Cyber Aggression and Cyberbullying Identification on
Social Networks
Vincenzo Gattulli
, Donato Impedovo
, Giuseppe Pirlo
and Lucia Sarcinella
Digital Innovation Srl, Via Edoardo Orabona, 4 (c/o Dipartimento di Informatica), 70125 Bari, Italy
Department of Computer Science, University of Studies of Bari “Aldo Moro”, Via Edoardo Orabona, 4, 70125 Bari, Italy
Keywords: Cyberbullying, Artificial Intelligence, Social Network, Cyber Aggression, Twitter, Machine Learning.
Abstract: Bullying includes aggression, harassment, and discrimination. The phenomenon has widespread with the great
diffusion of many social networks. Thus, the cyber aggression iteration turns into a more serious problem
called Cyberbullying. In this work an automatic identification system built up on the most performing set of
techniques available in literature is presented. Textual comments of various Italian Twitter posts have been
processed to identify the aggressive phenomenon. The challenge has been also identifying aggressive profiles
who repeat their malicious work on social networks. Two different experiments have been performed with
the aim of the detection of Cyber Aggression and Cyberbullying. The best results were obtained by the
Random Forest classifier, trained on an ad-hoc Dataset that contemplates a series of comments extracted from
Twitter and tagged manually. The system currently presented is an excellent tool to counter the phenomenon
of Cyberbullying, but there are certainly many improvements to be made to improve the performance of the
Social Networks are progressively exposed to
harmful issues including Cyber Aggression and
Cyberbullying. Cyber Aggression refers to aggressive
online behaviour using digital media content (text,
images, videos, etc.) to cause harm to another person.
Cyberbullying is defined as "An aggressive
intentional act by an individual or a group of
individuals, using electronic forms of contact,
repeated over time against a victim who cannot easily
defend himself" (Dredge et al., 2014).
This work deals with the automatic recognition of
Cyber Aggression (detected in textual comments) and
cyberbully profiling (Cyber Aggression repeated by a
certain user in multiple posts over time). The
experiments aim to prevent the phenomenon of
Cyberbullying on Social Networks. In this study
Italian behavioral patterns will be studied, recovering
recurrent patterns in the formulation of Italian
sentences or in the typology of attacks with the aim
of collecting and labeling comments and creating a
dataset called "Italian Aggressive Dataset". The
attention is mainly focused on the kind of language
used by the attacker considering vocabularies of
expressions and words belonging to the vulgar jargon.
Next, each word within the sentence is weighted
according to its negative, neutral, or positive value
along with a large set of other characteristics. The
main contributions of this work are:
An ensemble of features with a comparison of
different classification models.
The creation of a vocabulary of Italian words
considering four types of categories: Bad Word,
Second Person, Threats, Bulling Terms. These
dictionaries contain some of the most common
terms in Italian, used to verbally attack and
offend someone. The definition of these words
was made by viewing countless comments under
the posts of famous singers and politicians.
"Aggressive Italian Dataset": Creation and
labeling of a balanced Italian dataset composed
of aggressive and non-aggressive comments,
extracted from the social platform Twitter
The rest of the work is organized as follows.
Section 2 will illustrate the state of the art. Section 3
will describe the software design and implementation
methodology. Section 4 will describe the "Italian
Aggressive Dataset". The results of the
experimentation are provided in Section 5. Finally,
Section 6 concludes the document.
Gattulli, V., Impedovo, D., Pirlo, G. and Sarcinella, L.
Cyber Aggression and Cyberbullying Identification on Social Networks.
DOI: 10.5220/0010877600003122
In Proceedings of the 11th International Conference on Pattern Recognition Applications and Methods (ICPRAM 2022), pages 644-651
ISBN: 978-989-758-549-4; ISSN: 2184-4313
2022 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved
This document focuses on textual data being the most
widespread data in social media having the aim in
identifying aggression and pattern could be referred to
cyberbullies in their mild stage (Shah et al., 2021).
