Deep Learning CNN-LSTM Approach for Identifying Twitter Users

Suffering from Paranoid Personality Disorder

Mourad Ellouze, Seifeddine Mechti and Lamia Hadrich Belguith

ANLP Group MIRACL Laboratory, FSEGS, University of Sfax-Tunisia, Tunisia

Keywords:

Paranoid Personality Disorder Detection, Deep Learning Architecture, Symptoms and Disease Detection,

Text Mining, Twitter.

Abstract:

In this paper, we propose an approach based on artiﬁcial intelligence (AI) and text mining techniques for

measuring the degrees of appearance of symptoms related to paranoid disease in Twitter users. This operation

will then help in the detection of people suffering from paranoid personality disorder in a manner that provides

justiﬁable and explainable results by answering the question: What factors lead us to believe that this person

suffers from paranoid personality disorder? These challenges were achieved using a deep neural approach,

including: (i) CNN layers for features extraction step from the textual part, (ii) BiLSTM layer to classify

the intensity of symptoms by preserving long-term dependencies, (iii) an SVM classiﬁer to detect users with

paranoid personality disorder based on the degree of symptoms obtained from the previous layer. According to

this approach, we get an F-measure rate equivalent to 71% for the average measurement of the degree of each

symptom and 65% for detecting paranoid people. The results achieved motivate and encourage researchers to

improve them in view of the relevance and importance of this research area.

1 INTRODUCTION

Paranoid personality disorder (PPD) is a psychologi-

cal disease marked by widespread and persistent in-

terpersonal distrust, in which others’ acts are mis-

understood as spiteful and malicious (Bernstein and

Useda, 2007). These side effects might cause in-

appropriate and unwanted behaviors (such as reck-

lessness, social isolation, insecurity and moodiness),

putting the patient in a situation of conﬂict with soci-

ety. The worst thing is that many complications are

associated with the therapy of this disease, as psy-

chiatric ailments are diagnosed differently than other

diseases. This distinction is due to the fact that symp-

toms are not tangible as the difﬁculty of breathing and

the feeling of oppression for people having Coron-

avirus.

All these effects have contributed to the appear-

ance of several dangerous consequences existing fre-

quently in our era such as suicide, terrorism, etc.

Despite the danger of these diseases, we notice that

the number of people having psychological problems

is increasing, especially in less-developed countries

olves et al., 2006) since there is negligence about

different problems such as economic, social, etc. In

this context, the World Health Organization (WHO)

has declared that one for every four adults in the world

suffers from mental problems and in half of the coun-

tries of the world, there is one psychiatrist per 100,000

people. Furthermore, 40 percent of countries have

fewer than one hospital bed for mental diseases per

10,000 people (Organization, 2001). As a result, new

approaches based on artiﬁcial intelligence (AI) have

been increasingly used in recent years to automate

the work of identifying people with psychiatric issues

from raw data.

In this context, (Baumgartl et al., 2020) worked

on Electroencephalographic data, while other works

based their processing on speech data (Wang et al.,

2021). Despite their relevance, these works are lim-

ited since to ensure the proper functioning of their

systems it is necessary to have sophisticated equip-

ment (MRI, sensors, etc.) which makes the task of

the detection extremely challenging.

In this era, social media represents one of the most

conducive environments that allows their users to in-

teract and express themselves freely about everything

that happens in the world. In recent years, a signiﬁ-

cant number of researchers have based their works on

data collected from social media. In fact the progress

and impressive development of computer technolo-

gies and tools has made the processing of the huge

612

Ellouze, M., Mechti, S. and Hadrich Belguith, L.

Deep Learning CNN-LSTM Approach for Identifying Twitter Users Suffering from Paranoid Personality Disorder.

DOI: 10.5220/0011322300003266

In Proceedings of the 17th International Conference on Software Technologies (ICSOFT 2022), pages 612-621

ISBN: 978-989-758-588-3; ISSN: 2184-2833

amount of this data more attainable.

For that, our challenge in this work is to detect

people having a personality disorder by analyzing

their textual production on social media. To achieve

our goal, we divided this paper into two objectives:

1. Measure the appearance degree of each symp-

tom of paranoid disease for each user proﬁle from

their textual data. That will ensure the reliability of

our ﬁnal results by providing an explanation and jus-

tiﬁcation for our decision.

2. Detect people having paranoid disease by tak-

ing advantage of the result of (1).

This work offers to Twitter the possibility to di-

agnose the state of their users in order to ensure their

well-being by detecting hidden information. In matter

of fact, users may not be aware of their mental state.

In addition, this work can allow for Twitter the possi-

bility of tracking the progress states of their users in

real time (for example Twitter can compare the degree

of symptoms related to a speciﬁc person in different

periods of time).

Starting with a state of the art in which we present

some studies done in this ﬁeld and their limitations.

Then, we detail our methods with the different tools

used. Next, we discuss the results achieved. Finally,

we conclude our work with a conclusion and some

perspectives.

