# Cyberbullying Detection via Aggression-Enhanced Prompting

arXiv: https://arxiv.org/abs/2508.06360 · HTML: https://mlsanity.com/paper/2508.06360/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 34 references that are in the map.
- First posted on arXiv 8 Aug 2025.

## Builds on (most cited first, 13 of 13)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [Large Language Models Are Human-Level Prompt Engineers](https://mlsanity.com/paper/2211.01910/) (2022)
- [Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference](https://mlsanity.com/paper/2001.07676/) (2020)
- [SemEval-2020 Task 12: Multilingual Offensive Language Identification in Social Media (OffensEval 2020)](https://mlsanity.com/paper/2006.07235/) (2020)
- [A Benchmark Dataset for Learning to Intervene in Online Hate Speech](https://mlsanity.com/paper/1909.04251/) (2019)
- [Understanding Abuse: A Typology of Abusive Language Detection Subtasks](https://mlsanity.com/paper/1705.09899/) (2017)
- [Cyberbullying detection across social media platforms via platform-aware adversarial encoding](https://mlsanity.com/paper/2204.00334/) (2022)
- [A Text-to-Text Model for Multilingual Offensive Language Identification](https://mlsanity.com/paper/2312.03379/) (2023)
- [Explain Thyself Bully: Sentiment Aided Cyberbullying Detection with Explanation](https://mlsanity.com/paper/2401.09023/) (2024)
- [Moderating Harm: Benchmarking Large Language Models for Cyberbullying Detection in YouTube Comments](https://mlsanity.com/paper/2505.18927/) (2025)
- [Assessing Text Classification Methods for Cyberbullying Detection on Social Media Platforms](https://mlsanity.com/paper/2412.19928/) (2024)
- [Detecting harassment and defamation in cyberbullying with emotion-adaptive training](https://mlsanity.com/paper/2501.16925/) (2025)

## Built on by (most cited first, 1 of 1)

- [Cyberbullying Governance on Social Media: A Unified Framework from Content Identification to Intervention](https://mlsanity.com/paper/2605.27584/) (2026)

## Abstract (arXiv)

Detecting cyberbullying on social media remains a critical challenge due to its subtle and varied expressions. This study investigates whether integrating aggression detection as an auxiliary task within a unified training framework can enhance the generalisation and performance of large language models (LLMs) in cyberbullying detection. Experiments are conducted on five aggression datasets and one cyberbullying dataset using instruction-tuned LLMs. We evaluated multiple strategies: zero-shot, few-shot, independent LoRA fine-tuning, and multi-task learning (MTL). Given the inconsistent results of MTL, we propose an enriched prompt pipeline approach in which aggression predictions are embedded into cyberbullying detection prompts to provide contextual augmentation. Preliminary results show that the enriched prompt pipeline consistently outperforms standard LoRA fine-tuning, indicating that aggression-informed context significantly boosts cyberbullying detection. This study highlights the potential of auxiliary tasks, such as aggression detection, to improve the generalisation of LLMs for safety-critical applications on social networks.