Many researchers have worked on textual comments
collected on Social Networks. Amali et. al (Ishara
Amali & Jayalal, 2020) with the aim of determining
insults (profane words) in the comments within the
tweets, five rules were taken into consideration (Ishara
Amali & Jayalal, 2020): i) percentage of bad words
within the tweet, ii) combination of first-person
pronoun, bad word and a second person pronoun, iii)
combination of second person pronoun with a bad
word combination of third person pronoun with a bad
word, iv) combination of first-person pronoun, bad
word and a third person pronoun. Selected comments
were successively classified adopting SVM, K-
Nearest Neighbors (KNN) and Naïve Bayes (NB): the
SVM with RBF kernels scored better than others
reporting a 91% f1-score.
Chatzakou et al. (Chatzakou et al., 2017)
processed 1.6 million tweets collected over 3 months
of conversations. In this case user-based features, text-
based features and network-based features were
considered. User-based features had the aim of
describing the general user’s behaviour (e.g., bully,
and generic aggressors are faster than normal users in
posting activity), text-based features were referred to
uppercases, specific word embedding and to the
positive/negative sentiment in short text. The
network-based features were aimed to evaluate
popularity, reciprocity, power difference and
influence of users within the group. RF classifier was
able to perform 90% of AUC (Chatzakou et al., 2017).
Raza et al. (Raza et al., 2020) developed a model with
LR, RF and NB algorithms to identify if a particular
comment is an insult, threat, or a hate message, with
Voting and AdaBoost classifiers. Supervised machine
learning with LR achieved 82.7% accuracy. With the
voting classifier, an accuracy of 84.4% was observed
(Raza et al., 2020). Shtovba et al. (Shtovba et al.,
2019) found syntactic dependencies in comments, i.e.,
relationships with proper nouns, personal pronouns,
possessive pronouns, etc. Three features were
highlighted that greatly improve detection: the number
of dependencies with proper names in the singular, the
number of dependencies that contain profanity, and
the number of dependencies between personal
pronouns and profanities. The data used comes from
the Kaggle contest "Toxic Comment Classification
Challenge (Large number of Wikipedia comments)".
An DT classifier is used (Shtovba et al., 2019).
Dwivedi et al. (Kumari et al., 2021b) present a
deep learning-based model (LSTM network) detecting
different levels of aggression (direct, indirect and no
aggression) in social media posts in a bilingual
scenario. Datasets from Facebook and Twitter with
bilingual (English and Hindi) data were used (Kumari
et al., 2021b). Sentiment description has been also
considered evaluating comments i) contain remarks,
critic, sarcasm, etc., ii) referred to specific topics (e.g.,
politics, crimes, race, sex, etc.), iii) containing swear
words. In this case three different classifiers were
adopted: KNN, SVM, and LR. The best performances
were achieved by the SVM with linear kernel
reporting 86% on accuracy and recall and 84% of f1
score (Chen et al., 2017). The automatic detection of
cyberbullying can be exploited considering
psychological features of users, including
personalities, feelings, and emotions. User
personalities can be determined using the Big Five
model (openness to experience, conscientiousness,
extraversion, agreeableness, and neuroticism) (Costa
& McCrae, 1992) (John & Srivastava, n.d.) and Dark
Triad (narcissism, Machiavellianism, and
psychopathy) which specifically refers to malevolent
qualities (Paulhus & Williams, 2002) (Goodboy &
Martin, 2015). Many scientific papers have used
hybrid approaches, correlating images (Dentamaro et
al., 2021) and post comments under the same images.
Singh et al. (Kumari et al., 2021a) present a model
based on Convolutional Neural Network (CNN) and
Binary Particle Swarm Optimization (BPSO) to
classify social media posts containing images with
associated textual comments into non-aggressive,
medium aggressive and highly aggressive classes. The
proposed model with optimized features and Random
Forest classifier (Dentamaro et al., 2020) achieves a
weighted F1-Score of 0.74 (Kumari et al., 2021a).
