Bullyspoiler: Detect and Block Cyberbullying Tweet and Bully using Deep Learning

Irish Interdisciplinary Journal of Science & Research (IIJSR)
Vol.6, Iss.2, Pages 65-75, April-June 2022
ISSN: 2582-3981 www.iijsr.com
65
Bullyspoiler: Detect and Block Cyberbullying Tweet and Bully using Deep Learning
Soundararajan K1
, Pathmapriya S2
, Rajashree R3
& Sujitha J4
1
Assistant Professor, Department of Information Technology, Vivekanandha College of Technology for Women.
2-4
Student, Department of Information Technology, Vivekanandha College of Technology for Women.
DOI: http://doi.org/10.46759/IIJSR.2022.6209
Copyright © 2022 Soundararajan K et al. This is an open access article distributed under the terms of the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Article Received: 12 March 2022 Article Accepted: 21 May 2022 Article Published: 15 June 2022
1. Introduction
Cyberbullying is bullying that takes location over digital gadgets like cellular telephones, computer systems,
and pills. Cyberbullying can occur via SMS, text, and apps, or on-line in social media, boards, or gaming in
which people can view, take part in, or share content. Cyberbullying consists of sending, posting, or sharing
negative, harmful, false, or mean content about a person else. it is able to encompass sharing private or
non-public data about someone else causing embarrassment or humiliation. a few cyberbullying crosses the
line into unlawful or criminal behavior.
Special forms of Cyberbullying
There are many methods that a person can fall sufferer to or revel in cyberbullying whilst the usage of
technology and the internet. a few common strategies of cyberbullying are:
Harassment – whilst someone is being harassed online, they're being subjected to a string of abusive messages
or efforts to contact them with the aid of one character or a set of humans. humans can be harassed via social
media as well as via their cell telephone (texting and calling) and e mail. most of the touch the sufferer will
receive could be of a malicious or threatening nature.
Doxing – Doxing is while an person or institution of people distribute any other person’s private records
which includes their home deal with, cellular phone range or place of job onto social media or public forums
without that character’s permission to accomplish that. Doxing can motive the sufferer to sense extremely
hectic and it may affect their intellectual fitness.
Cyberstalking – similar to harassment, cyberstalking includes the wrongdoer making continual efforts to
benefit touch with the sufferer, but this differs from harassment – more commonly than no longer, humans
ABSTRACT
Social networking site is being hastily expanded in latest years, which provides platform to join human beings all over the global and share
their hobbies. however, Social Networking sites is providing possibilities for cyberbullying activities. Cyberbullying is harassing or insulting
a person through sending messages of hurting or threatening nature the usage of digital communication. Cyberbullying poses full-size chance
to bodily and intellectual fitness of the victims. as a result, it's far crucial to reveal person's posts and clear out the detest speech related
publish earlier than it's far spread. Detection of cyberbullying and the supply of next preventive measures are the main courses of movement to
fight cyberbullying. This paper proposed a system for computerized detection and prevention cyberbullying thinking about the primary traits
of cyberbullying such as intention to damage an character, repeatedly and over time and the use of abusive curl language or hate speech the
use of LSTM set of rules. The proposed version is capable to stumble on hate content on Twitter robotically. This method is based on a bag of
phrases and TFIDF (term frequency-inverse record frequency) technique. Those capabilities are used to educate deep mastering classifiers.
The detest stage encapsulates the level of hate in a given virtual surroundings. We present methods to robotically decide the dislike stage, and
block submit and users of the post with the aid of the utilising transfer mastering on pre-trained language fashions with annotated information
to create automatic hate detectors.

