Fatma Elsafoury
When the timeline meets the pipeline: A survey on automated cyberbullying detection
Elsafoury, Fatma; Katsigiannis, Stamos; Pervez, Zeeshan; Ramzan, Naeem
Authors
Dr Stamos Katsigiannis stamos.katsigiannis@durham.ac.uk
Assistant Professor
Zeeshan Pervez
Naeem Ramzan
Abstract
Web 2.0 helped user-generated platforms to spread widely. Unfortunately, it also allowed for cyberbullying to spread. Cyberbullying has negative effects that could lead to cases of depression and low self-esteem. It has become crucial to develop tools for automated cyberbullying detection. The research on developing these tools has been growing over the last decade, especially with the recent advances in machine learning and natural language processing. Given the large body of work on this topic, it is vital to critically review the literature on cyberbullying within the context of these latest advances. In this paper, we survey the automated detection of cyberbullying. Our survey sheds light on some challenges and limitations for the field. The challenges range from defining cyberbullying, data collection, and feature representation to model selection, training, and evaluation. We also provide some suggestions for improving the task of cyberbullying detection. In addition to the survey, we propose to improve the task of cyberbullying detection by addressing some of the raised limitations: 1) Using recent contextual language models like BERT for the detection of cyberbullying; 2) Using slang-based word embeddings to generate better representations of the cyberbullying-related datasets. Our results show that BERT outperforms state-of-the-art cyberbullying detection models and deep learning models. The results also show that deep learning models initialized with slang-based word embeddings outperform deep learning models initialized with traditional word embeddings.
Citation
Elsafoury, F., Katsigiannis, S., Pervez, Z., & Ramzan, N. (2021). When the timeline meets the pipeline: A survey on automated cyberbullying detection. IEEE Access, 9, 103541-103563. https://doi.org/10.1109/access.2021.3098979
Journal Article Type | Article |
---|---|
Acceptance Date | Jul 17, 2021 |
Online Publication Date | Jul 21, 2021 |
Publication Date | 2021 |
Deposit Date | Jul 23, 2021 |
Publicly Available Date | Jul 29, 2021 |
Journal | IEEE Access |
Publisher | Institute of Electrical and Electronics Engineers |
Peer Reviewed | Peer Reviewed |
Volume | 9 |
Pages | 103541-103563 |
DOI | https://doi.org/10.1109/access.2021.3098979 |
Files
Accepted Journal Article
(2.4 Mb)
PDF
Publisher Licence URL
http://creativecommons.org/licenses/by/4.0/
Copyright Statement
CCBY - IEEE is not the copyright holder of this material. Please follow the instructions via https://creativecommons.org/licenses/by/4.0/ to obtain full-text articles and stipulations in the API documentation.
You might also like
Toward Automatic Tutoring of Math Word Problems in Intelligent Tutoring Systems
(2023)
Journal Article
Multi-modal lung ultrasound image classification by fusing image-based features and probe information
(2022)
Conference Proceeding
Downloadable Citations
About Durham Research Online (DRO)
Administrator e-mail: dro.admin@durham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search