SIAS: Suicidal Intentions Alerting System

Georgios Domalis

, Christos Makris

, Pantelis Vikatos

, Anastasios Papathanasiou

2,3

Efterpi Paraskevoulakou

and Manos Sfakianakis

2,3

Computer Engineering & Informatics Department, University of Patras, Patras, Greece

Department of Informatics, University of Piraeus, Piraeus, Greece

Cyber Crime Division, Hellenic Police, Athens, Greece

Keywords:

Alerting System, Classiﬁcation Model, Suicidal Intention.

Abstract:

In this paper, we present an alerting system based on an efﬁcient classiﬁcation model for detecting suicidal

people using natural language processing and data mining techniques. The model uses linguistic features

which are derived from an analysis of handwritten and electronic messages/notes. The model was trained and

validated with fully anonymised real data provided by the Cyber Crime Division of Greek Police as well as

available suicidal notes from social media. The alerting system is intended as a prevention, management tool

for automatic detection of suicidal intentions.

1 INTRODUCTION

Suicide is a serious problem of social and public

health perspective. According to recent World Health

Organization

more than 30,000 suicide deaths in the

United States and nearly 1 million suicide deaths

worldwide occur every year constituting suicide one

of the 20 leading causes of death. The Internet as well

as social media can play an important role in preven-

tion of suicide cases. In addition, current scientiﬁc

research (Burnap et al., 2015; Colombo et al., 2016;

Sueki, 2015) is focused on discovering patterns and

association between users’ behavior in social media

with suicidal intention. Regarding ofﬁcial statistics

from Greek Law Enforcement Agency

the rates of

interventions Cyber Crime Division, related to suici-

dal intentions have been extremely increased during

the current decade. The efﬁcient investigation and

management of cases that involve a report of suicide

on the internet requires collaboration between Law

Enforcement Agency (Cyber Crime Division) and re-

sponsible ISPs. Law Enforcement Agency encoun-

ters difﬁculties in the investigation of such cases and

cybercrimes due to the strict legislative framework.

First of all the cross-border nature and the involve-

ment of many jurisdictions impede the investigation

and detection of such cases. Therefore, it is neces-

htt p : //www.who.int/mental health/prevention/en/

htt p : //www.hellenicpolice.gr

sary the international police cooperation in immedi-

ate time given the serious and urgent nature of these

cases (online suicides). However, there are a lot of

constraints and problems encountered by law enforce-

ment authorities in conducting investigations when-

ever the digital data and suicidal posts are stored on

computing cloud environments. More speciﬁcally,

many challenges that exist in these environments have

to be handled involving multiple levels, such as recog-

nition, collection, preservation, examination, inter-

pretation and reporting of digital data and evidence.

A major problem arises related to the fact that, even

if such data are identiﬁed, the characteristics of the

cloud environment complicate the collection of ev-

idence through legal procedure, since it is possible

by involving many jurisdictions. Another problem

is the large amount of data and information stored

in cloud computing environments, which adds difﬁ-

culty to identify those data related to the speciﬁc re-

search carried out every time. The policy and guide-

lines for addressing these challenges is imperative to

effectively investigate announced suicide cases on the

Internet and law enforcement generally on the World

Wide Web.

The goal of this paper is the implementation of

an alerting system called SIAS (Suicidal Intentions

Alerting System) for predicting suicide intention us-

ing an efﬁcient classiﬁcation model. The classiﬁca-

tion model introduces a set of characteristics which

Domalis, G., Makris, C., Vikatos, P., Papathanasiou, A., Paraskevoulakou, E. and Sfakianakis, M.

SIAS: Suicidal Intentions Alerting System.

DOI: 10.5220/0006297402910297

In Proceedings of the 13th International Conference on Web Information Systems and Technologies (WEBIST 2017), pages 291-297

ISBN: 978-989-758-246-2

291

are derived from analysis of texts and are related to

cohesion, emotional instability, part of speech, cer-

tain emotional lexicons and personality traits. Natu-

ral language techniques have been used for the fea-

ture extraction directly from the textual sources. We

train and evaluate machine learning algorithms in or-

der to conclude with, one which performs efﬁciently

in terms of F-measure for our datasets. We use pre-

annotated texts divided into two categories. The ﬁrst

one constitute a set of citizens’ texts with the proven

suicidal intention and other with normal behavior pro-

vided by diverse sources.

