The introduction of the Internet has been beneficial to society. But with benefits, comes the drawbacks. Cyberbullying is one of its many drawbacks that cannot be ignored. A person’s emotional state or sentiment has a considerable impact on the content that is intended by the respective person. As per Indian context, there are numerous languages that can be prevalent but Hindi and English make up the majority of communication on various social media platforms. The current study represents the first effort to examine the function of sentiment as well as emotion data for recognizing cyberbullying content. Therefore, the proposed model is developed as a multimodal and multitasking paradigm for the purpose of detecting cyberbullies taking into account sentiment analysis and emotion identification. Additionally, the emoji that are included with tweet messages can help to better comprehend the user’s intent. Hence, emoji and tweet text are both included in the developed dataset.

1.
Krishanu
Maity
,
Sriparna
Saha
, and
Pushpak
Bhattacharyya
, “
Emoji, Sentiment and Emotion Aided Cyberbullying Detection in Hinglish
,”
IEEE Transaction on Computational Social Systems
,
2022
2.
Andrea
Pereraa
,
Pumudu
Fernandob
, “
Accurate Cyber bullying Detection and Prevention on Social Media
,”
CENTERIS - International Conference on ENTERprise Information Systems / ProjMAN - International Conference on Project MANagement / HCist - International Conference on Health and Social Care Information Systems and Technologies
2020
3.
Chris
Emmery
,
Ben
Verhoeven
,
Guy
De Pauw
,
Gilles
Jacobs
,
Cynthia
Van Hee
,
Els
Lefever
,
Bart
Desmet
,
Véronique
Hoste
,
Walter
Daelemans
, “
Current limitations in cyber bullying detection: On Evaluation criteria, reproducibility, and data scarcity
,”
the AMiCA (Innovation by Science and Technology (IWT) SBO-project 120007) project
2020
4.
Dr.
Vijayakumar
V.
,
Dr
Hari Prasad
D.
, “
A Study on deep learning algorithms for multimodal and multilingual cyberbullying detection
,”
Indian Journal of Applied Research
: Volume -
11
| Issue -
07
| July –
2021
5.
Adya
Bansal
,
Akash
Baliyan
,
Akash
Yadav
,
Aman
Kamlesh
,
Hemant Kumar
Baranwal
, “
Cyberbullying Detection on Social Networks Using Machine Learning Approaches
,”
International Research Journal of Engineering and Technology (IRJET)
Volume:
09
Issue:
05
| May
2022
6.
Mehdi Ben Lazreg Morten Goodwin Ole-Christoffer Granmo
, (
2016
), Deep Learning for Social Media Analysis in Crises Situations, The 29th Annual Workshop of the Swedish Artifcial Intelligence Society (SAIS),
Malmö, Sweden
7.
Bryan
Perozzi
,
Rami
Al-Rfou
and
Steven
Skiena
, (
2014
)
DeepWalk: online learning of social representations, Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
, Pages
701
710
.
8.
J.
Devlin
,
M.-W.
Chang
,
K.
Lee
, and
K.
Toutanova
, “
BERT: Pre-trainingof deep bidirectional transformers for language understanding
,”
2018
,arXiv:1810.04805.
9.
D. L.
Hoff
and
S. N.
Mitchell
, “
Cyberbullying: Causes, effects, and remedies
,”
Journal of Educational Administration
,
2009
.
10.
P.
Badjatiya
,
S.
Gupta
,
M.
Gupta
, and
V.
Varma
, “
Deep learning for hate speech detection in tweets
,” in
Proceedings of the 26th International Conference on World Wide Web Companion
,
2017
, pp.
759
760
.
11.
S.
Hinduja
and
J. W.
Patchin
, “
Bullying, cyberbullying, and suicide
,”
Archives of suicide research
, vol.
14
, no.
3
, pp.
206
221
,
2010
.
12.
B.
Felbo
,
A.
Mislove
,
A.
Søgaard
,
I.
Rahwan
, and
S.
Lehmann
, “
Usingmillions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasm
,”
2017
, arXiv:1708.00524.
13.
Sweta
Agrawal
,
Amit
Awekar
, (
2018
),
Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms
, arXiv:1801.06482v1 [cs.IR].
14.
R.
Kumar
,
A. N.
Reganti
,
A.
Bhatia
, and
T.
Maheshwari
,“
Aggression-annotated corpus of Hindi-English code-mixed data
,”
2018
,arXiv:1803.09402
15.
S.
Bastiaensens
,
H.
Vandebosch
,
K.
Poels
,
K.
Van Cleemput
,
A.
Desmet
, and
I.
