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

Media

N. Ignatiev

1,2 a

, I. Smirnov

1,2 b

and M. Stankevich

1 c

Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Moscow, Russia

Peoples Friendship University of Russia (RUDN University), Russia

Keywords:

Depression Detection, Social Media, Classiﬁcation.

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.

1 INTRODUCTION

Depression can have a severe impact on the quality of

life of individuals and it is one of the main causes of

disability in the world (WHO, 2017). This mental dis-

order is closely related to a variety of somatic diseases

and cases of self-harm behavior. Not surprisingly, re-

searchers in the ﬁeld of psychology are making great

efforts to study the phenomenon of depression.

Society in its current state is strongly merged with

social networks. The popularity of social networks

made it possible to study human behavior, personal

traits, and mood by mining and analyzing the data

provided by users. At the same time, given the

fact that depression can affect human behavior, re-

searchers came up with the idea of monitoring depres-

sion and other mental disorders by using social media

data (De Choudhury et al., 2013a). Analysis of social

media data provides an opportunity to privately de-

tect the symptoms of depression before they progress

into more advanced stages of depression. This would

allow suggesting measures for the prevention of de-

pression and treatment during the early stages.

Our work is based on the general proﬁle data, text

messages, and images provided by users from the

Russian-speaking social media network VKontakte

https://orcid.org/0000-0001-8834-9319

https://orcid.org/0000-0003-4490-2017

https://orcid.org/0000-0003-0705-5832

(VK). We collected screening results of the Beck De-

pression Inventory questionnaire to perform a binary

classiﬁcation task: predicting whether a user was de-

pressed or not. We already evaluated a similar prob-

lem in our previous work (Stankevich et al., 2019),

where we analyzed the text from user posts on VK.

It was found, that it is not possible to apply the pro-

posed methods for all users since some of them do not

have text data in their proﬁles. The same situation is

observed with image data. In contrast to our previ-

ous work, we deﬁne the depression prediction task as

a global task, which means that we include users in

our dataset regardless of data types available in their

proﬁles. Overall, we processed over 60,000 photos,

95,000 posts and 9,000 groups of 619 users from VK.

To address this task we formed several feature

sets that were based on different data types from user

proﬁles: tf-idf, psycholinguistic markers, objects and

color properties of posted images, general proﬁle in-

formation, and user subscriptions. To deal with miss-

ing data we evaluated several approaches to form fea-

ture vectors and used a stacking classiﬁer approach on

the data, which yielded the best results on this task.

2 RELATED WORK

There are growing amounts of studies related to the

topic of predicting mental health by processing data

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

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

DOI: 10.5220/0010986100003122

In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2022), pages 753-760

ISBN: 978-989-758-549-4; ISSN: 2184-4313

753

Figure 1: Beck Depression Inventory screening results. Control group: score610; Depression group: score>30.

from social networks, and the majority of them are

focused on analyzing text data (Wongkoblap et al.,

2017). This is not surprising since the main content

of social media usually consists of various user text

messages. A good example of text-based analysis is

the Clef/eRisk series, that provided a shared task and

dataset of Reddit users’ messages for researchers to

address the task of early risk prediction of depression

(Losada et al., 2017; Losada et al., 2018). From this

point of view, this problem is closely associated with

the problem of predicting mental health from text in

general, not only from social media. For example,

an interesting task of predicting current and future

psychological health from childhood essays was pro-

vided by the Clpsych 2018 shared task (Lynn et al.,

2018). We should also note the Linguistic Inquiry and

Word Count tool (LIWC) (Tausczik and Pennebaker,

2010) - the feature extraction tool, which was success-

fully utilized in many psychological studies, as well

as in studies related to our topic (Wang et al., 2017;

Schwartz et al., 2016; De Choudhury et al., 2013b).

The image data posted by social media users can

be analyzed by searching for cues of mental disor-

ders. The dataset of 43950 photos from 166 Instagram

users was utilized to address the depression prediction

task (Reece and Danforth, 2017). Authors of the work

retrieved color properties, ﬁlter usage, frequency of

human faces, and some activity features from Insta-

gram accounts to perform binary classiﬁcation. In an-

other study, image properties such as color theme, sat-

uration, brightness, color temperature, and color clar-

ity were analyzed to detect psychological stress (Lin

et al., 2014a).