66
will cyberstalk every other character due to deep feelings in the direction of that character, whether they're
fine or negative. a person who's cyberstalking is much more likely to amplify their stalking into the offline
international.
Revenge porn – Revenge porn, is when sexually express or compromising photos of a person were disbursed
onto social media or shared on revenge porn unique websites without their permission to achieve this.
normally, pictures of this nature are posted with the aid of an ex-accomplice, who does it with the cause of
inflicting humiliation and harm to their popularity.
Swatting – Swatting is whilst a person calls emergency responders with claims of dangerous activities taking
location at an deal with. people swat others with the purpose of inflicting panic and worry when armed
reaction gadgets arrive at their domestic or place of work. Swatting is more popular inside the online gaming
network.
Corporate assaults – inside the company global, assaults may be used to send masses of data to a website so
that you can take the website down and make it non-purposeful. corporate attacks can have an effect on public
self-belief, unfavorable companies reputations and in a few times, pressure them to crumble.
Account hacking – Cyberbullies can hack into a victim’s social media debts and submit abusive or damaging
messages. this could be especially damaging for brands and public figures.
False profiles – faux social media bills can be setup with the purpose of damaging someone or brand’s
reputation. this may easily be done by acquiring publicly to be had snap shots of the sufferer and making the
account seem as authentic as viable.
Slut shaming – Slut shaming is whilst someone is referred to as out and labelled as a “slut” for something that
they've carried out formerly or maybe simply how they dress. This form of cyberbullying frequently happens
whilst someone has been sexting some other person and their pictures or conversations grow to be public. it's
far seen greater usually inside young human beings and teenagers however everybody can fall sufferer to
being slut shamed.
2. Literature Survey
1. Accurate Cyberbullying Detection and Prevention on Social Media
Objective:
The aim of this research could be a system for automatic detection and interference cyberbullying considering
the most characteristics of cyberbullying like Intention to hurt a private, repeatedly and over time and
victimisation abusive curl language or cyberbullying victimisation supervised machine learning.
Methodology:
the use of virtual/social media is growing day by day with the advancement of generation. folks within the
ordinal century square measure being raised in AN internet-enabled world with social media. Communication
has been only one button click. even if there square measure lots of opportunities with digital media folks tend

67
to misuse it. folks unfold emotion toward someone in social networking. Cyberbullying affects folks in
numerous aspects. It doesn’t have an effect on just for health, there square measure a lot of completely
different aspects which can lead life to a threat. Cyberbullying could be a worldwide fashionable development
that humans cannot avoid hundred % however may be prevented. Most existing solutions have shown
techniques/approaches to observe cyberbullying, however they're not freely on the market for end-users to
use. They haven’t thought-about the evolution of language that makes a giant impact on cyberbullying text.
this text papered a TF-IDF (Term Frequency, Inverse Document Frequency) by victimisation TFIDF which
might live the importance of words in an exceedingly document and customary words like “is”, “am” don't
have an effect on the results because of force. this text used Support Vector Machines (SVM), a well-known
economical binary classifier to coach the model. logistical regression was accustomed choose the most
effective combination of options. SVM algorithmic rule, coaching knowledge is employed to be told a
classification operate. It will classify new knowledge not antecedently seen in one amongst the 2 classes. It
separates the coaching knowledge set into 2 classes employing a massive hyperplane. logistical regression
could be a linear classifier that predicts the chances.
2. Cyberbullying Detection on Social Networks Using Machine Learning Approaches
Objective:
The aim of this research is to style and develop a good technique to sight on-line abusive and bullying
messages by merging tongue process and machine learning.
Methodology:
the employment of social media has grownup exponentially over time with the expansion of the web and has
become the foremost influential networking platform within the twenty first century. However, the
improvement of social property typically creates negative impacts on society that contribute to a handful of
dangerous phenomena like on-line abuse, harassment cyberbullying, crime and on-line troll. Cyberbullying
often results in serious mental and physical distress, notably for girls and kids, and even typically force them
to try suicide. on-line harassment attracts attention thanks to its sturdy negative social impact. several
incidents have recently occurred worldwide thanks to on-line harassment, like sharing non-public chats,
rumours, and sexual remarks. Therefore, the identification of bullying text or message on social media has
gained a growing quantity of attention among researchers. the aim of this text is to style and develop a good
technique to sight on-line abusive and bullying messages by merging tongue process and machine learning. 2
distinct options, particularly Bag-of - Words (BoW) and term frequency-inverse text frequency (TFIDF), ar
accustomed analyse the accuracy level of 4 distinct machine learning algorithms.
3. Cyberbullying Detection on Twitter using Multiple Textual Features
Objective:
The aim of this research is to focus on the Japanese text on Twitter and construct an optimal model for
automatic detection of cyberbullying by extracting multiple textual features and investigating their effects
with multiple machine learning models.