De Bourdeaudhuij
, “Cyberbullying on social network sites. an experimental study into bystanders’ behavioral intentions to help the victim or reinforce the bully,”
Computers in Human Behavior
, vol.
31
, pp.
259
271
,
2014
.
16.
Finkel
,
J.R.
, &
Manning
,
C.D.
(
2009
).
Hierarchical bayesian domain adaptation
. In
Proceedings of human language technologies: The 2009 annual conference of the North American Chapter of the Association for Computational Linguistics
(pp.
602
610
).
17.
Cynthia
Van Hee
,
Els
Lefever
,
Ben
Verhoeven
,
Julie
Mennes
,
Bart
Desmet
,
Guy
De Pauw
,
Walter
Daelemans
and
Veronique
Hoste
, (
2015
), Detection and Fine-Grained Classification of Cyberbullying Events,
Proceedings of Recent Advances in Natural Language Processing
, pages
672
680
,
Hissar, Bulgaria
.
18.
K.
Raiyani
,
T.
Gonçalves
,
P.
Quaresma
, and
V. B.
Nogueira
, “
Fully connected neural network with advance preprocessor to identify aggression over Facebook and Twitter
,” in
Proc. 1st Workshop Trolling, Aggression Cyberbullying (TRAC)
,
2018
, pp.
28
41
.
19.
S.
Madisetty
and
M. S.
Desarkar
, “
Aggression detection in social media using deep neural networks
,” in
Proc. 1st Workshop Trolling, Aggression Cyberbullying (TRAC)
,
2018
, pp.
120
127
.
20.
A.
Mishra
, “
Metrics to Evaluate your Machine Learning Algorithm
,”
Medium
, May 28,
2020
. https://towardsdatascience.com/metrics-to-evaluate-your-machine-learning-algorithm-f10ba6e38234 (accessed Jul. 26, 2020)
21.
K.
Wang
,
Q.
Xiong
,
C.
Wu
,
M.
Gao
, and
Y.
Yu
, “Multi-modal cyberbullying detection on social networks,” in
2020 International Joint Conference on Neural Networks (IJCNN)
.
IEEE
,
2020
, pp.
1
8
.
22.
S. R.
Safavian
and
D.
Landgrebe
, “
A survey of decision tree classifier methodology
,”
IEEE transactions on systems, man, and cybernetics
, vol.
21
, no.
3
, pp.
660
674
,
1991
.
23.
Mohammed Ali
Al-Garadi
,
Mohammad Rashid
Hussain
,
Nawsher
Khan
,
Ghulam
Murtaza
,
Henry Friday
Nweke
,
Ihsan
Ali
,
Ghulam
Mujtaba
,
Haruna
Chiroma
,
Hasan Ali
Khattak
, and
Abdullah
Gan
, (
2019
),
Predicting Cyberbullying on Social Media in the Big Data Era Using Machine Learning Algorithms: Review of Literature and Open Challenges
, in
IEEE Access
, vol.
7
, pp.
70701
70718
, doi: .
24.
J. W.
Patchin
, “
2019 Cyberbullying Data
,”
Cyberbullying Research Center
, Jul. 09,
2019
. https://cyberbullying.org/2019 cyberbullying data (accessed Oct. 11, 2019).
25.
M.
Ptaszynski
et al., “
Sustainable cyberbullying detection with category maximized relevance of harmful phrases and double-filtered automatic optimization
,”
Int. J. Child-Comput. Interact.
, vol.
8
, pp.
15
30
, May
2016
26.
S.
Ghosh
,
A.
Ekbal
, and
P.
Bhattacharyya
, “
A multitask framework to detect depression, sentiment and multi-label emotion from suicide notes
,”
Cogn. Comput.
, vol.
14
, pp.
110
129
, Feb.
2021
.
27.
Harassment Beyond Borders; Can Victims Be Protected By Cyber Bullying In Sri Lanka?
,”
Colombo Telegraph
, Apr. 15,
2019
. https://www.colombotelegraph.com/index.php/harassment-beyond-borders-can-victims-be-protected-by-cyber-bullying-in-sri-lanka/ (accessed Apr. 17, 2020).
28.
I.
Rish
et al., “
An empirical study of the naive bayes classifier
,”
IJCAI 2001 workshop on empirical methods in artificial intelligence
, vol.
3
, no.
22
, pp.
41
46
,
2001
29.
Anisha
Datta
,
Shukrity
Si
,
Urbi
Chakraborty
,
Sudip kumar
Naskar
, (
2020
) Spyder: Aggression Detection on Multilingual Tweets,
Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying
, pages
87
92
,
Language Resources and Evaluation Conference (LREC 2020
),
Marseille
.
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