In addition to text and image data, it is also possi-

ble to retrieve valuable features from general informa-

tion about users’ proﬁles, activity, and interactions be-

tween users and communities in social media. For ex-

ample, posting time, the number of followers/follows,

the number of Twitter reply posts, retweets and links

were applied as features for the depression prediction

model in (De Choudhury et al., 2013a). There are

several works that studied the graph structures of so-

cial media interactions to ﬁnd signs of mental disor-

ders (Wang et al., 2017; Bollen et al., 2011). Another

study evaluated the possibility of using the digital me-

dia content of Facebook users (Kosinski et al., 2013)

to predict personality traits and socio-demographic at-

tributes.

Researchers provide a methodological framework,

as an alternative to similar works(Shen et al., 2017;

Ghosh and Anwar, 2021; Chiu et al., 2021), which

solves the problem of predicting mental health and,

in particular, depression, by analyzing records on so-

cial networks. However, the majority of the studies

evaluate this task by utilizing text, images, and social

media activity data separately, and there is a lack of

works that predict depression by processing all types

of social media data simultaneously.

3 DATASET

To form the dataset, we implemented the Beck De-

pression Inventory questionnaire (Beck et al., 1996)

ICPRAM 2022 - 11th International Conference on Pattern Recognition Applications and Methods

754

Table 1: Available Data in Control and Depression Groups.

Data General Text Images Text +

Type Proﬁle Images

Depression 259 148 (57.1%) 195 (75.2%) 110 (42.4%)

Control 360 239 (66.3%) 238 (66.1%) 156 (43.3%)

Sum 619 387 (62.5%) 433 (69.9%) 266 (42.9%)

Figure 2: Available data from 619 VK users.

and VK API authorization in our web-application that

was specially developed for our task. Using the inter-

nal VK advertisement system, we asked volunteers to

participate in our research by providing access to their

VK data and completing the Beck questionnaire un-

der privacy concerns. There are 1330 VK users who

provided Beck screenings and social media record-

ings. The Beck Depression Inventory is a well-known

and well-validated 21-item questionnaire designed to

measure the severity and presence of depression on

a 0 to 63 score scale. The distribution of depression

screening scores is presented in Fig. 1.

The questionnaire scores were used to deﬁne

3 groups of users: control group (score610),

middle group(116score629), and depression group

(score>30). These cut-off values closely correspond

to the values presented in (Beck et al., 1988). Similar

to our previous work (Stankevich et al., 2019), we re-

moved users with middle scores to perform a binary

classiﬁcation on data. After removing the middle we

ended up with a dataset of 619 users. We outline 3

data types that are possible to retrieve from VK:

• General proﬁle data.

• Text data;

• Image data.

All of the 619 users have general proﬁle data, but

not every user has images or enough text volume in

his/her proﬁle. It seems that some users only use VK

as messenger app. The analysis of these proﬁles re-

vealed that the main activity in their proﬁles are re-

posting (analog of retweets from Twitter). The di-

agram in Fig. 2 provides some insight into how the

data partition looks like.

Overall, the dataset includes the data from 387

users who have a sufﬁcient volume of authored text,

433 users who have more than 10 images, and 266

users who had enough text and image data. The statis-

tics about available data between control and depres-

sion groups are demonstrated in Table 1.

Data Availability. Unfortunately, there is no com-

plete way to anonymize data that we collected, be-

cause that part of VK proﬁles is freely available on the

Internet. That means that it is not possible to provide

depression screening results at the same time with raw

text and image data without possibly revealing the

identity of participants, which is contrary to our pri-

vacy concerns agreement with volunteers. However,

we can consider sharing already processed data for re-

search purposes under the data agreement form, if we

will become sure that there is no way to reveal users’