Kumari et al. (Kumari & Singh, 2021a) present textual
features extracted using a three-layer parallel
convolutional neural network. The image and text
features are then combined to obtain a hybrid feature
set that is further optimized using a binary firefly
optimization algorithm (Kumari & Singh, 2021a).
Finally, Singh et al. (Kumari & Singh, 2021b) present
a pre-trained VGG-16 network and a convolutional
neural network to extract features from images and
text, respectively. These features are further optimized
using a genetic algorithm to increase the efficiency of
the whole system. The proposed model achieves an F1
score of 78% (Kumari & Singh, 2021b). The hybrid
approach was not considered due to both the lack of
datasets and the poor performance reported in the read
Cyber Aggression and Cyberbullying Identification on Social Networks
The proposed approach is organized in a pipeline
made-up of three stages:
A. Post selection and test comments extraction;
B. Feature engineering;
C. Classification and Metrics.
This paper proposes two different experiments.
The first one aims to identify Cyber Aggression, the
second one aims to identify Cyberbullying. The first
experiment identifies aggression from user comments.
In case an aggression is identified by the classifier and
there are multiple aggressions on multiple posts by the
same user, then that user could be flagged as an
aggressive profile (bully), thus giving rise to the
second phase. The system is designed not only to run
experiments as described in this paper, but also to be
able to work online. For the Training phase, the dataset
created in this study called the Aggressive Italian
Dataset is used. For the testing phase, an additional
1000 different comments were extracted from
different Twitter posts for each of the four celebrities
that we will discuss in the next subchapter. The
importance of identifying the different posts is related
to the problem of identifying cyberbullies stalking the
victim. The comments selected for the Test phase
were manually labeled, and the feature extraction
phase was performed for each comment, as well as for
the Training. Twitter comments from the Test phase
extracted are in Italian, dated November-December
2020. During the period considered, each post
contained approximately 100/150 comments (6
Twitter posts). In summary, the "Aggressive Italian
Dataset" containing 3028 comments was used for the
Training phase. As a Test, 1000 comments of different
posts were extracted for each famous person. Figure 1
illustrates the phases of the experiment.
Figure 1: General scheme of the system.
A. Post Selection and Test Comments Extraction
Famous people with large audiences and many
followers clearly attract both supporters and "haters".
Four famous Italian people who suffer acts of Cyber
Aggression have been considered in this work. Many
users carry out verbal aggression by commenting
under each post, also attacking private life, behavior
very similar to a Cyberbullying action. Profiles here
considered are: Achille Lauro (Italian singer/rapper),
Fabio Rovazzi (Italian singer and youtuber), Matteo
Renzi and Giuseppe Conte (Italian politicians). The
period selected for posts is between November-
December 2020. In this period, Italy was in a
government crisis and a coronavirus pandemic.
B. Feature Engineering
In the feature extraction phase, nine features are
Number of Negative Words (BW). This feature
has been implemented by means of a "BadWord"
vocabulary containing 540 negative words extremely
vulgar used for aggressive purposes, offenses, and
humiliations. The use of regular expressions has
allowed to identify also negative words written
grammatically incorrect, all attached or with spaces
(Ex. Assssshole asshole) (Ishara Amali & Jayalal,
Number of "non/no"(NN). The use of "no/not"
within a sentence completely changes the meaning of
the sentence from positive to negative or vice versa.
Furthermore, the presence of a large number of
“no/not” can underline the controversy of the
Uppercase (U). This is a Boolean value
indicating whether the comment is capitalized or not.
In computer jargon, uppercase comment is about
shouting something. So, it can be interpreted as an
aggression against someone (Chatzakou et al., 2017).
Positive/negative Weight of the Comment
(PW/NW). This feature includes two values: a
positive and negative weight of the comment within
the range [0,1]. To do this, the relative synset and
weight of each word was extracted, using WordNet
and SentiWordNet (M et al., 2017) (Rendalkar &
Chandankhede, 2018) and then averaged for both
positive and negative weights. The average value was
chosen to take into account the length of the comment
and therefore the number of words.
