Artificial Intelligence-Enabled Cyberbullying-Free Online Social Networks in Smart Cities
Open accessAbstract
In recent years, smart city services have moved the existence of people from the physical to the virtual world (cyberspace), e.g., online banking, e-commerce, telemedicine, etc. Along with the benefits of smart cities, the problems of the physical world are also moved to the cyber world, like cyberbullying in online social networks (OSN). Automated cyberbullying detection techniques need to be designed to remove the potential tragedies in OSNs. The recent advent of artificial intelligence (AI) models like machine learning and deep learning (DL) models can be employed for the detection of cyberbullying in the OSN. With this motivation, this paper develops an AI-enabled cyberbullying-free OSN (AICBF-ONS) technique in smart cities. The proposed AICBF-ONS technique involves chaotic salp swarm optimization (CSSO)-based feature selection technique to derive a useful set of features from the OSN data. In addition, stacked autoencoder model is used as a classification model to allocate appropriate class labels of the OSN data. To improve the detection performance of the SAE model, a parameter tuning process take place using the mayfly optimization (MFO) algorithm. An extensive experimental analysis ensured the supremacy of the proposed AICBF-ONS technique.
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Machine learning models require explicit feature engineering, while deep learning models can learn meaningful representations directly from raw data.
“Machine learning models require explicit feature engineering, while deep learning models can learn meaningful representations directly from raw data [90].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [90]
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“In another paper [15], an AI-driven technique was developed for a cyberbullying-free Online social network (OSN), with a Chaotic salp swarm optimization (CSSO) method used to select key features from OSN data.”
Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms
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- Authors
- Al-Marghilani, A.
- Affiliations
- Northern Border University (SA)
- Corresponding author
- Al-Marghilani, Abdulsamad
- Venue
- International Journal of Computational Intelligence Systems
- Published
- 2022-01-24
- Volume
- 15
- Issue
- 1
- ISSN
- 1875-6883 (ISSN-L)
- OpenAlex
- W4207015726
- Cited by
- 38
- References
- 25
- Citations by year
- 2026: 6 · 2025: 9 · 2024: 14 · 2023: 5 · 2022: 4
- FWCI
- 2.63
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- International Journal of Computational Intelligence Systems (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Text and Document Classification Technologies
- Keywords
- cyberbullying detection, social networking sites, smart cities, salp swarm algorithm, feature selection, stacked autoencoder, mayfly optimization, metaheuristic parameter tuning, deep learning classification
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