identities using this data. We also provide some code

sources for VK data loader

and evaluation pipeline

4 FEATURE SETS

4.1 General Proﬁle Data

We outlined two types of features that we were able to

retrieve from general proﬁle data. First of all, we re-

trieved features available on the main page of the pro-

ﬁle. There are some quantitative features: the number

of friends, followers, messages, afﬁliations, photos,

audio- and video- content, likes on user posts, repost

ratio, etc.; categorical features from VK predeﬁned

list of information about a person: attitude to alco-

hol and smoking, the main goal in life, marital sta-

tus, etc.; binary features that indicate the availability

of the following information in proﬁles: religion, fa-

vorite books, ﬁlms, quotes, games, relationship status,

TV, and online shows, etc. We named these features

the activity set (Activity). In general, people in the

https://github.com/naignatiev/VKParser7

https://github.com/naignatiev/psy vk

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

755

Figure 3: Examples of feature distributions in control and depression groups: a) number of friends, b) number of words, and

c) sentiment score; µ - mean.

depression group have fewer friends (see a) in Fig-

ure 3), which is also noted in other studies (Stanke-

vich et al., 2019).

We also considered information about users’ sub-

scriptions as general proﬁle data and formed a sub-

scriptions feature set (Subs.). We retrieved the list of

groups to which every user subscribed to, and gath-

ered information about these groups. Groups with

less than four subscriptions from users in our dataset

were removed from our feature set. We processed

around 9500 subscription items and identiﬁed 28 fea-

tures. The 33- and 66-percentile cut-off values of

the total number of group subscribers were used to

determine 3 group types: large, medium, and small.

The most common topics of groups were used to de-

termine 21 types: humor, creativity, cooking, educa-

tion, media, city community, show, literature, soci-

ety, science, design, unspeciﬁed type of community,

culture, cinema, style, photography, tourism, music,

artist, animals, and personal care. Groups age re-

strictions were used to describe another 4 types: 0+,

16+, 18+ and not speciﬁed. For each user, we pro-

cessed their subscription list and counted how many

of their groups fall into these 28 categories. The re-

sulting values were normalized by the total number of

user subscriptions.

4.2 Text Data

Our text data contains 95255 user messages, where

for each user we assembled messages with an overall

volume of up to 60000 characters from posts that are

most close to the Beck Depression Inventory screen-

ing date. Large messages with lengths exceeding

5000 characters were removed from observation since

manual analysis revealed that these texts are not au-

thored by users themselves in most cases. Since the

procedure for text selection for all users was the same,

it can be judged that the users in the depression group

generally provide less textual volume on social media

(see b) in Figure 3). The ﬁrst text data based fea-

ture set is psychological markers (PM), which con-

tains a large number of features that have been iden-

tiﬁed based on the lexical, morphological, syntactic,

semantic, and sentiment characteristics of the text

(Smirnov et al., 2021). The open access version

of markers retrieving tool with limited functionality

available at github

. The sentiment score of each user

was calculated with a help of the Linis-Crowd senti-

ment dictionary (Koltsova et al., 2016) by summariz-

ing sentiment scores of each word in user messages.

Distributions of sentiment scores (normalized by sen-

tence count) in the depression and control groups pre-

sented at c) in Figure 3. Features from the PM set are

similar to the PM set from our previous work (Stanke-

vich et al., 2019), but we also extended it with dictio-

nary based features, which were previously separated

from the PM set, and semantic features (Shelmanov

and Smirnov, 2014). As a second text-based feature

set, we computed tf-idf values over the unigram rep-

resentation of user messages (TF-IDF).

4.3 Image Data

We utilized Faster R-CNN (Ren et al., 2016) pre-

trained on the COCO (Lin et al., 2014b) dataset to

detect 80 types of objects on images from users’ pro-

ﬁles. The three image sources that were observed

were: avatar (proﬁle pictures), images from mes-

sages, and custom user albums. Overall, we processed

61794 user-related images. For each image source,

we calculated the frequency of occurrence of each ob-

ject. The frequency value of each object occurrence

was normalized by the total image count on the re-

spective proﬁle (Objects). In addition, we retrieved

color features (Colors) from users’ images by com-

puting the average values and standard deviation of

each component of the following color spaces: RGB,

HSV, XYZ, LAB.

https://github.com/tchewik/titanis-open/

ICPRAM 2022 - 11th International Conference on Pattern Recognition Applications and Methods

756

Table 2: Best cross-validation binary F1-scores for depression class.