Use of the Second Person (SP). It is a Boolean
value indicating the presence or absence of a second
singular or plural form in the comment. This feature
is important because attacks are often accompanied
using the second person, thus targeting a specific
person. This feature was extracted through a specially
created dictionary containing 24 words, including
verbs and pronouns referring to the second person, for
example words ending with "tu" or "ti" ("you" in
English) (Shtovba et al., 2019).
Presence of Threats (TR), instigation to violence
or suicide. A Boolean value indicates the presence of
threats, violence, or instigation to suicide within the
comments. Many negative comments are accompa-
ICPRAM 2022 - 11th International Conference on Pattern Recognition Applications and Methods
nied using profane language or threats such as "I kill
you" or instigations to suicide "thrown from a
bridge", all expressions used only in aggressive
contexts. Even in this case, these expressions have
been identified using a specifically devoted
vocabulary containing 314 violent or instigating
expressions (Raza et al., 2020).
Presence of Bulling Terms (KW). A Boolean value
indicates the presence of the so-called keywords of
cyberbullying, or insults, used to injure or attack a
person (e.g., idiot, stupid, ...), but also target words
which in themselves do not take on a negative
meaning, but in some contexts, such as that of
cyberbullying, they can be used equally to insult (e.g.,
clown, whale, garbage, ...). In this case a vocabulary
containing 359 terms identified as insults and possible
insults has been created.
Comment Length (L). This feature represents the
length of the comment in terms of words. In fact, it
has been observed that most negative comments are
made up of a few words, usually no more than three.
The choice of these nine features was dictated by
both the state of the art and a careful analysis made
on the real comments of the people of Twitter. It has
been found that the language on the web is rough, full
of expressions and words belonging to the vulgar
jargon that leaves little room for misunderstanding.
The Italian language has many identical terms that are
used when verbally attacking someone. This led to the
creation of a list of these words, creating a veritable
dictionary of profane words. The weight of profane
words does not have to decree with certainty the
negativity of the sentence, for this reason another
feature has been devised that considers the weight
that a word can have in a sentence, both in negative
and in positive. Again, it was noted that some
negative comments were capitalized, as if to simulate
a higher tone of voice. This has led to thinking of a
way to keep track of this particularity. Another
feature that was highlighted is the presence of
negation in aggressive comments, in fact in many
cases it was noted that the aggression sessions started
with the word "no / non" to contradict the victim.
Again, the presence of the second person, an example
would be "TI uccido" (I kill you), "DEVI morire"
(You must die). As an enrichment of the vocabulary
on "profane words", two other vocabularies have
been defined with expressions very close to
aggressive juvenile language. The first is defined as
expressions of incitement to violence with the
purpose of wishing someone's death. The second are
defined as expressions linked to juvenile and
offensive language, closely linked to pokes and
assonances with animals in a derogatory way.
Aggression in both bullying and in a more general
context embraces these themes which have been
gradually considered and applied to the extraction of
each individual comment.
C. Classification and Matrics
The classification of the comments has been carried
out using four supervised classification algorithms:
SVM with linear kernel, RF, MLP and DT. The
problem of classification has been considered here as
a two class one: Aggressive comments and Non-
Aggressive ones. In this work the SVM kernel is linear
because it works well for text classification (Malmasi
& Zampieri, 2018) (Davidson et al., n.d.). In this work
the maximum RF depth has been set at 10, and the
number of estimators is set at 1800 (Islam et al., 2019)
(Chatzakou et al., 2017). In this work the MLP alpha
parameter has been set equal to 0.05, hidden layer
levels equal to (25, 20) and learning rate equal to 0,001
(Ramchoun et al., 2016). The parameters considered
were tested as best after a Greed Search approach.
Four parameters were considered to evaluate the
system performance: Accuracy, Precision (P), Recall
(R), F1-score (F1) (Prastowo et al., 2019).
The "Aggressive Italian Dataset" consists of Italian
comments extracted from Twitter, both Aggressive
and Non-Aggressive and contains 3028 comments.
