Cyberbullying Detection in Social Networks Using Bi-GRU with Self-Attention Mechanism
With the propagation of cyberbullying in social networks as a trending subject, cyberbullying detection has become a social problem that researchers are concerned about. Developing intelligent models and systems helps detect cyberbullying automatically. This work focuses on text-based cyberbullying...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-04-01
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| Series: | Information |
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| Online Access: | https://www.mdpi.com/2078-2489/12/4/171 |
| _version_ | 1797537554203934720 |
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| author | Yong Fang Shaoshuai Yang Bin Zhao Cheng Huang |
| author_facet | Yong Fang Shaoshuai Yang Bin Zhao Cheng Huang |
| author_sort | Yong Fang |
| collection | DOAJ |
| description | With the propagation of cyberbullying in social networks as a trending subject, cyberbullying detection has become a social problem that researchers are concerned about. Developing intelligent models and systems helps detect cyberbullying automatically. This work focuses on text-based cyberbullying detection because it is the commonly used information carrier in social networks and is the widely used feature in this regard studies. Motivated by the documented success of neural networks, we propose a complete model combining the bidirectional gated recurrent unit (Bi-GRU) and the self-attention mechanism. In detail, we introduce the design of a GRU cell and Bi-GRU’s advantage for learning the underlying relationships between words from both directions. Besides, we present the design of the self-attention mechanism and the benefit of this joining for achieving a greater performance of cyberbullying classification tasks. The proposed model could address the limitation of the vanishing and exploding gradient problems. We avoid using oversampling or downsampling on experimental data which could result in the overestimation of evaluation. We conduct a comparative assessment on two commonly used datasets, and the results show that our proposed method outperformed baselines in all evaluation metrics. |
| first_indexed | 2024-03-10T12:16:51Z |
| format | Article |
| id | doaj.art-df193e466a944a4fa7d9c8dbff3d33bd |
| institution | Directory Open Access Journal |
| issn | 2078-2489 |
| language | English |
| last_indexed | 2024-03-10T12:16:51Z |
| publishDate | 2021-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj.art-df193e466a944a4fa7d9c8dbff3d33bd2023-11-21T15:49:28ZengMDPI AGInformation2078-24892021-04-0112417110.3390/info12040171Cyberbullying Detection in Social Networks Using Bi-GRU with Self-Attention MechanismYong Fang0Shaoshuai Yang1Bin Zhao2Cheng Huang3College of Cybersecurity, Sichuan University, Chengdu 610065, ChinaCollege of Cybersecurity, Sichuan University, Chengdu 610065, ChinaCETC Avionics Co., Ltd., Chengdu 611731, ChinaCollege of Cybersecurity, Sichuan University, Chengdu 610065, ChinaWith the propagation of cyberbullying in social networks as a trending subject, cyberbullying detection has become a social problem that researchers are concerned about. Developing intelligent models and systems helps detect cyberbullying automatically. This work focuses on text-based cyberbullying detection because it is the commonly used information carrier in social networks and is the widely used feature in this regard studies. Motivated by the documented success of neural networks, we propose a complete model combining the bidirectional gated recurrent unit (Bi-GRU) and the self-attention mechanism. In detail, we introduce the design of a GRU cell and Bi-GRU’s advantage for learning the underlying relationships between words from both directions. Besides, we present the design of the self-attention mechanism and the benefit of this joining for achieving a greater performance of cyberbullying classification tasks. The proposed model could address the limitation of the vanishing and exploding gradient problems. We avoid using oversampling or downsampling on experimental data which could result in the overestimation of evaluation. We conduct a comparative assessment on two commonly used datasets, and the results show that our proposed method outperformed baselines in all evaluation metrics.https://www.mdpi.com/2078-2489/12/4/171cyberbullying detectionsocial networkneural networksbidirectional gated recurrent unitself-attention mechanism |
| spellingShingle | Yong Fang Shaoshuai Yang Bin Zhao Cheng Huang Cyberbullying Detection in Social Networks Using Bi-GRU with Self-Attention Mechanism Information cyberbullying detection social network neural networks bidirectional gated recurrent unit self-attention mechanism |
| title | Cyberbullying Detection in Social Networks Using Bi-GRU with Self-Attention Mechanism |
| title_full | Cyberbullying Detection in Social Networks Using Bi-GRU with Self-Attention Mechanism |
| title_fullStr | Cyberbullying Detection in Social Networks Using Bi-GRU with Self-Attention Mechanism |
| title_full_unstemmed | Cyberbullying Detection in Social Networks Using Bi-GRU with Self-Attention Mechanism |
| title_short | Cyberbullying Detection in Social Networks Using Bi-GRU with Self-Attention Mechanism |
| title_sort | cyberbullying detection in social networks using bi gru with self attention mechanism |
| topic | cyberbullying detection social network neural networks bidirectional gated recurrent unit self-attention mechanism |
| url | https://www.mdpi.com/2078-2489/12/4/171 |
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