Classiﬁer XGB LGBM CAT RF NB SVM KNN LR MLP R

Activity .62

∗

.55 .62

∗

.59 .63

∗

.59 .54 .59 .59

Subs. .61

∗

.58 .61

∗

.56 .58 .62

∗

.59 .57 .56

PM .56 .50 .57 .55 .61

∗

.55 .59

∗

.63

∗

.50

TF-IDF .57 .53 .57 .54 .68

∗

.61

∗

.59

∗

.50 .54

Objects .54 .50 .55 .53 .59

∗

.59

∗

.58

∗

.49 .50 .40

Colors .51 .44 .56 .50 .59

∗

.59

∗

.59

∗

.56 .45

1-stack .66 .65 .66 .65 .65 .64 .59 .63 .64

3-stack .70 .67 .71 .67 .67 .69 .69 .68 .69

All .56 .51 .58 .48 .60 .61 .45 .57 .54

∗

predictions of these classiﬁers were used to train 3-stack model

Table 3: Classiﬁcation report on the test data.

Feature set Model

Depression class Control class

F1-macro

Recall Precision F1-score Recall Precision F1-score

Activity NB .68 .57 .62 .64 .74 .68 .65

Subs. SVM .70 .59 .63 .65 .75 .70 .67

PM LR .67 .55 .60 .60 .72 .65 .63

TF-IDF NB .68 .52 .59 .55 .71 .62 .60

Objects SVM .67 .53 .59 .57 .70 .63 .61

Colors SVM .62 .56 .59 .65 .70 .67 .63

1-stack CAT .57 .57 .57 .69 .69 .69 .63

3-stack CAT .65 .63 .64 .73 .74 .74 .69

All SVM .56 .56 .56 .69 .69 .69 .62

5 METHOD AND EVALUATION

SETUP

Social media contains information about users of dif-

ferent kinds. In our particular case of predicting de-

pression from social media, we analyzed VK data and

outlined three types of information about users that

we can assemble: general proﬁle data, text data, and

image data. Recent studies demonstrate that it is pos-

sible to process all of these data sources to retrieve

features, which can distinguish depressed and non-

depressed users. The problem is that it is not possible

to apply prediction models that are trained on text-

based features for users that are missing text messages

in their proﬁles. The same is true for image-based

prediction models. Taking into account that datasets

for this type of research are usually relatively small,

It is also hard to strictly compare performance yielded

by these models since they are trained on completely

different samples. If we observe the situation where

we need to run a real world application that predicts

the depression status of social media users, it would

not be that easy to determine which model we should

use. To address this problem we consider the given

task as a global task, without separation on the text-

based and image-based datasets, and generalize our

approach to identifying depressed users. To evaluate

the performance of our models we split our data on

train and test samples (468 for the train part and 151

for the test part). The diversity of available data was

taken into account to make balanced splits.

We build a pre-processing pipeline with the fol-

lowing steps: missing values replacement, feature se-

lection, data transformation, and dimensional reduc-

tion. Along with parameters of classiﬁers, the settings

of each of the pre-processing stages were considered

as a hyperparameter and were optimized using the

Tree-Structured Parzen Estimator approach (Bergstra

et al., 2011) with the help of hyperopt (Bergstra et al.,

2013) on 3 times 5-fold cross-validation. It was found

empirically, that three repetitions are enough to not

overﬁt hyperparameters to the training set. At the

missing values replacement step, missing data was re-

placed by a median, average value, or zeros. The k-

Best Nearest Neighbor algorithm with varied k-values

was used for feature selection. At the stage of trans-

formation, the data were scaled to a normal, standard

or Gaussian-like form. As part of pre-processing,

PCA with linear, poly or rbf cores was used to lower

the number of dimensions. For the tf-idf sparse di-

mension, we also used truncated SVD for reduction.

It is important to note, that each of the pre-processing

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

757

steps could be skipped.

We evaluated three gradient boosting algorithms:

XGBoost (XGB) (Chen and Guestrin, 2016), Light-

GBM (LGBM) (Ke et al., 2017), CatBoost (CAT)

(Prokhorenkova et al., 2017) and 6 classic machine

learning algorithms: support vector machine (SVM),

random forest (RF), gaussian naive bayesian classi-

ﬁer (NB), k-nearest neighbors (KNN), multilayer per-

ceptron (MLP), and logistic regression (LR). We also

included a random based model for comparison (R).