2 RELATED WORKS

There are many obstacles related to the treatment of

data obtained from social media, since there are cer-

tain criteria that may intervene and inﬂuence user-

generated data. These criteria can include the age of

the person, country, level of education, etc. In ad-

dition, many users did not consider social media as

a formal framework for that, several users used in

their writing style irony, sarcasm, etc., which can dis-

rupt the treatment afterward. Moreover, we note a

violation of the language rules (punctuation, capital

letters, using terms that do not belong in a particu-

lar language’s lexicon, composing a sentence in more

than one language, etc.). However, many researchers

opt for text extracted from social networks for their

research works. In this context, several researchers

have used the data of social networks to detect violent

and extremist people (Rekik et al., 2019; Ahmad and

Siddique, 2017). Despite the difﬁculty found in the

processing of data extracted from social networks, the

objective of these studies remains achievable because

there is no concealed information, therefore we can

detect the distinct classes using a lexical technique

based on keyword searches. On the other hand, de-

tecting hidden information such as age, personality

traits and psychological problems is different.

For that in this case, we have to process a huge

volume of varied data. In this context, (Varshney

et al., 2017; Pramodh and Vijayalata, 2016; An et al.,

2018) worked on data obtained from several sources

and having different types to ensure the variation in

the data. Other researchers worked on the diversity of

the characteristics selected (Bleidorn and Hopwood,

2019; Gonz

alez-Gallardo et al., 2015; Celli and Lepri,

2018), for example some of them combined linguis-

tic criteria as morphological analysis, etc., meta-data

of Twitter such as number of friends and different in-

formation related to the tweet like number of words

or number of hashtag. Generally, the result of hid-

den information detection system contains an impor-

tant degree of uncertainty. For this reason, there

are a lot of researchers who used the statistical ap-

proach (Pramodh and Vijayalata, 2016; Ellouze et al.,

2020) instead of classical machine learning technique

(Stankevich et al., 2019; Mbarek et al., 2019) and

deep learning technique (Wang et al., 2019b; An et al.,

2018; Wang et al., 2019a) in order to guarantee the

notion of fuzzy logic. Among the drawbacks of dif-

ferent machine learning and statistical techniques is

that their results are not explanatory, for this reason

we found a lot of works that use the rule-based tech-

nique (Umar and Qamar, 2019; Muhammad et al.,

2019). Although the results of these rules are explana-

tory since they are based on cause and effect links, the

construction of these rules is very time consuming.

Other researchers focused on extracting use-

ful knowledge for doctors by detecting linguistic

speciﬁcities from the textual production of peo-

ple having psychological problems (Hall and Caton,

2017),(Schwartz et al., 2013). Among the results

found it by (Schwartz et al., 2013): (i) the extro-

vert people used more terms related to the lexicon of

friends and family. Besides, they used terms show-

ing positive feelings. Thus, (Baik et al., 2016) pro-

posed an approach to extract the relevant writing style

of each personality trait by associating for each trait

some categories of the most used subjects. This work

help authors to conclude that extrovert people are very

interesting in sports, shopping, hotels. Whereas intro-

verted people are more interesting in gaming.

After the analysis of the different mentioned pa-

pers, we note that most of authors have focused on the

detection of the consequences of psychological dis-

eases such as violence, terrorism or suicide (Rekik

et al., 2019; Ahmad and Siddique, 2017; Mbarek

et al., 2019), and only a few researchers who worked

on detecting personality disorders types (Haz et al.,

2022; Ellouze et al., 2021b; Ellouze et al., 2021a). In

Deep Learning CNN-LSTM Approach for Identifying Twitter Users Suffering from Paranoid Personality Disorder

613

fact, we did not ﬁnd any paper which treats paranoid

disease on social networks. Moreover, we note an ex-

cessive use of the English language, despite the ex-

istence of other languages with the same importance.

Besides, there are some problems related to the use of

the lexical approach (Salem et al., 2019). Generally,

this technique is based on the research of keywords

from the corpus (lexicons related to each class), so

among problems related to this technique is the difﬁ-

culty of ﬁnding a training corpus which includes all

lexicons related to a speciﬁc class. We note also that

results obtained (Stankevich et al., 2019; An et al.,

2018; Lin et al., 2017) are very abstracts and difﬁcult

to interpret besides they need more explanation.

3 PROPOSED APPROACH

In this study, we propose an approach illustrated in

ﬁgure 1 that allows Twitter to analyse in real time

the textual production of their users in order to apply

the process of diagnosis committed by the psychiatrist

(listen to the patient, identify the different symptoms,

detect the disease).

This was done using a novel deep learning model

containing a set of convolution layers CNN for auto-

matic features extraction task, since we do not know

the criteria of distinction between classes. Next, BiL-

STM to make the classiﬁcation of the degree of each

symptom of paranoid disease from the textual part

since it highlights the long-distance dependencies of

the textual part. Finally, SVM in order to detect para-

noid disease based on the degree of each symptom

since SVM is among the most conﬁgurable learning

algorithms (see ﬁgure 2).

