Detection of Cyberbullying Using Deep Neural Network

Banerjee, Vijay; Telavane, Jui; Gaikwad, Pooja; Vartak, Pallavi

doi:10.1109/icaccs.2019.8728378

Cited by 98Hate Speech and Cyberbullying DetectionSpam and Phishing DetectionMisinformation and Its Impacts

Detection of Cyberbullying Using Deep Neural Network

Banerjee, Vijay, Telavane, Jui, Gaikwad, Pooja, Vartak, Pallavi·2019·2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)

DOI 10.1109/icaccs.2019.8728378

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Abstract

Innovation is developing quickly today. This headways in innovation has changed how individuals cooperate in an expansive way giving communication another dimension. But despite the fact that innovation encourages us in numerous parts of life, it accompanies different effects that influence people in a few or the other way. Cyberbullying is one of such effects. Cyberbullying is a wrongdoing in which a culprit focuses on an individual with online provocation and loathe which has antagonistic emotional, social and physical effects on the victim. So as to address such issue we proposed a novel cyberbullying detection method dependent on deep neural network. Convolution Neural Network is utilized for the better outcomes when contrasted with the current systems.

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When the data size is large, deep learning algorithms outperform traditional machine learning algorithms.
“When the data size is large, several studies [11-14] have shown that DL algorithms outperform the traditional ML algorithms.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [14]
Several studies used TensorFlow in their cyberbullying detection experiments.
“We find that several studies used TensorFlow [14,129,182,183] in their experiments, because the framework is flexible, easy to use and suits natural language processing tasks.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [14]

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Authors: Banerjee, V., Telavane, J., Gaikwad, P., & Vartak, P.
ORCID: Gaikwad, Pooja
Venue: 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS)
Publisher: IEEE
Published: 2019-03
Pages: 604-607
DOI: 10.1109/icaccs.2019.8728378
OpenAlex: W2949094806
Cited by: 98
References: 10
Citations by year: 2026: 1 · 2025: 13 · 2024: 23 · 2023: 30 · 2022: 13 · 2021: 11 · 2020: 6 · 2019: 1
FWCI: 2.90
Type: conference paper
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: International Conference on Advanced Computing and Communication Systems (ICACCS) (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
Keywords: cyberbullying detection, online harassment, convolutional neural networks, deep neural network, text classification, social media

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Detection of Cyberbullying Using Deep Neural Network

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