The rest of the paper is structured as follows. Sec-

tion 2 overviews related work, we motivate our re-

search from current challenges regarding and related

studies. In Section 3, we provide an overview of the

system architecture that we propose with the detailed

description of each module. In Section 4 the classiﬁ-

cation is mentioned with the data preparation and the

training/evaluation of the model. Section 5 overviews

details of the implementation of the system for mod-

ules and sub-modules respectively and presents a ref-

erence to our experimental results. Finally, in Sec-

tion 6, we discuss the strengths and limitations of our

approach and we conclude with an outlook to future

work.

2 RELATED WORK

The concept of prediction of suicidal people through

electronic or hand written messages has gained the

interest of researchers in the ﬁeld of natural language

processing, sentiment analysis and machine learning

(Burnap et al., 2015; Thompson et al., 2014). In

Pestian et al. (Pestian et al., 2010) they introduce

methods that distinguish genuinely and elicited sui-

cide notes using a content analysis based on natural

language processing.

Also, several studies are focused on predicting mil-

itary and veteran suicide risk (Soltaninejad et al.,

2014; Thompson et al., 2014). (Thompson et al.,

2014) describes language models in order to detect

suicidal ideation from texts derived from social media

and speciﬁcally, Facebook posting. The results of this

study show that word pairs are more useful than single

words for model construction. Study (Burnap et al.,

2015) introduces an approach with text mining tech-

nique using classiﬁcation models in order to catego-

rize each text derived from Twitter to multiple classes,

related to suicidal ideation and topics such as report-

ing of a suicide, memorial, campaigning and support.

Another study (Sueki, 2015) discovers the association

between suicide-related Twitter posts with suicidal

behavior. Especially this research focuses on young

people using the Internet and social media to identify

and predict suicide intentions based on the texts and

behavior of Twitter users. The linking of personality

traits with the suicidal ideation constitutes the aim of

study (Soltaninejad et al., 2014). Regarding to this re-

search personality traits such as neuroticism, extraver-

sion and conscientiousness traits may predict suicidal

ideation on the other hand traits such as agreeable-

ness and openness weakly correlate with suicide. An

approach using a vocabulary of topics which suici-

dal persons are used to talking is presented in (Ab-

boute et al., 2014). Twitter messages are parsed and

checked using the topics by a process combining nat-

ural language processing and learning methods to in-

dicate suicidal risky behavior. In addition, in the study

(Lightman et al., 2007) well-known tools for linguis-

tic analysis such as Coh-Metrix (Graesser et al., 2004)

and LIWC (Pennebaker et al., 2001) have been used

to investigate the correlation of features with suici-

dal and non-suicidal songwriter’s lyrics. In (Mairesse

et al., 2007) the authors present ﬁrstly a detailed cor-

relation analysis between Big Five personality traits

and the features contained in software, e.g. LIWC;

then they described an automatic recognition of user

personality traits using regression and classiﬁcation

models. In another study (Stirman and Pennebaker,

2001) the word use of suicidal and non suicidal po-

ets are examined and correlations between the suici-

dal ideation and features from LIWC are extracted.

In this work, we deviate from the above approaches

due to the combination of linguistic features with per-

sonality traits which are strongly correlated with sui-

cidal intentions improving the performance of predic-

tion.

3 SYSTEM ARCHITECTURE

In this section, we introduce our model a detailed de-

scription of the alerting system architecture. The pro-

posed alerting system is focused to immediate identi-

ﬁcation of suicidal intentions.

The system is composed of the following modules:

• The targets identiﬁcation. The alerting system

starts by targeting of certain citizens (subjects)

which their psychological disorder could lead to

suicidal attempt. The targets are reported by cit-

izens, psychological clinics or Law Enforcement

Agency.

• The user proﬁle creation. This module deals with

the registration of targets’ personal info and the

creation of users’ proﬁle. Coordinating Opera-

tional Center of Cyber Crime Division ﬁnds or

WEBIST 2017 - 13th International Conference on Web Information Systems and Technologies

292

receives information about the suicidal intention

over the Internet or other communication tech-

nologies, following a thorough online police in-

vestigation, which includes correspondence with

concerned websites (Facebook, Twitter, Linkedin

etc.) and companies providing telecommunica-

tions and Internet Services (ISPs) in order to iden-

tify certain citizens.

• The feature extraction. The module of feature ex-

traction accepts raw texts for each individual tar-

get. In this step, feature vectors are produced by

analysis of text, creating characteristics related to

cohesion and emotional instability, linguistic and

personality traits. The feature vector is introduced

to the classiﬁcation model for suicidal intention

declaration.