In addition, our approach treats other problems at

the same time such as: (1) corpus imbalanced by us-

ing synthetic data generation step, (2) the lexical ap-

proach, by the use of sentence embedding technique

in order to detect the meaning of the word in the sen-

tence.

3.1 Preprocessing

In this step, we focused on preparing our corpus by

deleting unnecessary elements that do not distinguish

between classes in order to avoid negatively affect-

ing the subsequent processing, especially that our

work is based on data extracted from Web 2.0 (Petz

et al., 2015). This task was done by following these

steps: For the ﬁrst time, we eliminated the various

stop words including articulatory words such as and,

also, therefore, etc. These words are used by any per-

son, so they do not help to make the distinction be-

tween the different classes. Next, we removed from

our corpus the different symbols used for expressing

money, time, number, etc. Then, we converted cap-

ital letters to lowercase letters and abbreviated terms

to their ordinary form to normalize our corpus using

the resource Google Graph Knowledge like AI to Ar-

tiﬁcial Intelligence. Finally, we converted the inﬂec-

tional forms of words to a common root to behave

similarly to words having the same common root as

transform, transformation, transforming, etc.. This

step was done using the library NLTK (Natural Lan-

guage Toolkit).

3.2 Features Generation

This step involves converting the textual data into

numerical vectors that can be handled by machine

learning algorithms. Based on our review of several

works, there are many ways to achieve this transfor-

mation such as Word Embedding (Bakarov, 2018).

However, the major problem of this technique is that

it does not preserve the meaning of the whole sen-

tence which makes it difﬁcult for the algorithm to

measure the intention and the nuance existing in the

text. For that, we choose to work with sentence em-

bedding techniques as Universal Sentence Encoder

(USE) (Cer et al., 2018), InferSent (Reimers and

Gurevych, 2019), Sentence Bert (Feng et al., 2020),

etc. After an empirical study, we choose to work

with the Sentence Bert technique since this model is

trained on a large amount of data also it has a speciﬁc

architecture that allows it to learn deep bi-directional

representations, it accepts a large number of parame-

ters, making it more adaptable (Eke et al., 2021). This

technique is based on the calculation of the similarity

between sentences by applying pooling layers in order

to keep only important descriptors. In addition, this

technique provides as a result a set of standardized

vectors while settling many recognized issues related

to the size of the data set and the assortment of vocab-

ularies in the corpus. In our work, we have only relied

on textual data and we do not use other types of infor-

mation such as the number of retweets per user, or the

number of retweets per tweet since we work on data

that is obtained in a streaming manner, therefore at

the beginning, the value of these attributes is zero. In

addition, our approach is based on a deep learning ap-

proach so its speciﬁc architecture offers assistance to

distinguish automatically the relevant attributes from

the raw data.

ICSOFT 2022 - 17th International Conference on Software Technologies

614

Figure 1: Proposed approach for symptoms and paranoid disease detection.

Figure 2: Proposed Deep CNN–BiLSTM for symptoms and paranoid disease detection.

3.3 Features Extraction

The architecture of the Convolutional Neural Net-

work differs from the classic architecture of the

MLP (Multi Layers Perceptron) model, this differ-

ence mainly revolves around the convolutional part.

The purpose of this section is to automatically ex-

tract the characteristics and reduce the gross size of

the entry form to highlight the relevant characteris-

tics. For this reason, we used in our work the CNN

architecture since it performed well on different tasks

of natural language processing for capturing the syn-

tactic and semantic aspects (Ombabi et al., 2020). The

execution of this task was done by ﬂowing the input

(tweets) through a succession of ﬁlters, the output of

these ﬁlters is called convolution maps. The result-

ing convolution maps are concatenated into a feature

vector called CNN code.

3.4 Synthetic Generation of Data

Observations

In this step, we aim to maximize the number of in-

stances, considering the difﬁculty of annotating the

data and the difﬁculty of getting balanced data. For

this purpose, there are numerous ways like Multi-

objective Genetic Sampling for Imbalanced Classi-

ﬁcation (E-MOSAIC) (Fernandes et al., 2019), Ex-

ploratory Data Analysis (EDA) for handling dupli-

cate records, Synthetic Minority Over-sampling Tech-

nique (SMOTE) (Chawla et al., 2002), etc. After

an empirical study, we choose to work with SMOTE

technique, since it has shown a great deal of success

in various applications and ﬁelds (Quan et al., 2021;

Ishaq et al., 2021) and our corpus is not linked to a

particular ﬁeld. This technique uses the nearest neigh-

bors algorithm to produce new and synthetic data.

Deep Learning CNN-LSTM Approach for Identifying Twitter Users Suffering from Paranoid Personality Disorder

615

3.5 Symptoms Classiﬁcation

LSTM is an extension of RNN architecture that was

created to address RNN’s problem of explosion and

vanishing gradient since we may ﬁnd in a situation of

lags of unknown duration between the different events

of a time series. Therefore, the LSTM network is

adapted to the classiﬁcation, processing and the re-

alization of predictions based on time series data. In

our work, we choose to use BiLSTM in order to keep

the dependency links between the lexicon. For that,

we concatenated the output of the convolutional layer

to two LSTM layers in order to measure the depen-

dence between terms. The last layer is composed of

6 neurons (from 0 to 5) which measures the degree

of each symptom of the disease such as exaggerated

mistrust, negative interpretation of the gestures of oth-

ers, incessant doubt, etc. The reason for considering

the challenge as a classiﬁcation rather than a regres-

sion is based on the low instances of certain degrees.