68
Methodology:
This article aims at automatic cyberbullying detection and utilize machine learning methods to realize the
purpose. Two most important aspects for classifying cyberbullying based on machine learning are what
features to be extracted and what machine learning models to be selected. This article focus on the Twitter text
(hereinafter referred to as tweet) and intend to find the features that mostly contribute to cyberbullying
detection. This article uses the technique of text mining and analyse a range of textual features including
n-gram, Word2Vec, Doc2Vec, tweets’ emotion values, and unique characteristics on Twitter. In addition,
multiple machine learning models including linear models, tree-based models and deep learning models are
investigated with multiple textual features to construct an optimal model. Based on the collected tweets, it
evaluates the quality of automatic detection of cyberbullying, and find that the best model with predictive
textual features and it achieve the accuracy of over 90%.
4. Cyberbullying Detection on Instagram with Optimal Online Feature Selection
Objective:
The aim of this research could be a novel rule to drastically scale back the amount of options employed in
classification for cyberbullying detection.
Methodology:
Cyberbullying has emerged as a large-scale social drawback that demands correct ways for its detection in an
endeavor to mitigate its damaging consequences. whereas automatic, data-driven techniques for analysing
and police investigation cyberbullying incidents are developed, the measurability of existing approaches has
mostly been neglected. At a similar time, the complexities underlying cyberbullying behaviour (e.g., social
context and ever-changing language) build the automated identification of “the best set of features” to use
difficult. to deal with this gap by formulating cyberbullying detection as a sequent hypothesis testing
drawback. supported this formulation, this text proposes a completely unique rule to drastically scale back the
amount of options employed in classification. this text demonstrates the utility, measurability and
responsiveness of this text employing a real-world dataset from Instagram, the web social media platform
with the very best proportion of users coverage experiencing cyberbullying. this text approach improves
recall by a staggering 700%, whereas at a similar time reducing the common range of options by up to ninety
nine.82% compared to progressive supervised cyberbullying detection ways, learning approaches that need
weak superintendence, and ancient offline feature choice and spatiality reduction techniques.
5. Automated Cyberbullying Detection using Clustering Appearance Patterns
Objective:
The aim of this research is to enhanced the Naïve Bayes classifier for extracting the words and examining
loaded pattern clustering.
Methodology:
This article developed an automatic cyberbullying detection system to detect, identify, and classify
cyberbullying activities from the large volume of streaming texts from OSN services. Texts are fed into

69
cluster and discriminant analysis stage which is able to identify abusive texts. The abusive texts are then
clustered by using K-Mean. Naïve Bayes is used as classification algorithms to build a classifier from the
training datasets and build a predictive model. Moreover, it also used Naïve Bayes to classify the abusive texts
into one of the eight pre-defined categories. The categories include activities approach, communicative,
desensitization, compliment, isolation, personal information, reframing, and relationship. The proposed
approach consists of two main methods. The first method aims to clean and pre-process the datasets by
removing non-printable and special characters, reducing the duplicate words and clustering the datasets. The
second one concerns classification model to predict the text messages for preventing cyberbullying. The
method was executed on Cybercrime Data, which is a manually labelled dataset, for 170,019 posts and
Twitter web site for 467 million Twitter posts.
3. Existing System
However, maximum of the present research anticipate that every piece of records spreads independently
regardless of the interactions between contagions. Because of the massive extent of contagions and users in
the on-line social networks, gaining knowledge of interactions for each pair of contagions and customers is
impractical. Social media may be a double-edged sword for society, both as a handy channel changing ideas
or as an unexpected conduit circulating faux information through a huge population.
In this chapter existing machine learning classifiers used for tweet classification are mentioned. This chapter
analysed 5 supervised machine learning algorithms: Support Vector Machines (SVM), Naive mathematician
(NB), Random Forest (RF), call Tree (DT), Gradient Boosting model (GBM), supplying Regression (LR) and
ballot Classifier (Logistic Regression C random Gradient Descent classifier).
Random Forest
RF may be a tree based mostly classifier within which input vector generated trees arbitrarily. RF uses
random options, to form multiple call trees, to create a forest. Then category labels of take a look at
information area unit expected by aggregating ballot of all trees. Higher weights area unit allotted to the
choice trees with low worth error. Overall prediction accuracy is improved by considering trees with low error
rate.
Support Vector Machine
The Support vector machine (SVM) is known that executes properly as sentiment analysis. SVM typifies
preference, ambit and makes usage of the mechanisms for the assessment and examines records, that area unit
earned at intervals the index space. Arrangements of vectors for each magnitude embody crucial details. data
(shown in variety of vector) has been organized in sort to attain this target. Next, the border is classified in 2
coaching sets by stratagem. this can be an extended method from any space within the coaching samples.
Support-vector machines in machine learning includes targeted learning models connected to learning
evaluations that examine material that's exploited to categorize, additionally revert examination.
Naive mathematician
Ordering approach, Naive Bayes(NB), with durable (naive) freelance assumptions among stabilities, depends
on theorem. NB classifier anticipates that the proximity of a selected component of sophistication that's

