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Cited by 116Hate Speech and Cyberbullying DetectionSentiment Analysis and Opinion MiningMental Health via Writing

CyberBERT: BERT for cyberbullying identification

Paul, Sayanta, Saha, Sriparna·2022·Multimedia Systems
DOI 10.1007/s00530-020-00710-4
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Crossref · 10.1007/s00530-020-00710-4

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19 of 19 references verified0 citation errors · 0 not checkable

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  • Studies show BERT achieved high accuracy and F1 scores to classify cyberbullying in various types of online content, such as tweets and comments.

    “Studies [22,58-60] show that BERT achieved high accuracy and F1 scores to classify cyberbullying in various types of online content, such as tweets and comments.”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [22]

  • 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 [22]

  • BERT has produced outstanding results on natural language understanding tasks, and in numerous studies it outperformed conventional machine learning algorithms and demonstrated promising results in cyberbullying detection.

    “On a variety of natural language understanding tasks, Bidirectional Encoder Representation from Transformers (BERT) has produced outstanding results. In numerous studies, BERT outperformed conventional machine learning algorithms, achieved cutting-edge performance, and demonstrated promising results in the detection of cyberbullying.”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [22]

  • Paul et al. found BERT performs significantly better than conventional machine learning algorithms such as CNN, RNN + LSTM, and Bi-LSTM with attention in terms of F1 scores on Formspring, Twitter, and Wikipedia datasets.

    “BERT was applied to three real-world datasets: Formspring, Twitter, and Wikipedia by Paul et al. [22]. According to experimental findings, BERT performs significantly better than conventional machine learning algorithms such as CNN, RNN + LSTM, and Bi-LSTM, with attention in terms of F1 scores.”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [22]

  • BERT can accurately detect bullying behavior in social media messages when the cue is subtle or indirect.

    “BERT can accurately detect bullying behavior in social media messages when the cue is subtle or indirect [22,116,117].”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [22]

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Authors
Paul, S., & Saha, S.
Affiliations
Indian Institute of Technology Patna (IN)
Corresponding author
Saha, Sriparna
ORCID
Saha, Sriparna
Venue
Multimedia Systems
Publisher
Springer Science and Business Media LLC
Published
2020-11-11
Volume
28
Issue
6
Pages
1897-1904
ISSN
  • 0942-4962 (ISSN-L)
  • 1432-1882
DOI
10.1007/s00530-020-00710-4
OpenAlex
W3098614582
Cited by
116
References
19
Citations by year
2026: 15 · 2025: 22 · 2024: 23 · 2023: 34 · 2022: 14 · 2021: 8
FWCI
5.50
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
  • Social Sciences › Psychology › Social Psychology › Mental Health via Writing
Keywords
cyberbullying detection, BERT, pre-trained language models, text classification, social media posts, Twitter corpus, Wikipedia corpus, online aggression

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