This classiﬁcation step has been repeated nine times

(the most appeared number of symptoms of paranoid

disease), given that in our case it is a multi-label is-

sue, a person may simultaneously have more than one

symptom.

3.6 Paranoid Classiﬁcation

The task of disease detection is very sensitive, this

sensitivity is due to the absence of speciﬁc rules that

allow taking decisions. For example, it is not neces-

sary to have all symptoms in a person to afﬁrm that

he has the disease. Moreover, it is difﬁcult to ﬁnd a

person with incessant doubt and self-estimation at the

same time. In addition, there are a huge number of

combinations of symptoms degrees. For that, in this

step, we took advantage of the degree of all symp-

toms detected in the previous step in order to build

a vector (represent the list of symptoms related to an

individual). Then, we passed this vector to the SVM

layer in order to make the detection of the disease. In

this part, the number of features is reduced for that

we limited ourselves by the classical classiﬁcation al-

gorithms such as SVM, Naive Bayes, decision tree,

etc. After an empirical study we choose to work with

SVM algorithm.

4 EXPERIMENTS

This section presents the different details about our

dataset, LSTM settings for ”negative interpretation of

others’ actions” classiﬁcation and an extract of our

results. This work has been implemented using the

python programming language which integrates the

Tensorﬂow framework.

4.1 Corpus

We applied our approach to data composed of a set of

tweets that included a vocabulary linked to the dis-

ease’s negative effects ”personality disorders” such

as ”I congratulate myself”, ”I am wary”, ”I am in

the confusion of”, etc.. This data was obtained us-

ing Apache Spark Streaming tool for tweets in French

language from 01-03-2020 to 30-05-2020.

Two psychiatrists were requested to doubly anno-

tate this corpus based on their knowledge and expe-

riences. The annotation process began with an em-

pirical study of a 10% part of the corpus in order to

better grasp the nuances related to the language of

social networks and to develop a manual of annota-

tion. After that, each annotator separately annotated

the 90% of the corpus. The annotations of both types

of classiﬁcation are done independently, which means

for each user proﬁle (20 tweets) each annotator gives:

(i) the degree of each symptom (a number between

0 and 5 where 0 indicates the absence of the symp-

tom and 5 indicates the high degree of the presence of

a symptom), (ii) their decision about the state of the

person ”paranoid person” or ”normal person”. We

consider a person with a paranoid personality disor-

der if in their last 20 tweets there is a redundancy of

linguistic indicators that show the symptoms of this

disease such as the semantic information indicating

terrible disturbance and fear as for example the fol-

lowing expressions ”my hair is standing on the end”,

”I can hardly breathe”, ”my throat gets knotted”, etc.

We set a limit of 20 tweets per user because we aim to

develop an approach able of recognizing people with

PD by the fewest number of tweets possible in or-

der to ensure early prevention while guaranteeing the

credibility of the results.

The selection of paranoid symptoms is based on

the most well-known symptoms of paranoid disorder

that we have chosen to present at levels 1-5 to be pre-

cise. After the annotation phase committed by the 2

experts in order to annotate the degree of presence of

symptoms as well as the existence of the disease, we

proceed to calculate the rate of agreement between

these 2 experts by using Cohen’s Kappa measure. In

this context, we obtained a value of 0.9 for the detec-

tion of the disease and 0.73 for the detection of the

degree of symptoms. Conﬂicting cases are mainly re-

lated to the misinterpretation of cases (misinterpreta-

tion in measuring the degree of the intensity of symp-

toms as well as between missing information or dili-

gence). For that, we asked our experts to meet again

ICSOFT 2022 - 17th International Conference on Software Technologies

616

and choose between (agreement or removing) con-

ﬂicting cases. Tables 1 and 2 show in more detail the

distribution of tweets per class.

Note 1: In table 2 we present the number of para-

noid symptoms of each user presented in table 1,

which means one person that exists in table 1 can ap-

pear up to 9 times in table 2.

Note 2: It should be noted that in some cases we

encountered difﬁculties in the collection of data. For

example, for people who believe that they are always

right or that they are isolated, they do not need to talk

to others and try to persuade them. Particularly for the

ﬁrst case, which have a tendency to be self-centered.

While in the case of ”reading hidden meanings in the

innocent remarks” and ”recurrent suspicions”, any-

thing can be a trigger and an incentive for these peo-

ple to write and show what is not expressed (hidden

ideas).

Table 1: The distribution of instances for paranoid classiﬁ-

cation.

Paranoid YES NO

Number of in-

stances

280 users

(5600 tweets)

450 users

(9000 tweets)

Table 2: The distribution of instances for symptoms classi-

ﬁcation.