70
confined to the closeness of a handful of various variables. as an example, a natural organic product is
presumptively viewed as Associate in Nursing apple, if its shading is redness, if sort of it's spherical and it's
roughly three creeps in expansiveness. In machine learning, Naive mathematician classifiers area unit a
gathering of essential "probabilistic classifiers" considering applying Bayes' speculation with gullible chance
assumptions between the options. they're thought-about because the minimum problematic theorem network
models.
Gradient Boosting Machine
GBM may be a cubic centimeter based mostly boosting model and is wide being employed for regression and
classification tasks, that works by a model fashioned by ensemble of weak prediction models, ordinarily call
trees. In boosting, weak learners area unit reborn to robust learners. Each new generated tree may be a
changed variety of previous one and use gradient as loss perform. Loss calculate the potency of model
coefficients fitting over underlying information. Logically loss perform is employed for model improvement.
Logistic Regression
In LR category possibilities area unit calculable on the idea of output like they predict if the input is from
category X with likelihood x and from category Y with likelihood y. If x is larger than y, then expected output
category is X, otherwise Y. Insight, a supplying approach used for demonstrating the likelihood of an explicit
cluster alternatively, prevalence is available, e.g., top/bottom, white/black, up/down, positive/negative or
happy/unhappy. this can be ready to stretch out and to indicate a tiny low range of categories concerning
events, for instance, to create a call if a picture includes a snake, hound, deer, etc., each article being notable
within the image would be appointed a likelihood where within the series of zero and one with whole addition
to at least one.
Stochastic Gradient Descent
Gradient Descent's sorts embody random Gradient Descent (SGD). SDGD is Associate in Nursing unvaried
strategy for advancing a target work with applicable perfection properties (for example differentiable or sub
differentiable). Degree of advancement is calculated by it in light-weight of development of other variables.
it's fine, is also viewed as a random guess of inclination plummet advancement, since it replaces the real
Associate in Nursinggle (determined from the full informational index) by a gauge therefrom (determined
from an every which way chosen set of the information).
Voting Classifier
Voting Classifier(VC) may be a cooperative learning that engages multiple individual classifiers and
combines their predictions, that may attain higher performance than one classifier. it's been exhibited that the
mixture of multiple classifiers may well be additional operative compared to any distinct ones. The VC may
be a meta classifier for hypothetically exceptional cubic centimeter classifiers for order through bigger half
throwing a ballot type. It executes "hard" and "soft" casting a ballot. arduous ballot offers the scientist the
possibility to foresee {the category|the category} name in situ of the last class mark that has been anticipated
usually through models of characterization.

71
Disadvantages
a. Process of reporting such cases is long, tedious job.
b. Difficult to track.
c. Most of the cyberbullying cases go unreported.
d. Low accuracy.
e. Time consuming process.
f. Problem isn't always automatically detected and no longer promptly document bullying message.
g. Response time is slow.
h. Basic features and common classifier accuracy is low.
i. data are manually labelled the use of on line services or custom programs.
j. Usually data limited only to a small percentage.
k. Blocking the blacklisting user is not considered.
4. Proposed System
This paper proposed a machine for automatic detection and prevention cyberbullying thinking about the
primary characteristics of cyberbullying together with intention to damage an individual, again and again and
through the years and the use of abusive curl language or hate speech the use of BiLSTM algorithm. The
proposed version is capable to locate cyberbullying content on social media mechanically. This method is
based totally on a bag of words and TFIDF (time period frequency-inverse report frequency) technique. these
functions are used to teach deep mastering BiLSTM classifiers. The Bullied degree encapsulates the extent of
cyberbullying in a given virtual surroundings.
Advantages
It successfully classifies the tweets in various classes.
Auto report generator generates a simple report for probable accusers.
Several analytics and report can be sent to the crime department.
Accuracy is high.
Foul language on any given page, removes it, and can highlight words as well,
This method detects the offensive post or messages it block that user id.
The “filtered content” is displayed at back to the page, in such a way preventing the display of explicit
content.
An automatically generate a report for each incident is also provided.

