Ethical Considerations in AI-Generated Content on Social Media
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Traditional classifiers struggle with automatic feature extraction, processing large data volumes, and understanding word meanings and semantics associated with cyberbullying.
“Traditional classifiers struggle with automatic feature extraction, processing large data volumes, and understanding word meanings and semantics associated with cyberbullying [124].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [124]
Cites
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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
- Medhat, M., Ayoub, L. W., Daher, M., & Mohamed, K. M.
- Affiliations
- Ajman University (AE)
- Corresponding author
- Medhat, Mervat
- Venue
- Studies in Big Data
- Publisher
- Springer Nature Switzerland
- Published
- 2025
- Pages
- 611-620
- ISSN
- 2197-6503 (ISSN-L)
- 2197-6511
- OpenAlex
- W4409518580
- Cited by
- 5
- References
- 23
- Citations by year
- 2026: 2 · 2025: 3
- FWCI
- 16.07
- Type
- book chapter
- Language
- en
- Open access
- closed
- Licence
- Locations
- Studies in big data (publishedVersion)
- Topics
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Social Sciences › Decision Sciences › Information Systems and Management › Technology's Impact on Media
- Keywords
- AI-generated content, social media, ethics, algorithmic bias, transparency, accountability, misinformation, public trust, privacy, AI regulation, fairness
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