Symptoms Number of instances

aggressiveness 121 users (2420 tweets)

perceives attacks 163 users (3260 tweets)

recurrent suspicions 282 users (5640 tweets)

isolation 46 users (920 tweets)

believing they are always

right

76 users (1520 tweets)

read hidden meanings in the

innocent remarks

227 users (4540 tweets)

poor relationships with oth-

ers

273 users (5460 tweets)

doubt the commitment 193 users (3860 tweets)

unforgiving and hold

grudges

187 users (3740 tweets)

4.2 Results

For the various settings applied to each layer in our

model, we applied three convolution layers, we ac-

cord for each of them 320 feature maps and an activa-

tion function ”Relu”. Moreover, three pooling layers

with a pool size of (1,9). Then, we employed two

LSTM layers composed of 250 neurons for the ﬁrst

layer and 150 neurons for the second layer combined

with a hidden layer using ”softmax” as an activation

function and next with an output layer composed of

6 neurons (representing the degree of this symptom).

We repeated the execution of this task 9 times (num-

ber of symptoms) and in each case we predicted for

each symptom a value which represents the degree of

this symptom. The model of CNN input and output

with multiple parameters is presented in the table 3.

Table 3: Model parameter structure.

Layer type Output

shape

Param#

Input Layer (768,1)

conv1d (Conv1D) (768, 320) 3200

max pooling1d (233, 320) 0

dropout (Dropout) (233, 320) 0

conv1d 1 (Conv1D) (233, 320) 921920

max pooling1d 1 (85, 320) 0

dropout 1 (Dropout) (85, 320) 0

conv1d 2 (Conv1D) (85, 320) 921920

max pooling1d 2 (9, 320) 0

dropout 2 (Dropout) (9, 320) 0

time distributed (1, 8000) 0

lstm (LSTM) (250) 1211000

lstm 1 (LSTM) (100) 240600

classiﬁcation layer 6 906

Next, we passed the vector composed of the de-

gree of the nine symptoms obtained from the previous

step to a SVM layer in order to make the classiﬁcation

of paranoid disease. We used SVM layer with a linear

kernel and scale gamma since our instances are lin-

early separable. We employ the Python programming

language to handle these various layers with their set-

tings. The following table 4 shows an excerpt of our

results for paranoid’s symptoms degrees detection.

4.3 Evaluation

We used the classical metrics of recall, precision, and

F-measure to assess the performance of each type of

classiﬁcation (symptoms, disease). For the classiﬁca-

tion of symptoms degrees, we calculated the stated

mentioned criteria for the results of the ﬁrst out-

put layer of our model (degree of symptom i) which

means for each symptom of paranoid disease. For the

evaluation of the classiﬁcation of paranoid disease,

we applied the mentioned criteria to the results of the

last layer of our model.

Note 1: The evaluation of the classiﬁcation of

paranoid disease contains also the error rate ﬁgured

on the detection of symptoms degrees.

The two tables 5 and 6 display with more details

the evaluation of our approach. We compared the re-

sults of our work with a Baseline architecture com-

posed of CNN, BiLSTM, SVM which consists in pre-

dicting the disease directly without going through the

symptom prediction stage. This Baseline is inspired

Deep Learning CNN-LSTM Approach for Identifying Twitter Users Suffering from Paranoid Personality Disorder

617

Table 4: Extract of results (translate to English) of paranoid’s symptoms degrees detection.

user’s tweets doubt the

commitment

unforgiving and

hold grudges

isolation

1. Il y a des gens vous

etes vrmt pourri jusqu’

a l’

ame. (There are

people you are really rotten to the core.)

2. Imagine t’aimes pas provoquer ? quelle vie fade. (Imagine you

don’t like to provoke? what a bland life.)

3. Depuis que je suis ma seule priorit

e jsuis beaucoup plus heureuse,

c¸a paye pas de faire passer les autres avant soi. (Since I’m my only

priority I’m much happier, it doesn’t pay to put others before your-

self.)

4. Cette nuit j’aurais pr

e ne pas r

ever (Tonight I would have pre-

ferred not to dream.)

5. Y’a d comportement que jsupporte plus (There is a behavior that I

can’t stand anymore.)

6. C’est quoi cette nouvelle mode des meufs de se vanter de parler

1939101 mecs c’est pas une ﬁert

e et c¸a le sera jamais. (What is this

new trend for girls to brag about talking to 1939101 guys? It’s not a

pride and it will never be.)

7. Maintenant

etre ﬁd

ele c’est devenue une qualit

e alors que c¸a de-

vrait

etre normal. (Now being faithful has become a quality when it

should be normal.)

8. Y’en a vous

etes culott

e c’est incroyable. (There are some of you

who are cheeky, it’s incredible.)

9. T’as qu’a imaginer que c’

etait un r

eve et que tous ce qu’on a v

ecu

c’

etait pas vrai. (You just have to imagine that it was a dream and that

everything we lived was not true.)