• The classiﬁcation model. This module constitutes

the predictor of the suicidal intention. The clas-

siﬁcation model has been trained and evaluated

with texts derived from, Cyber Crime Division,

open data and posts from well-known social me-

dia. According to our approach the prediction of

suicidal intention constitutes a binary classiﬁca-

tion problem in which a feature vector is placed

in a category (Y/N).

• The monitoring & alerting. The monitoring &

alerting module is actually the platform which

presents in real time the indicators of all targets

as well as the classiﬁcation.

4 CLASSIFICATION MODEL

The efﬁciency of the proposed system depends on the

performance of classiﬁcation model. The main chal-

lenge that we face is the lack of available data sources

due to the nature and anonymity of this type of data.

Cyber Crime Division of Greek police intervenes in

cases related to suicidal intentions in which citizens

inform of their attempt in social media, blogs or open

sources. Respecting personal information and accord-

ing to ethical principles and Greek legislation, a real

text corpus (fully anonymised) of suicidal cases, in

which Cyber Crime Division intervened in the past,

was used for the evaluation of our system. In addi-

tion the dataset was enriched with text with normal

behavior provided by diverse sources. Furthermore,

we added suicidal notes from two social media Tum-

blr

and Whisper

. Also, we introduced a detailed

graph model that makes easy the decision whether a

htt ps : //www.tumblr.com/

htt ps : //whisper.sh/

text implies or not a suicidal manifestation as in it is

shown in Figure 2; we explain this in the sequel.

4.1 Data Preparation

In this section, there is a brief description of the fea-

tures which are used for the training procedure of dif-

ferent classiﬁcation algorithms. The choice of co-

hesion and linguistic metrics were based on previ-

ous research concerning the relationship between psy-

chological health and language use as it is described

in (Lightman et al., 2007; Stirman and Pennebaker,

2001). In addition, we introduce personality traits as

additional characteristics.

There is a fruitful discussion through academics about

the relation of personality traits with suicidal in-

tention (Carrillo et al., 2001; Kotrla Topi

c et al.,

2012; Rozanov and Mid’ko, 2011; Soltaninejad et al.,

2014). Figure 2 presents a graph with the association

of the ﬁve basic dimensions of personality, that re-

main stable in individuals forming the Big Five Model

(McCrae and John, 1992), with two factors of Auto-

Destructive Behavior and Depression which imme-

diate inﬂuence the suicidal ideation. The Depres-

sion and Auto-Destructive Behavior are recognized

as signiﬁcant predictors of various suicidal manifes-

tations, in particular, phenomena of hopelessness and

suicidal ideation (Kotrla Topi

c et al., 2012; Rozanov

and Mid’ko, 2011). According to Big Five, the hu-

man personality is described as a vector of ﬁve val-

ues of traits. The combination of Big Five personal-

ity dimensions explain the dynamics of a personality.

For example, a person may be very talkative (high

Extraversion), not very tolerant and sensitive (low

Agreeableness), systematic and punctual (high Con-

scientiousness), easily anxious (high Neuroticism)

and extremely curious (high Openness). Each per-

sonality trait correlates differently with Depression

and Auto-Destructive Behavior. More speciﬁcally

individuals that present open to fantasy, but not to

actions are prone to harmful Auto-Destructive in-

tentions and depression respectively (Carrillo et al.,

2001). Furthermore, an extrovert person seems to be

less risky to deal with depression or to harm himself

(Kotrla Topi

c et al., 2012). Also, people that lack

of sufﬁciency, self-discipline, self-control in decision

making increase the probability of belonging to the

depression and suicidal class (Rozanov and Mid’ko,

2011; Soltaninejad et al., 2014). In study (Soltanine-

jad et al., 2014) is mentioned that the high degree of

neuroticism which includes anger, hostility, impulsiv-

ity and vulnerability constitutes a signiﬁcant of de-

veloping psychiatric disorders such as mood disor-

ders, depression which may lead to suicide (Solta-

SIAS: Suicidal Intentions Alerting System

293

Figure 1: Alerting system architecture

Figure 2: Association of Personality Traits with Suicidal

Ideation.

ninejad et al., 2014). Last but not least people who

react sensitively, tolerant and kind of situations could

lead confront with auto destructiveness and depres-

sion (Kotrla Topi

c et al., 2012).

The personality traits in Big Five model can be

extracted based on a questionnaire that determines

user personality as described in (John and Srivastava,

1999). However, we follow an approach for unsuper-

vised recognition of personality traits deriving from

text analysis (Celli, 2012).

