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Cited by 212Hate Speech and Cyberbullying DetectionSentiment Analysis and Opinion MiningSpam and Phishing Detection

Cyberbullying detection solutions based on deep learning architectures

Iwendi, Celestine, Srivastava, Gautam, Khan, Suleman, Maddikunta, Praveen Kumar Reddy·2023·Multimedia Systems
DOI 10.1007/s00530-020-00701-5
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Crossref · 10.1007/s00530-020-00701-5

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34 of 34 references verified0 citation errors · 0 not checkable

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  • Understanding the context of text or images increases the chance of providing better accuracy.

    “In addition, understanding the context of the text or images increases the chance of providing better accuracy [19-23].”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [21]

  • Iwendi et al. [21] found Bi-LSTM outperformed other models with an accuracy score of 82.18% on DISCo Kaggle dataset.

    “Iwendi et al. [21] used Bi-LSTM, GRU, LSTM, and RNN models for cyberbullying detection over the DISCo Kaggle dataset where Bi-LSTM outperformed other models with an accuracy score of 82.18%.”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [21]

  • Several studies have been conducted on automatic identification of cyberbullying using different independent ML techniques.

    “Several studies [21,64,115,134] have been conducted on the automatic identification of cyberbullying by using different independent ML techniques.”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [21]

  • Several deep-learning-based cyberbullying detection methods have been conducted using individual models and presenting performance with popular evaluation metrics.

    “Several deep-learning-based cyberbullying detection methods have been conducted by using individual models and presenting the performance of the models with popular evaluation metrics [21,134,149,150].”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [21]

  • A few studies exploit RNN-based techniques (LSTM, BiLSTM, RNN) while some other studies use CNN-based techniques (CNN, PCNN, Char-CNNS) to detect cyberbullying from different sources.

    “A few studies [21,134] exploit RNN-based techniques, i.e., LSTM, BiLSTM, RNN, etc., while some other studies [5,137] use CNN-based techniques (i.e., CNN, PCNN, Char-CNNS, etc.) to detect cyberbullying from different sources.”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [21]

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Authors
Iwendi, C., Srivastava, G., Khan, S., & Maddikunta, P. K. R.
Affiliations
  • Central South University of Forestry and Technology (CN)
  • China Medical University (TW)
  • Air University (PK)
  • Vellore Institute of Technology University (IN)
Corresponding author
Srivastava, Gautam
ORCID
Venue
Multimedia Systems
Publisher
Springer Science and Business Media LLC
Published
2020-10-13
Volume
29
Issue
3
Pages
1839-1852
ISSN
  • 0942-4962 (ISSN-L)
  • 1432-1882
DOI
10.1007/s00530-020-00701-5
OpenAlex
W3092739266
Cited by
205
References
33
Citations by year
2026: 22 · 2025: 38 · 2024: 49 · 2023: 43 · 2022: 33 · 2021: 20
FWCI
9.44
Type
journal article
Language
en
Open access
closed
Licence
Locations
Multimedia Systems (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
Keywords
cyberbullying detection, insults, social media text, bidirectional LSTM, GRU, LSTM, recurrent neural networks, text classification, online harassment

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