10. L’ingratitude est la pire des choses sah tu donnes tout

a des gens

qui se foutent de ta gueule.(Ingratitude is the worst thing sah you give

everything to people who make fun of you.)

11. Rencontrer quelqu’un avec le m

eme

etat d’esprit que vous est

rare. (Meeting someone with the same mindset as you is rare.)

12. Le d

ego

ut sa aide beaucoup a oublier. (Disgust helps a lot to

forget.)

13. Arr

ete de croire que tout le monde te consid

ere comme tu les

consid

erer ! (Stop believing that everyone considers you the way you

consider them!)

14. C’

etait des grandes paroles en l’air. (It was all talk and no action.)

3 2 2

by (Ombabi et al., 2020) in which the authors ad-

dressed the issue of classifying textual data from so-

cial networks. The purpose of this comparison is to

demonstrate the impact of the layer allowing the de-

tection of symptoms before the disease on the results.

The results of this comparison are illustrated in ta-

ble 7.

Note 2: For the evaluation task of our approach,

we applied K-fold cross-validation technique. It

should be noted that each time we switch between the

training folds and the test fold. This is due that, we ap-

plied the SMOTE technique only to the training folds

in order to not inﬂuence the evaluation results of our

approach.

Note 3: We were tolerated in the evaluation of

symptoms degrees classiﬁcation results, this is at the

level of accepting the difference of +1 between the

real value and the predicted value (the opposite di-

rection is not accepted). However, this remains valid

except for cases where the value is between 1 and 5

which means our system has committed an error in

the choice of the degree and not in the existence of

the symptom.

5 DISCUSSION

This paper presented an intelligent approach based on

machine learning and text mining techniques. The ob-

jective of this approach is to measure the presence

degrees of symptoms in order to detect afterwards

paranoid disease among people using social networks.

This work meets the limitations presented in the re-

lated work section at the level that we respected the

logical passage to detect the disease (detect the symp-

toms then the disease) which makes our results pre-

cise, reliable and interpretable. In addition, we used

the full process of the deep learning approach (fea-

tures extraction and classiﬁcation techniques) since

we do not know precisely what are relevant features

ICSOFT 2022 - 17th International Conference on Software Technologies

618

Table 5: Variation of Recall, Precision and F-measure ac-

cording to the model CNN+BiLSTM for symptoms classi-

ﬁcation.

Symptoms Recall

(%)

Precision

(%)

F-

measure

(%)

aggressiveness 80 73 76

perceives attacks 68 68 68

recurrent suspicions 61 59 60

isolation 86 77 81

believing they are al-

ways right

85 78 81

read hidden meanings

in the innocent re-

marks

69 65 67

poor relationships

with others

58 56 57

doubt the commit-

ment

72 66 69

unforgiving and hold

grudges

80 75 77

Table 6: Variation of Recall, Precision and F-measure ac-

cording to the selected classiﬁer for paranoid disease detec-

tion based on symptoms classiﬁcation results.

Recall

(%)

Precision

(%)

F-

measure

(%)

Softmax 60 61 60

Gradient

Boosting

60 57 58

KNN 62 62 62

AdaBoosting 65 60 62

Random Forest 61 60 60

SVM 66 64 65

Table 7: Recall, Precision and F-measure comparison of our

results with baseline results for Paranoid classiﬁcation.

Recall

(%)

Precision

(%)

F-measure

(%)

Baseline

(Ombabi

et al., 2020)

59 53 56

Our architec-

ture

66 64 65

Improvement 7 11 9

offering assistance in distinguishing between differ-

ent classes. Moreover, we addressed issues associ-

ated with the size and the unbalanced corpus using the

technique of data generation. Thus, problems linked

to the lexical approach through the sentence embed-

ding technique ”BERT” that deals with the meaning

of words in the sentence. We got the most satisfac-

tory results (F-measure equal to 81%) for the follow-

ing symptoms classiﬁcation degrees: believing they

are always right and isolation. We obtained the poor-

est results (F-measure equal to 57%) for the classiﬁca-

tion of poor relationships with others symptom. This

difference is due to the language speciﬁcity linked to

each symptom class and the way of reacting of the al-

gorithm to each situation. In the same context, get the

best result for the classiﬁcation of symptom believing

they are always right despite the lack of data com-

pared to the symptom poor relationships with others

for which we have enough data. This is justiﬁed by

the fact that the second symptom is measured by four

degrees with a high error rate, whereas in the ﬁrst case

the symptom is measured using only two degrees.

Moreover, we can conclude that the task of data gen-

eration has helped us to overcome the problem of data

reduction. For the classiﬁcation of the disease, we ob-

tained the best results using SVM algorithm since the

instances of our corpus are linearly separable. Re-

garding the average results obtained for the detection

of some symptoms compared to the results of disease

detection, this is due to the: (i) linguistic phenomena

such as irony, negations, etc, (ii) high number degree

of symptoms (not like binary classiﬁcation of the dis-

ease), (iii) issues with using features in lexicon format

(general lexicon, an idea can be written in more than

one way, etc). Finally, this work gives Twitter the op-

portunity to track the state of its users (if there are new

symptoms that appear or disappear).

