Cyberbullying Detection With Fairness Constraints
Open accessAbstract
Cyberbullying is a widespread adverse phenomenon among online social interactions in today's digital society. While numerous computational studies focus on enhancing the cyberbullying detection performance of machine learning algorithms, proposed models tend to carry and reinforce unintended social biases. In this study, we try to answer the research question of “Can we mitigate the unintended bias of cyberbullying detection models by guiding the model training with fairness constraints?” For this purpose, we propose a model training scheme that can employ fairness constraints and validate our approach with different datasets. We demonstrate that various types of unintended biases can be successfully mitigated without impairing the model quality. We believe our work contributes to the pursuit of unbiased, transparent, and ethical machine learning solutions for cyber-social health.
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Twitter offers real-time capabilities, allowing researchers to analyze trends and patterns in cyberbullying characteristics as they occur.
“Additionally, Twitter offers real-time capabilities, allowing researchers to analyze trends and patterns in cyberbullying characteristics as they occur.”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [130]
Integrating textual, contextual, emotional, social attributes, and sentiment characteristics with conventional text features can elevate cyberbullying detection efficiency.
“Integrating textual, contextual, emotional, social attributes, and sentiment characteristics together with conventional text features can elevate cyberbullying”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [130]
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“Although traditional NLP techniques have become highly successful in detecting social media cyberbullying, there are still some challenges that need to be addressed: the short text allowed by social media platforms, imbalance between aggressive and nonaggressive comments, natural language ambiguity, and excessive use…”
Background
- Authors
- Gencoglu, O.
- Affiliations
- Tampere University (FI)
- Corresponding author
- Gencoglu, Oguzhan
- ORCID
- Gencoglu, Oguzhan
- Venue
- IEEE Internet Computing
- Published
- 2021-01-01
- Volume
- 25
- Issue
- 1
- Pages
- 20-29
- ISSN
- 1089-7801 (ISSN-L)
- 1941-0131
- OpenAlex
- W3026016891
- Cited by
- 53
- References
- 14
- Citations by year
- 2026: 1 · 2025: 8 · 2024: 8 · 2023: 9 · 2022: 7 · 2021: 17 · 2020: 3
- FWCI
- 3.51
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- IEEE Internet Computing (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Keywords
- cyberbullying detection, fairness constraints, algorithmic bias, online social interaction, model training
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