Adaptive Text Representation Learning for Cyberbullying and Abusive Language Detection
Keywords:
adaptive representation learning; cyberbullying detection; abusive language; content moderation; fairness; infrastructure governanceAbstract
Cyberbullying and abusive language detection is a complex socio-technical problem that cannot be reduced to a single classification task. This paper examines adaptive text representation learning as a systems-oriented strategy for improving detection across dynamic platforms, languages, and communicative contexts. The discussion integrates representational design, architectural trade-offs, infrastructure requirements, robustness, fairness, deployment sustainability, and policy. The paper argues that representation learning must be adaptive at multiple levels: semantic, contextual, temporal, and organizational. Drawing on recent advances in contextual language modeling and evaluation, the analysis examines how dynamic attention mechanisms and contrastive learning objectives can reshape detection pipelines. It further considers how such systems interact with moderation governance, institutional oversight, and platform accountability. The analysis highlights that gains in accuracy do not automatically translate into fair or resilient moderation. Detection systems require transparent evaluation, adversarial stress testing, bias auditing, and continuous domain adaptation. Infrastructure choices around latency, model size, annotation supply, and monitoring are equally decisive. The paper contributes a structured synthesis of existing research and a forward-looking research agenda for adaptive, accountable, and sustainable abusive language detection systems.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