72
5. Modules Description
1. Social Networking internet App
Build a social networking service is a web platform which individuals use to make social networks or social
relationships with people UN agency share similar personal or career interests, activities, backgrounds or
real-life connections.
Social networking offerings target layout and also the vary of functions.
2. User Access management
2.1. New User
Create user account with Aadhar range.
2.2. Existing User
The existing users of Sn will have to be compelled to transfer a scanned copy of their Aadhar Card.
If they fail to try and do therefore, their profile are suspended inside following fifteen days.
3. Cyberbullying Classification API – coaching part
3.1. Cyber Bullying Dataset
We used cyberbullying twitter dataset from Kaggle. The dataset has 2 labels, hangdog and Non Bullyied,
3.2. Preprocessing
In this module Preprocessing was administered to change the text information suitably within the experiment.
We used decapitalization and didn't mark the begin and finish of the sentences
The system deleted #, 2 or additional areas, tabs, Retweets (RT), and stop words.
Convert matter content to vectors, our classifier best takes numerical statistics.
3.3. Feature Extraction
TF –IDF Vectoriser from sklearn module to remodel the matrix into a TF-IDF illustration. this can be
illustration is usually used in document classification and knowledge retrieval.
Bullied score classification
3.4. BiLSTM Classification
The model we tend to created mistreatment BiLSTM had a complete of 5; 218; 854 trainable parameters. the
whole design is structured as follows: Embedding) Bidirectional) world easy lay Pool) Dropout) Dense)
Dropout) Dense) SoftMax(6).The model became good with a pair of epochs.
4. Cyberbullying Detection coaching part
4.1. Sn User Live Post
In this module, user login and write post, offer comments concerning the post and conjointly chat with the
opposite social networking users.

73
4.2. Feature Extraction
In the planned model, we've used TF-IDF and Word2Vec techniques for feature extraction from the live post.
4.3. Prediction
In this module, Finally, we tend to used matrix resolution to predict hangdog or not hangdog.
5. Bullied Level Indicator
Frequency of cyberbullying themes/categories related to cyberbullying like racist, sexual, physical mean,
swear and different, the author has manually compiled a listing of words related to racist, sexual, physical
mean and swear.
BLI are taking a proportion of the associated words supported the word count of the total sentence.as associate
degree example ” you're a black nigger”, word count related to race is a pair of and full word count within the
sentence is three, the proportion is sixty seven ((2/3) *100).
lexicon was used for this purpose.
Severe
Moderate
Mild
6. Blocker
Harasser or Bully: individual UN agency initiates the bullying.
Blocker blocks the Bully Account and customer – if he incessantly bullying once warned.
7. Performance Analysis
The assessment metrics parameters are accustomed execute those classifiers. it's administered in exclusive
approaches.
(TP) = time period instances which may be high-quality and anticipated as top quality
(TN) = the despair instances which may be terrible and anticipated as terrible.
(FN) = the melancholy instances which could be nice however anticipated to be negative.
(FP) = the despair cases which may be undoubtedly terrible however expected to be tremendous.
Accuracy = (TP + TN)/(TP + TN + FP + FN) - correct class proportion
F1-rating = a pair of × Pre × Rec/(Pre + Rec)- the harmonic average of the do not forget and also the
exactitude fee
6. Conclusion
Cyberbullying is that the harassment that takes place in digital devices like mobile phones, computers and
tablets. The suggests that accustomed harass victims ar terribly diverse: text messages, applications, social

