Cyberbullying Detection Based on Emotion
Open accessAbstract
Due to the detrimental consequences caused by cyberbullying, a great deal of research has been undertaken to propose effective techniques to resolve this reoccurring problem. The research presented in this paper is motivated by the fact that negative emotions can be caused by cyberbullying. This paper proposes cyberbullying detection models that are trained based on contextual, emotions and sentiment features. An Emotion Detection Model (EDM) was constructed using Twitter datasets that have been improved in terms of its annotations. Emotions and sentiment were extracted from cyberbullying datasets using EDM and lexicons based. Two cyberbullying datasets from Wikipedia and Twitter respectively were further improved by comprehensive annotation of emotion and sentiment features. The results show that anger, fear and guilt were the major emotions associated with cyberbullying. Subsequently, the extracted emotions were used as features in addition to contextual and sentiment features to train models for cyberbullying detection. The results demonstrate that using emotion features and sentiment has improved the performance of detecting cyberbullying by 0.5 to 0.6 recall. The proposed models also outperformed the state-of-the-art models by a 0.7 f1-score. The main contribution of this work is two-fold, which includes a comprehensive emotion-annotated dataset for cyberbullying detection, and an empirical proof of emotions as effective features for cyberbullying detection.
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Sparse representations store only relevant values and their indices, enhancing efficiency.
“Sparse representations store only relevant values and their indices, enhancing efficiency [121].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [121]
Automated methods like Self-Supervised Learning (SSL) models have been suggested for labeling data and enriching training sets using unlabeled data.
“In response to this challenge, automated methods like Self-Supervised Learning (SSL) models have been suggested for labeling data and enriching training sets using unlabeled data [121].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [121]
Models trained with a small volume of data had performance less than 0.5 because they did not have enough samples of either cyberbullying or non-cyberbullying instances to learn strong patterns.
“The results of the models showed that they had uninterested performance, less than a performance ratio of 0.5, since they were trained with a small volume of data and did not have enough samples of either cyberbullying or non-cyberbullying instances to learn strong patterns [59,121].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [121]
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Cited by
Sorted by citations
Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms
Philipo, A. G., Sarwatt, D. S., Ding, J., Daneshmand, M., & Ning, H. (2026) · ACM Computing Surveys · doi:10.1145/3785654 · Cited by 14 · see citing works
- Authors
- Al-Hashedi, M., Soon, L.-K., Goh, H.-N., Lim, A. H. L., & Siew, E.-G.
- Affiliations
- Multimedia University (MY)
- Monash University Malaysia (MY)
- Venue
- IEEE Access
- Published
- 2023
- Volume
- 11
- Pages
- 53907-53918
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4378713756
- Cited by
- 45
- References
- 55
- Citations by year
- 2026: 15 · 2025: 18 · 2024: 9 · 2023: 3
- FWCI
- 3.98
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Access (publishedVersion, cc-by-nc-nd)
- Siti Hasmah Digital Library-MMU Institutiona Repository (Multimedia University) (acceptedVersion)
- Monash University Research Portal (Monash University) (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Fundamental Research Grant Scheme, Ministry of Education, Malaysia (FRGS/1/2017/ICT02/MMU/02/6)
- 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, emotion recognition, sentiment analysis, Twitter, Wikipedia, anger, fear, guilt, contextual features
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