Cohesion & Emotional Instability, Linguistic and

Personality Traits constitute the 3 sets of features as it

summarily described in Table 1. The prediction fea-

tures are produced by the linguistic analysis of texts

for each individual.

Cohesion and Emotional Instability Metrics:

• Argument Overlap: This index measures aspects

of cohesion. The argument overlap index helps to

decide readability, complexity and grade level of

a particular corpus.

• Latent Semantic Analysis (LSA): LSA is a mea-

sure of cohesion like argument overlap. The LSA

index compares the contextual similarity between

sentences recognizing text’s cohesiveness.

• Word Concreteness: This feature measures the

terms’ concreteness of individuals in the written

texts.

• Communication Words: This measure is the ra-

tio of communication words in the sentences by

using a certain dictionary of words, such as talk,

share and converse. We created these dictionar-

ies adding the synonyms from Wordnet (Miller,

1995).

• Tense/Aspect Ratio: This index declares the de-

gree that sentences are linked by the time relation

in order to extract the overall temporal cohesion

on the sentences.

• References to Time: The feature exposes the ra-

tio of verbs which are referred to future tense and

words related to time in general.

• Death-Themed Words: This measure is the ratio

of death-oriented words in the sentences by us-

ing a certain dictionary. The dictionary includes

words related to death and dying and their syn-

onyms.

• Swear Words: A list of vulgar expressions has

been created to extract the ratio of swear words

in the sentences.

• Emotion Words: The ratio of words that address

affective or emotional processes. We divide these

words to positive and negative emotion.

Linguistic Metrics:

• Punctuation metrics: The number of commas,

question marks, exclamation marks, parenthesis

and all punctuation divided by the total number

of tokens in the text.

• Reference metrics: The number of @, ﬁrst person

(singular and plural) pronouns divided by the total

number of tokens in the text.

• Part of speech metrics: The number of ﬁrst person

singular pronouns, prepositions, pronouns, ﬁrst

person plural pronouns, second person singular

WEBIST 2017 - 13th International Conference on Web Information Systems and Technologies

294

pronouns, numbers divided by the total number

of tokens.

• Emoticons: The number of emoticons that express

negative and positive feelings divided by the total

number of tokens.

• Word/token metrics: This feature set includes the

mean word frequency, the type/token ratio, the

number of words longer than 6 letters divided by

the total number of tokens. word count: the num-

ber of words of the text.

• Special patterns: The number of external links

and the number negative particles divided by the

total number of tokens in the text.

Personality Traits:

• Agreeableness (A): This personality dimension

includes attributes such as affability, tolerance,

sensitivity, trust and kindness.

• Conscientiousness (C): Common features of

this dimension include organization, punctuality,

achievement-orientation and dependency.

• Extraversion (E): This trait includes individuals

such as outgoing, talkative, sociable and enjoying

social situations.

• Neuroticism (N): Individuals high in this trait

tend to be anxious, irritable, temperamental and

moody.

• Openness (O): This trait includes curiosity, orig-

inality, intellectuality, creativity and openness to

new ideas.

4.2 Training & Evaluation

The feature vectors are enriched with the category for

each individual Y , N for suicidal and non-suicidal in-

tention respectively in order to form the datasets for

training and evaluation of the classiﬁers. We follow

the same approach as previous studies (Burnap et al.,

2015; Thompson et al., 2014; Abboute et al., 2014)

introducing the dataset to different classiﬁers to in-

vestigate the appropriate one which ﬁts efﬁciently for

this type of data. The performance was evaluated by

F-measure. Also, the parameter selection is tested us-

ing a greedy approach in each classiﬁer separately.

5 EXPERIMENTAL RESULTS

5.1 Implementation

We gathered 200 samples of suicidal and non-suicidal

intention related notes as it is shown in Table 2. The

Table 1: The User Proﬁle Feature Vector.

Features # Description

Linguistic Metrics 21 Punctuation(5),

Reference(2),

Part-of-speech(6),

Emoticons(2),

Word/token(4),

Special patterns(2)

Cohesion Metrics 21 Tense/Aspect ratio,

References to

Time(3), Argu-

ment Overlap(9),

Communication

Words, Death

Themed Words,

Emotion Words(3),

Word Concrete-

ness, LSA, Swears

Words

Personality Traits 5 Openness,

Neuroticism,

Agreeableness,

Conscientiousness,

Extraversion

suicidal notes are derived from Cyber Crime Divi-

sion of Greek police in cases that information about

their suicide attempt has been exposed in social me-

dia, blogs and open sources. Furthermore, we use sui-

cidal notes from 2 well-known social networks (Tum-

blr, Whisper). The features were separated into 3 sets

which each one produces a different dataset. The ﬁrst

set includes Cohesion & Emotional Instability Met-

rics (Cohesion), the second set includes the combi-

nation of the ﬁrst set with Personality traits (Coh-

B5) and the third one the aggregation of all features.

