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Cited by 21Hate Speech and Cyberbullying DetectionSentiment Analysis and Opinion MiningText and Document Classification Technologies

Identification and Classification of Cyberbullying Posts: A Recurrent Neural Network Approach Using Under-Sampling and Class Weighting

Open access
Agarwal, Ayush, Chivukula, Aneesh Sreevallabh, Bhuyan, Monowar H., Jan, Tony, Narayan, Bhuva, Prasad, Mukesh·2020·Communications in Computer and Information Science
DOI 10.1007/978-3-030-63823-8_14
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Crossref · 10.1007/978-3-030-63823-8_14

Cites

19 of 19 references verified0 citation errors · 0 not checkable

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  • 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 [134]

  • 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 [134]

  • 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 [134]

  • Agarwal et al. [134] found their Bi-LSTM with attention layers method outperformed existing works with precision, recall, and F1 scores of 89%, 86%, and 88% on Wikipedia dataset.

    “Experimental results show that their proposed method outperformed the existing works in terms of precision, recall, and F1 score of 89%, 86%, and 88%, respectively.”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [134]

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Authors
Agarwal, A., Chivukula, A. S., Bhuyan, M. H., Jan, T., Narayan, B., & Prasad, M.
Affiliations
  • Delhi Technological University (IN)
  • University of Technology Sydney (AU)
  • Umeå University (SE)
  • Melbourne Institute of Technology (AU)
Corresponding author
Prasad, Mukesh
ORCID
Venue
Communications in Computer and Information Science
Publisher
Springer International Publishing
Published
2020
Pages
113-120
ISSN
  • 1865-0929 (ISSN-L)
  • 1865-0937
DOI
10.1007/978-3-030-63823-8_14
OpenAlex
W3105050934
Cited by
21
References
24
Citations by year
2026: 1 · 2025: 1 · 2024: 6 · 2023: 7 · 2022: 5 · 2021: 1
FWCI
1.24
Type
conference paper
Language
en
Open access
Licence
Locations
  • Communications in computer and information science (publishedVersion)
  • UTS ePRESS (University of Technology Sydney) (submittedVersion)
  • UTS ePRESS (University of Technology Sydney) (submittedVersion, other-oa)
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 › Artificial Intelligence › Text and Document Classification Technologies
Keywords
cyberbullying detection, social media abuse, class imbalance, Tomek links, undersampling, class weighting, bidirectional LSTM, attention mechanism, Wikipedia

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