Comments were divided between 1514 aggressive
and 1514 non-aggressive. The dataset was carefully
balanced keeping the same number of aggressive and
non-aggressive comments, labelled (T) with the
manual procedure explained below. Each comment
was analyzed by ten people, each person categorized
the comment as aggressive and non-aggressive
through their attitude towards the issue.
Finally, the most frequent classes were assigned
to each of the comments. Aggression was understood
as any form of aggression that hurt the sensibilities of
the person being attacked. The content of the
comments did not have to contain a profane word, but
a verbal attack that could hurt any person receiving
that message. While, about comments classified as
non-aggressive, those comments that did not go to
hurt the sensitivity of others were considered. After
labeling, statistically it was noted that the people in
question agreed because the selected comments carry
little ambiguity. Many aggressive comments
registered feelings of violence and aggression
account a particular person. If the dataset will be
extended and shared the labeling part will be better
Cyber Aggression and Cyberbullying Identification on Social Networks
Table 1: Evaluation of the comments of the last six posts by Achille Lauro.
chille Lauro P R F1 P R F1 P R F1 P R F1
-aggressive 0.98 0.88 0.93 0.94 0.86 0.90 0.99 0.91 0.95 0.96 0.91 0.94
Aggressive 0.70 0.94 0.81 0.64 0.83 0.72 0.77 0.98 0.86 0.75 0.75 0.81
Accuracy 0.90 0.85 0.93 0.90
Table 2: Evaluation of the comments of the six posts of Fabio Rovazzi.
abio Rovazzi P R F1 P R F1 P R F1 P R F1
-aggressive 0.94 0.84 0.89 0.89 0.78 0.84 0.98 0.83 0.90 0.92 0.86 0.89
Aggressive 0.75 0.90 0.82 0.66 0.82 0.73 0.75 0.97 0.85 0.76 0.87 0.81
0.86 0.80 0.88 0.86
Table 3: Evaluation of the comments of the last six posts by Matteo Renzi.
atteo Renzi P R F1 P R F1 P R F1 P R F1
-aggressive 0.98 0.95 0.97 0.98 0.95 0.96 0.99 0.98 0.98 0.98 0.96 0.97
Aggressive 0.74 0.89 0.81 0.71 0.84 0.77 0.85 0.95 0.90 0.76 0.88 0.82
Accuracy 0.94 0.94 0.97 0.95
Table 4: Evaluation of the comments of the last six posts by Giuseppe Conte.
Giuseppe Conte P R F1 P R F1 P R F1 P R F1
-aggressive 0.93 0.84 0.89 0.89 0.77 0.83 0.96 0.85 0.90 0.90 0.85 0.88
Aggressive 0.73 0.87 0.79 0.64 0.81 0.71 0.75 0.92 0.82 0.82 0.84 0.78
Accuracy 0.85 0.80 0.87 0.84
Table 5: Extract of the table containing the profiles of cyberbullies.
Twitter Users nAC nC nAC
Peppe*** 7 12 7 0 0 0
paoloG*** 6 8 0 3 3 0
nonseic*** 6 8 6 0 0 0
peso*** 6 8 0 6 0 0
bettav*** 5 5 0 0 5 0
marcoLu** 4 9 0 0 4 0
alessand_* 4 4 0 4 0 0
fatazu**** 4 13 0 0 0 4
Mart*** 4 4 0 4 0 0
Table 6: Extract of table containing the profiles of the victims.