6 CONCLUSION

In this paper, we proposed a method to detect peo-

ple with paranoid personality disorder. This method

has an advantage compared to other works since it

provides explanatory results by detecting symptoms

of paranoid disease. Besides, it takes advantage of

a deep learning approach that combines at the same

time the extraction of features and the classiﬁcation

tasks. Furthermore, it addresses the problems of un-

balanced data and reduced size of the corpus through

the task of generating data. The proposed method was

implemented and evaluated and the evaluation results

obtained are encouraging, indeed, the F-measure is

equal to 79%. As perspectives, we envisage testing

our method on other types of personality disorders

with a particular application ﬁeld.

REFERENCES

Ahmad, N. and Siddique, J. (2017). Personality assess-

ment using twitter tweets. Procedia computer science,

112:1964–1973.

An, G., Levitan, S. I., Hirschberg, J., and Levitan, R.

Deep Learning CNN-LSTM Approach for Identifying Twitter Users Suffering from Paranoid Personality Disorder

619

(2018). Deep personality recognition for deception

detection. In INTERSPEECH, pages 421–425.

Baik, J., Lee, K., Lee, S., Kim, Y., and Choi, J. (2016). Pre-

dicting personality traits related to consumer behavior

using sns analysis. New Review of Hypermedia and

Multimedia, 22(3):189–206.

Bakarov, A. (2018). A survey of word embeddings evalua-

tion methods. arXiv preprint arXiv:1801.09536.

Baumgartl, H., Dikici, F., Sauter, D., and Buettner, R.

(2020). Detecting antisocial personality disorder us-

ing a novel machine learning algorithm based on elec-

troencephalographic data. In PACIS, page 48.

Bernstein, D. P. and Useda, J. D. (2007). Paranoid person-

ality disorder.

Bleidorn, W. and Hopwood, C. J. (2019). Using ma-

chine learning to advance personality assessment and

theory. Personality and Social Psychology Review,

23(2):190–203.

Celli, F. and Lepri, B. (2018). Is big ﬁve better than mbti?

a personality computing challenge using twitter data.

In CLiC-it.

Cer, D., Yang, Y., Kong, S.-y., Hua, N., Limtiaco, N., John,

R. S., Constant, N., Guajardo-Cespedes, M., Yuan,

S., Tar, C., et al. (2018). Universal sentence encoder.

arXiv preprint arXiv:1803.11175.

Chawla, N. V., Bowyer, K. W., Hall, L. O., and Kegelmeyer,

W. P. (2002). Smote: synthetic minority over-

sampling technique. Journal of artiﬁcial intelligence

research, 16:321–357.

Eke, C. I., Norman, A. A., and Shuib, L. (2021). Context-

based feature technique for sarcasm identiﬁcation

in benchmark datasets using deep learning and bert

model. IEEE Access, 9:48501–48518.

Ellouze, M., Mechti, S., and Belguith, L. H. (2020). Au-

tomatic proﬁle recognition of authors on social media

based on hybrid approach. Procedia Computer Sci-

ence, 176:1111–1120.

Ellouze, M., Mechti, S., and Belguith, L. H. (2021a).

Approach based on ontology and machine learning

for identifying causes affecting personality disorder

disease on twitter. In International Conference on

Knowledge Science, Engineering and Management,

pages 659–669. Springer.

Ellouze, M., Mechti, S., Krichen, M., Belguith, L. H., et al.

(2021b). A deep learning approach for detecting the

behavior of people having personality disorders to-

wards covid-19 from twitter.

Feng, F., Yang, Y., Cer, D., Arivazhagan, N., and Wang, W.

(2020). Language-agnostic bert sentence embedding.

arXiv preprint arXiv:2007.01852.

Fernandes, E. R., de Carvalho, A. C., and Yao, X.

(2019). Ensemble of classiﬁers based on multiob-

jective genetic sampling for imbalanced data. IEEE

Transactions on Knowledge and Data Engineering,

32(6):1104–1115.

Gonz

alez-Gallardo, C. E., Montes, A., Sierra, G., N

unez-

arez, J. A., Salinas-L

opez, A. J., and Ek, J. (2015).

Tweets classiﬁcation using corpus dependent tags,

character and pos n-grams. In CLEF (working notes).

Hall, M. and Caton, S. (2017). Am i who i say i am? unob-

trusive self-representation and personality recognition

on facebook. PloS one, 12(9):e0184417.

Haz, L., Rodr

ıguez-Garc

ıa, M.

A., and Fern

andez, A.

(2022). Detecting narcissist dark triad psychological

traits from twitter.

Ishaq, A., Sadiq, S., Umer, M., Ullah, S., Mirjalili, S., Ru-

papara, V., and Nappi, M. (2021). Improving the pre-

diction of heart failure patients’ survival using smote

and effective data mining techniques. IEEE access,

9:39707–39716.

olves, K., V

arnik, A., Schneider, B., Fritze, J., and Al-

lik, J. (2006). Recent life events and suicide: a case-

control study in tallinn and frankfurt. Social science

& medicine, 62(11):2887–2896.