We selected Naive Bayes, SVM, Rotation Forest, Ad-

aBoost and J48 as representative classiﬁers in order

to examine the performance for this type of data.

The classiﬁers are evaluated by the F-measure metric

which is the harmonic mean of precision and recall.

We separated each dataset to a train and a test set,

using two approaches:

• K-Fold Cross-Validation (K=10 Fold).

• Leave-One-Out Cross-Validation

The concept of using both techniques is that split-

ting with 10-Fold Cross-Validation, important infor-

mation can be removed from the train set. However,

the Leave-One-Out Cross-Validation technique evalu-

ates the classiﬁcation performance based on one sam-

ple.

The text processing as well as the feature extraction

and training of classiﬁers was implemented in Python

SIAS: Suicidal Intentions Alerting System

295

Table 2: Number of Instances per Class.

Class Instances

Suicidal Intention 114

Non-Suicidal Intention 106

2.7 using NLTK

and scikit-learn

modules.

5.2 Results & Discussion

In this section, the results of our research are pre-

sented. We evaluate our dataset with 10-Cross-

Validation and Leave-One-Out methods. Figure 3 and

Figure 4 depict performance for all classiﬁers. Ac-

cording to the result, we observe that the performance

of the classiﬁers in terms of F-measure is better in the

dataset with the aggregation of all features than re-

moving the Linguistic and Personality Traits sets. We

consider that the add on features improves the per-

formance of all classiﬁers except SVM and Rotation

Forest, which deviates in the Coh - B5 set of features

in Leave-One-Out evaluation.

Rotation Forest classiﬁer outperforms in comparison

to the other classiﬁers regarding F-measure. How-

ever Rotation Forest presents the highest deviation be-

tween the two methods of evaluation. Furthermore,

the use of linguistic metrics and personality traits in

the training of the classiﬁer enhances the performance

10% approximately in the 10-Cross-Validation. The

combination of Cohesion metrics with Big Five traits

improves or remains the performance. The aggre-

gation of all features improves the F-measure which

is 0.805 in the worst case using J48 classiﬁer and

reaches 0.895 in the best case using Rotation Forest.

In our understanding the use of Rotation Forest ben-

eﬁts the efﬁcient prediction of suicidal intention of

certain targets and enhance the alerting system. Also

the majority of the extracted features from text analy-

sis is language independent and our system can easily

be expanded to any language using a speciﬁc part of

speech and dictionaries for communicative, death and

swears words.

Table 3: Cohesion Metrics.

Classiﬁer 10-CV Leave-One-Out

Naive Bayes 0.727 0.727

SVM 0.795 0.804

Rotation Forest 0.8 0.814

AdaBoost 0.764 0.736

J48 0.704 0.677

htt p : //www.nltk.org/

htt p : //scikit − learn.org

Table 4: Cohesion Metrics and Big-Five.

Classiﬁer 10-CV Leave-One-Out

Naive Bayes 0.737 0.727

SVM 0.777 0.786

Rotation Forest 0.804 0.786

AdaBoost 0.764 0.736

J48 0.741 0.713

Table 5: All Metrics.

Classiﬁer 10-CV Leave-One-Out

Naive Bayes 0.836 0.764

SVM 0.841 0.85

Rotation Forest 0.895 0.868

AdaBoost 0.818 0.832

J48 0.805 0.814

Figure 3: 10-cross validation (F-measure).

Figure 4: Leave-One-Out (F-measure).

6 CONCLUSIONS

In this work, we looked into the design of the alert-

ing system (SIAS) for the immediate intervention of

authorities in suicidal intention. Our methodology in-

cludes 5 phases with the Targeting, Using Proﬁle Cre-

ation, Feature Extraction, Classiﬁcation and Alerting.

This paper has concentrated on building an efﬁcient

classiﬁer with natural language processing and data

mining techniques to predict the suicidal intention us-

WEBIST 2017 - 13th International Conference on Web Information Systems and Technologies

296

ing features derived from text analysis. The results

depict with high accuracy the suicidal manifestation

in the text of users in danger to commit suicide.

As a future work, we are considering to explore al-

ternative factors which are not introduced in our ap-

proach and potentially inﬂuence the prediction of sui-

cidal intention such as the behavior in social net-

works.

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