RF Model Number of aggressive
comments predicted
Number of actual
Aggressive comments
classification error percentage
Achille Lauro 123 129 5%
Fabio Rovazzi 224 229 2%
Matteo Renzi 332 342 3%
Giuseppe Conte 302 329 8%
ICPRAM 2022 - 11th International Conference on Pattern Recognition Applications and Methods
5.1 First Experiment
In the first experiments are revealed multiple
observations. The results for the different characters
are very similar to each other, but the RF model
achieved the best results considering the Accuracy
value for each of the four famous characters. In fact,
the results of the RF model regarding the
identification of comments without Cyber
Aggression, P is between 96-99%, R between 83-98%
and F1-score values between 90-98%. As for the
identification of aggressive comments, the RF
achieves an P ranging from 75% to 84%, R values
ranging from 92% to 98%, F1 score values ranging
from 82% to 90%. The results are shown in Tables 1,
2, 3 and 4. In general, however, the accuracy of the
entire system is very high, with values ranging from
80% to 98%. The cases in which the accuracy
assumes very low values, are within the posts in
which there were several ironic comments or
offensive words hidden by an incorrect use of
grammar, not identified by the system. All capitalized
comments that do not present aggression could be
identified as non-aggressive, or situations in which
the aggression does not refer to the person in the post,
but to another of similar social class. Hashtags that
may contain vulgar and offensive slogans are not
5.2 Second Experiment
Based on considerations previously reported, a basic
and easy to implement approach to reveal a tendency
or a propensity to a trait of cyberbullying is to
evaluate recurrence of aggressions. To the aim the
total number of comments considered as Cyber
Aggression by the system have been enumerated.
Two tables were created. In the first, all the users who
have been identified for a Cyber Aggression through
their comments are stored, and for each of them the
total number of comments, the number of comments
identified as Cyber Aggression and the related posts
are marked. With this first table it is therefore
possible to identify the cyberbully, detecting who has
many cyber-attacks in general or aimed at a specific
victim. An example would be the user named
"paoloG****" who wrote 8 comments, and more than
half were classified as Cyber Aggressive. In addition,
three were addressed on several posts to the politician
Matteo Renzi and three were addressed to the singer
Fabio Rovazzi. This means that this user does not
attack a single social category but attacks more than
a semantically different social category. A simpler
example could be the user "mart****" who made four
comments in several posts by Fabio Rovazzi, all
classified as Cyber Aggressive (see Table 5).
The second table (see Table 6), on the other hand,
shows the results of the Random Forest. It can be seen
that for all four characters, a 10% detection error was
made out of 1000 standard comments in the Test
phase (see Table 6). In the tables, the wording "nC"
identifies the number of comments made by the
particular user, while the wording "nAC" identifies
the aggressive comments predicted by the system.
In this work, the problem of Cyber Aggression related
to Cyberbullying was considered. The results
obtained show that the identification of aggressive
comments is done with a good degree of accuracy.
Different classification schemes were compared, and
Random Forest (RF) was found to be the one that
achieved the highest accuracy for all the different
cases considered here. The next step was the
identification of Cyberbullying sessions by tracking
users who posted comments classified as aggressive.
Through this analysis it was possible to obtain the
profiles of the pages most prone to this type of
phenomenon, being able to monitor these victims, to
report the situation to the competent authorities. In
addition, in this work have been considered some
innovative Feature Engineering phases and some
state-of-the-art ones, that have allowed together with
the creation of the "Aggressive Italian Dataset" in
Italian, the possibility to identify common patterns in
the Italian culture that could identify an aggression.
In addition to the macro dataset, innovative sub
vocabularies were created that also allowed the
identification of verbal aggression. This procedure
could be performed online, in a smaller context such
as schools to prevent cyberbullying. These datasets
could only be shared if the article is accepted.
Regarding future developments, the detection of
Cyberbullying comments should be improved, as it
was noted that many slang forms, or even
grammatically incorrect, were not identified, so it is
possible to expand the vocabularies used. It is also
possible to make a deeper analysis by monitoring
victims and cyberbullies to understand the frequency
or reasons that lead to this phenomenon and thus
prevent them. Finally, it would be interesting to
greatly expand the dataset by adding comments to the
posts of other people, not necessarily famous, but
ordinary people.
Cyber Aggression and Cyberbullying Identification on Social Networks
This work is supported by the Italian Ministry of Education,
University and Research within the PRIN2017 -
BullyBuster project - A framework for bullying and
cyberbullying action detection by computer vision and
artificial intelligence methods and algorithms.
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