Lin, H., Jia, J., Qiu, J., Zhang, Y., Shen, G., Xie, L., Tang,

J., Feng, L., and Chua, T.-S. (2017). Detecting stress

based on social interactions in social networks. IEEE

Transactions on Knowledge and Data Engineering,

29(9):1820–1833.

Mbarek, A., Jamoussi, S., Charﬁ, A., and Hamadou, A. B.

(2019). Suicidal proﬁles detection in twitter. In WE-

BIST, pages 289–296.

Muhammad, A., Hendrik, B., and Iswara, R. (2019). Ex-

pert system application for diagnosing of bipolar dis-

order with certainty factor method based on web and

android. In Journal of Physics: Conference Series,

volume 1339, page 012020. IOP Publishing.

Ombabi, A. H., Ouarda, W., and Alimi, A. M. (2020).

Deep learning cnn–lstm framework for arabic senti-

ment analysis using textual information shared in so-

cial networks. Social Network Analysis and Mining,

10(1):1–13.

Organization, W. H. (2001). The world health report 2001:

Mental health: new understanding, new hope.

Petz, G., Karpowicz, M., F

urschuß, H., Auinger, A.,

ıtesk

y, V., and Holzinger, A. (2015). Reprint of:

Computational approaches for mining user’s opinions

on the web 2.0. Information Processing & Manage-

ment, 51(4):510–519.

Pramodh, K. C. and Vijayalata, Y. (2016). Automatic per-

sonality recognition of authors using big ﬁve factor

model. In 2016 IEEE International Conference on

Advances in Computer Applications (ICACA), pages

32–37. IEEE.

Quan, Y., Zhong, X., Feng, W., Chan, J. C.-W., Li, Q., and

Xing, M. (2021). Smote-based weighted deep rotation

forest for the imbalanced hyperspectral data classiﬁca-

tion. Remote Sensing, 13(3):464.

Reimers, N. and Gurevych, I. (2019). Sentence-bert: Sen-

tence embeddings using siamese bert-networks. arXiv

preprint arXiv:1908.10084.

Rekik, A., Jamoussi, S., and Hamadou, A. B. (2019). Vio-

lent vocabulary extraction methodology: Application

to the radicalism detection on social media. In In-

ternational Conference on Computational Collective

Intelligence, pages 97–109. Springer.

Salem, M. S., Ismail, S. S., and Aref, M. (2019). Personality

traits for egyptian twitter users dataset. In Proceedings

ICSOFT 2022 - 17th International Conference on Software Technologies

620

of the 2019 8th International Conference on Software

and Information Engineering, pages 206–211.

Schwartz, H. A., Eichstaedt, J. C., Kern, M. L., Dziurzyn-

ski, L., Ramones, S. M., Agrawal, M., Shah, A.,

Kosinski, M., Stillwell, D., Seligman, M. E., et al.

(2013). Personality, gender, and age in the language

of social media: The open-vocabulary approach. PloS

one, 8(9):e73791.

Stankevich, M., Latyshev, A., Kuminskaya, E., Smirnov, I.,

and Grigoriev, O. (2019). Depression detection from

social media texts. In Elizarov, A., Novikov, B., Stup-

nikov., S (eds.) Data Analytics and Management in

Data Intensive Domains: XXI International Confer-

ence DAMDID/RCDL, page 352.

Umar, A. and Qamar, U. (2019). Detection and diagnosis of

psychological disorders through decision rule set for-

mation. In 2019 IEEE 17th International Conference

on Software Engineering Research, Management and

Applications (SERA), pages 33–37. IEEE.

Varshney, V., Varshney, A., Ahmad, T., and Khan, A. M.

(2017). Recognising personality traits using social

media. In 2017 IEEE International Conference on

Power, Control, Signals and Instrumentation Engi-

neering (ICPCSI), pages 2876–2881. IEEE.

Wang, B., Wu, Y., Vaci, N., Liakata, M., Lyons, T.,

and Saunders, K. E. (2021). Modelling paralin-

guistic properties in conversational speech to detect

bipolar disorder and borderline personality disorder.

In ICASSP 2021-2021 IEEE International Confer-

ence on Acoustics, Speech and Signal Processing

(ICASSP), pages 7243–7247. IEEE.

Wang, C., Wang, B., and Xu, M. (2019a). Tree-structured

neural networks with topic attention for social emo-

tion classiﬁcation. IEEE Access, 7:95505–95515.

Wang, L., You, Z.-H., Chen, X., Li, Y.-M., Dong, Y.-

N., Li, L.-P., and Zheng, K. (2019b). Lmtrda: Us-

ing logistic model tree to predict mirna-disease as-

sociations by fusing multi-source information of se-

quences and similarities. PLoS computational biol-

ogy, 15(3):e1006865.

Deep Learning CNN-LSTM Approach for Identifying Twitter Users Suffering from Paranoid Personality Disorder

621