al., 2016) and col-

lected data by crawling Instagram posts with indica-

tive words: ”depression” and ”suicide” for depressed

users and ”happy” for control users. This work

presents interesting results but implemented data col-

lection methods differ from questionnaire screening

and it is still not clearly evident that we compare these

approaches. The work presented in (Reece and Dan-

forth, 2017) has more similarities with ours. The class

partition in this study is 43% (71 users) for the depres-

ICPRAM 2020 - 9th International Conference on Pattern Recognition Applications and Methods

238

Table 3: Result of experiments on different combinations of features and image sets. All results presented as F1-score for

depression class. obj - object features; col - color features.

Feature set RAND LR SVM MLP RF CAT NB KNN

Avatars (obj) .4858 .5175 .5249 .6554 .5166 .5977 .6256 .5779

Avatars (col) - .5376 .5482 .4988 .5636 .6172 .5128 .5469

Avatars (obj+col) - .5669 .5395 .5243 .5889 .6000 .6245 .5374

Posts (obj) - .5263 .5113 .6554 .5271 .5925 .6338 .5547

Posts (col) - .4958 .4854 .5214 .5255 .5609 .5033 .5509

Posts (obj+col) - .5250 .4904 .6554 .5234 .5185 .6338 .5273

Albums (obj) - .5124 .5020 .6518 .5271 .5783 .4467 .5539

Albums (col) - .5183 .4700 .4976 .5217 .5542 .4500 .5178

Albums (obj+col) - .5298 .4767 .5243 .5279 .5952 .6245 .5390

All sets (obj) - .5452 .5380 .4972 .5377 .5609 .6254 .5333

All sets (col) - .5059 .5242 .5536 .5676 .6233 .4885 .5145

All sets (obj+col) - .5361 .5575 .6554 .5172 .5542 .6118 .5240

Table 4: Feature importance.

Object type Source Importance

person Posts 5.05

tie Posts 3.43

cat Posts 3.11

cat Avatars 2.59

car Avatars 2.16

clock Avatars 2.02

person Avatars 1.83

cup Posts 1.80

clock Posts 1.69

tv Posts 1.62

sion group and 57% (95 users) for the control group.

In additional to depression questionnaire screening

they also asked volunteers about depression history

and make use of this information to form 2 sets of ex-

periments: classiﬁcation using all data and classiﬁca-

tion using posts submitted by depressed users before

the ﬁrst depression incident (pre-diagnosis). The best

result for all-data was 64.7% of F1-score and 40.1%

for pre-diagnosis.

5 CONCLUSION

The present study is focused on the task of predicting

depression by using images posted by users on social

media. We built a dataset that consists of images col-

lected from Vkontakte and scores of Beck Depression

Inventory screenings which were used to determine

the binary classiﬁcation task. To perform on the task

we retrieved object and color features from users’ im-

ages. Our experiments demonstrated that by utilizing

data from different sources of images in social media

such as proﬁle photos, images attached to the posts

and custom albums it is possible to retrieve useful fea-

tures. The best performances were achieved by Multi-

layer Perceptron based classiﬁer using object features

with 65.54% of F1-score.

We believe that to achieve better results it is nec-

essary to apply some constraints on the step of data

pre-processing. First, it seems fair that we should

deal with outliers in the data and adjust the amount

of provided images from each user to the same num-

ber. Secondly, it is important to consider the speci-

ﬁcity of the aimed task and impose time constraints

on the data by observing only the users’ images that

were posted during a short time period before ques-

tionnaire screening. As a general idea for future work,

we planning to apply these methods to our previous

research (Stankevich et al., 2019) where we analyzed

text messages to perform on the same task.

ACKNOWLEDGEMENTS

The reported study was funded by RFBR according to

the research project 17-29-02225.

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