74
media, forums or interactive games. one in all the items that complicates these styles of things that occur
through the net, is that the obscurity this atmosphere permits. Since this facilitates cyberbullying will cowl
most areas of the victim’s life, that is: instructional atmosphere, work, social or captivated life. once the
identity of the harasser isn't identified, though the facts ar reportable, in several cases it's not enough to open
associate degree investigation, establish it and buy the crime committed. This paper planned a deep learning
model bifacial Long Short Term Memory (BiLSTM. Thus, this paper has designed a technique of
mechanically police work the Cyberbullying attack cases. Identifies the messages or comments or posts that
the BiLSTM model predicts as offensive or negative then it blocks that person id, then the admin will produce
machine-controlled reports and send to the priority department. Experiments ar conducted to check 3 machine
learning and a pair of deep learning models that are; (1) GBM, (2) LR, (3) NB, (4) LSTM-CNN and (5)
BiLSTM. This paper extensively utilized 2 feature illustration techniques Tf and TF-IDF. The results showed
that everyone models performed well on tweet dataset however our planned BiLSTM classifier outperforms
by victimisation each TF and TF-IDF among all. planned model achieves the best results victimisation
TF-IDF with ninety six Accuracy, ninety two Recall and ninety fifth F1-score.
7. Future Scope
For the present, the bot works for Twitter, so it can be extended to various other social media platforms like
Instagram, Reedit, etc. Currently, only images are classified for NSFW content, classifying text, videos could
be an addition. A report tracking feature could be added along with a cross-platform Mobile / Desktop
application (Progressive Web App) for the Admin. This model could be implemented for many languages like
French, Spanish, Russian, etc. along with India languages like Hindi, Gujarati, etc.
Declarations
Source of Funding
This research did not receive any grant from funding agencies in the public or not-for-profit sectors.
Consent for publication
Authors declare that they consented for the publication of this research work.
References
[1] N. Yuvaraj, K. Srihari, G. Dhiman, K. Somasundaram, A. Sharma, S. Rajeskannan, et al.,
"Nature-inspired-based approach for automated cyberbullying classification on multimedia social
networking", Math. Problems Eng., vol. 2021, pp. 1-12, Feb. 2021.
[2] R. R. Dalvi, S. B. Chavan and A. Halbe, "Detecting a Twitter cyberbullying using machine learning", Ann.
Romanian Soc. Cell Biol., vol. 25, no. 4, pp. 16307-16315, 2021.
[3] N. Yuvaraj, V. Chang, B. Gobinathan, A. Pinagapani, S. Kannan, G. Dhiman, et al., "Automatic detection
of cyberbullying using multi-feature based artificial intelligence with deep decision tree classification",
Comput. Electr. Eng., vol. 92, Jun. 2021.

75
[4] A. Al-Hassan and H. Al-Dossari, "Detection of hate speech in Arabic tweets using deep learning",
Multimedia Syst., Jan. 2021.
[5] Y. Fang, S. Yang, B. Zhao and C. Huang, "Cyberbullying detection in social networks using bi-GRU with
self-attention mechanism", Information, vol. 12, no. 4, pp. 171, Apr. 2021.
[6] A. Agarwal, A. S. Chivukula, M. H. Bhuyan, T. Jan, B. Narayan and M. Prasad, "Identification and
classification of cyberbullying posts: A recurrent neural network approach using under-sampling and class
weighting" in Neural Information Processing, Cham, Switzerland:Springer, vol. 1333, pp. 113-120, 2020.
[7] C. Iwendi, G. Srivastava, S. Khan and P. K. R. Maddikunta, "Cyberbullying detection solutions based on
deep learning architectures", Multimedia Syst., 2020.
[8] C. Van Hee et al., “Automatic Detection of Cyberbullying in Social Media Text.” 2018.
[9] M. Rezvan, S. Shekarpour, L. Balasuriya, K. Thirunarayan, V. Shalin, and A. Sheth, “A Quality
Type-aware Annotated Corpus and Lexicon for Harassment Research,” pp. 33–36, 2018.
[10] A. H. Alduailej and M. B. Khan, “The challenge of cyberbullying and its automatic detection in Arabic
text,” 2017 Int. Conf. Comput. Appl. ICCA 2017, pp. 389–394, 2017.
[11] A. Power, A. Keane, B. Nolan, and B. O. Neill, “A lexical database for public textual cyberbullying
detection,” Rev. Lenguas Para Fines Específicos, vol. 2, pp. 157–186, 2017.
[12] M. Drahošová and P. Balco, “ScienceDirect The analysis of advantages and disadvantages of use of
social media the analysis of advantages and disadvantages of use of social media in European Union in
European Union,” Procedia Comput. Sci., vol. 109, pp. 1005–1009, 2017.
[13] A. Squicciarini, S. Rajtmajer, Y. Liu, and C. Griffin, “Identification and characterization of
cyberbullying dynamics in an online social network,” in Proc. IEEE/ACM Int. Conf. Adv. Social Netw. Anal.
Mining, Aug. 2015, pp. 280–285.
[14] A. S. Srinath, H. Johnson, G. G. Dagher and M. Long, "BullyNet: Unmasking cyberbullies on social
networks", IEEE Trans. Computat. Social Syst., vol. 8, no. 2, pp. 332-344, Apr. 2021.
[15] P. Galán-García, J. G. De La Puerta, C. L. Gómez, I. Santos, and P. G. Bringas, “Supervised machine
learning for the detection of troll profiles in Twitter social network: Application to a real case of
cyberbullying,” Logic J. IGPL, vol. 24, no. 1, pp. 42–53, 2015.

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