A Hybrid Deep Learning System of CNN and LRCN to Detect Cyberbullying from SNS Comments

Bu, Seok-Jun; Cho, Sung-Bae

doi:10.1007/978-3-319-92639-1_47

Cited by 30Hate Speech and Cyberbullying DetectionSentiment Analysis and Opinion MiningText and Document Classification Technologies

A Hybrid Deep Learning System of CNN and LRCN to Detect Cyberbullying from SNS Comments

Bu, Seok-Jun, Cho, Sung-Bae·2018·Lecture Notes in Computer Science

DOI 10.1007/978-3-319-92639-1_47

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Bu et al. [66] found their combined CNN and LRCN model obtained AUC-ROC and accuracy scores of 88.54% and 87.22% on SNS comments.
“Bu et al. [66] applied a combined CNN and LRCN model to detect cyberbullying from SNS comments where the model obtained an AUC-ROC and accuracy scores of 88.54% and 87.22%, respectively.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [66]
Several studies present performance evaluation of different hybrid deep-learning-based approaches.
“In the literature, there are several studies that also present the performance evaluation of different hybrid deep-learning-based approaches [66,115,151,152].”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [66]

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(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
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Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,369 · see citing works
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Donahue, J., Hendricks, L. A., Guadarrama, S., Rohrbach, M., Venugopalan, S., Darrell, T., & Saenko, K. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298878 · Cited by 5,228 · see citing works
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(1994) · Choice Reviews Online · doi:10.5860/choice.32-1080 · Cited by 4,755 · see citing works
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Forman, G. (2008) · doi:10.1145/1458082.1458119 · Cited by 134 · see citing works
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Zhang, Y., Gong, L., & Wang, Y. (2005) · Journal of Zhejiang University Science · doi:10.1007/bf02842477 · Cited by 84 · see citing works
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Zhang, Y., Gong, L., & Wang, Y. (2004) · Journal of Zhejiang University. Science A · doi:10.1631/jzus.2005.a0049 · Cited by 82 · see citing works
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“Bu et al. [66] applied a combined CNN and LRCN model to detect cyberbullying from SNS comments where the model obtained an AUC-ROC and accuracy scores of 88.54% and 87.22%, respectively.”
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Authors: Bu, S.-J., & Cho, S.-B.
Affiliations: Yonsei University (KR)
Corresponding author: Cho, Sung-Bae
ORCID: Cho, Sung-Bae
Venue: Lecture Notes in Computer Science
Publisher: Springer International Publishing
Published: 2018
Pages: 561-572
ISSN: 0302-9743 (ISSN-L)
1611-3349
DOI: 10.1007/978-3-319-92639-1_47
OpenAlex: W2806520865
Cited by: 30
References: 22
Citations by year: 2026: 1 · 2025: 2 · 2024: 4 · 2023: 6 · 2022: 4 · 2021: 8 · 2020: 3 · 2019: 2
FWCI: 1.32
Type: conference paper
Language: en
Open access: closed
Licence: www.springer.com/tdm (unspecified)
Locations: Lecture notes in computer science (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 › Artificial Intelligence › Text and Document Classification Technologies
Keywords: cyberbullying detection, ensemble learning, transfer learning, syntactic and semantic features

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A Hybrid Deep Learning System of CNN and LRCN to Detect Cyberbullying from SNS Comments

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