Cyberbullying detection in social media text based on character-level convolutional neural network with shortcuts
Abstract
Summary As people spend increasingly more time on social networks, cyberbullying has become a social problem that needs to be solved by machine learning methods. Our research focuses on textual cyberbullying detection because text is the most common form of social media. However, the content information in social media is short, noisy, and unstructured with incorrect spellings and symbols, and this impacts the performance of some traditional machine learning methods based on vocabulary knowledge. For this reason, we propose a Char‐CNNS (Character‐levelConvolutionalNeuralNetwork withShortcuts) model to identify whether the text in social media contains cyberbullying. We use characters as the smallest unit of learning, enabling the model to overcome spelling errors and intentional obfuscation in real‐world corpora. Shortcuts are utilized to stitch different levels of features to learn more granular bullying signals, and a focal loss function is adopted to overcome the class imbalance problem. We also provide a new ChineseWeibocomment dataset specifically for cyberbullying detection, and experiments are performed on both the Chinese Weibo dataset and the English Tweet dataset. The experimental results show that our approach is competitive with state‐of‐the‐art techniques on cyberbullying detection task.
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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 [5]
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- Authors
- Lu, N., Wu, G., Zhang, Z., Zheng, Y., Ren, Y., & Choo, K. R.
- Affiliations
- Hangzhou Dianzi University (CN)
- The University of Texas at San Antonio (US)
- Corresponding author
- Ren, Yizhi
- Venue
- Concurrency and Computation: Practice and Experience
- Publisher
- Wiley
- Published
- 2020-01-03
- Volume
- 32
- Issue
- 23
- ISSN
- 1532-0626 (ISSN-L)
- 1532-0634
- OpenAlex
- W2997975249
- Cited by
- 76
- References
- 33
- Citations by year
- 2026: 11 · 2025: 10 · 2024: 13 · 2023: 17 · 2022: 10 · 2021: 12 · 2020: 3
- FWCI
- 4.09
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Concurrency and Computation Practice and Experience (publishedVersion)
- Funders
- Natural Science Foundation of Zhejiang Province (No. LY18F020017)
- National Natural Science Foundation of China (No. 61872120)
- 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, social media text, character-level convolutional neural network, skip connections, focal loss, class imbalance, Twitter, spelling errors, text obfuscation
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