To address the proposed idea of treating data from

social media users in an equal way regardless of its di-

versity we utilized the stacking classiﬁers approach.

More speciﬁcally, models predictions with the best

train scores from each feature set evaluations were

used to train the new classiﬁcation models. We per-

formed this approach with 1 (1-stack: 6 features) and

3 (3-stack: 18 features) best scores from each fea-

ture set. As another evaluation setup, we combined

all of the 6 feature sets into one feature space (All)

and applied chained equations (Azur et al., 2011) and

k-nearest neighbors (Troyanskaya et al., 2001) ap-

proaches for missing values imputations (which were

also designed as a hyperparameter for optimization).

6 RESULTS

The results of the best cross-validation performances

are presented in Table 2. All values in Table 2 rep-

resent binary F1-scores for the depression class. The

best result for each feature set is highlighted in bold.

The results calculated on the test data are presented in

Table 3.

Comparing the initial 6 feature sets by F1-score

for the depression class, the best scores were achieved

by models that were trained on general proﬁle data:

.64 with activity and .65 with subscriptions. This

result is not surprising since these feature sets were

based on information about users that was available

for all 619 users. The test scores with text-based fea-

ture sets are .60 and .59 with the PM and tf-idf feature

sets retrospectively. With image-based features test

scores were both around .59. We understand that we

are not able to adequately compare performance con-

ducted on the text and image-based sets since classi-

ﬁcation performance in both situations were distorted

by samples with values inserted during the missing

values replacement step. However, this step was in-

cluded to perform a classiﬁcation stacking approach

on the data. Staking predictions with 1-stack has not

demonstrated any good results on the test data. But

with the 3-stack approach over the CatBoost classi-

ﬁer, we were able to improve results to .64 on the test

Table 4: Top 5 CatBoost feature importance.

Base Level Model Feature Importance

CAT: activity 56.6960

KNN: tf-idf 26.4112

XGB: subscriptions 14.1834

SVM: subscriptions 1.4027

LR: PM 1.1157

SVF: tf-idf 0.1907

samples, which is the best result in our experiment. In

addition, the 3-stack model demonstrates the best F1-

scores for the control class (.74) and F1-macro (.69).

We retrieved feature importance values of the 3-

stack model and demonstrated them in Table 4. It

is noteworthy that the predictions obtained from the

image data based models were almost completely ig-

nored in this case. We assume that correct predictions

with objects and color features are almost completely

coincided with predictions given by classiﬁers trained

on text and general proﬁle data features.

Generally speaking, the obtained prediction accu-

racy is comparable in quality to other studies (Skaik

and Inkpen, 2020). However, it should be noted that

a direct comparison with other works is impossible,

as all depression detection studies were carried out on

other social networks, with a different class balance

and using different metrics.

7 CONCLUSION

The work describes the depression detection task that

was performed on the basis of VK social media data.

We formed a dataset that consists of text, image and

proﬁle data from personal pages of 619 VK users.

The results of the Beck Depression Inventory screen-

ings were used to split our users into depression and

control groups in order to try to classify them us-

ing machine learning methods. User social media

data was processed to retrieve activity, subscriptions,

psycholinguistic markers, tf-idf, image objects, and

image color properties feature sets. The experimen-

tal evaluation was designed around using all avail-

able data from users’ proﬁles and creating a predic-

tion pipeline that can process data samples regardless

of the availability of text or image data in the user

proﬁle. We applied the stacking classiﬁers technique

by combining predictions from the best models that

were trained on the different feature sets and used

them as features for a meta-classiﬁer. This method

allows reaching the best performance in our exper-

iments with around 64% of binary F1-score for de-

pression class and 69% F1-macro.

ICPRAM 2022 - 11th International Conference on Pattern Recognition Applications and Methods

758

ACKNOWLEDGEMENTS

This paper has been supported by the RUDN Univer-

sity Strategic Academic Leadership Program. The re-

search was carried out using the infrastructure of the

shared research facilities ‘High Performance Comput-

ing and Big Data’ of FRC CSC RAS (CKP ‘Informat-

ics’).

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