# A survey of textual cyber abuse detection using cutting-edge language models and large language models

arXiv: https://arxiv.org/abs/2501.05443 · HTML: https://mlsanity.com/paper/2501.05443/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 144 references that are in the map.
- First posted on arXiv 9 Jan 2025.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [DeBERTa: Decoding-enhanced BERT with Disentangled Attention](https://mlsanity.com/paper/2006.03654/) (2020)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)](https://mlsanity.com/paper/1903.08983/) (2019)
- [HateBERT: Retraining BERT for Abusive Language Detection in English](https://mlsanity.com/paper/2010.12472/) (2020)
- [Racism is a Virus: Anti-Asian Hate and Counterspeech in Social Media during the COVID-19 Crisis](https://mlsanity.com/paper/2005.12423/) (2020)
- [Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Model](https://mlsanity.com/paper/2111.01515/) (2021)
- [CLUENER2020: Fine-grained Named Entity Recognition Dataset and Benchmark for Chinese](https://mlsanity.com/paper/2001.04351/) (2020)
- [Exposing Influence Campaigns in the Age of LLMs: A Behavioral-Based AI Approach to Detecting State-Sponsored Trolls](https://mlsanity.com/paper/2210.08786/) (2022)
- [Character-level HyperNetworks for Hate Speech Detection](https://mlsanity.com/paper/2111.06336/) (2021)
- [Why Is It Hate Speech? Masked Rationale Prediction for Explainable Hate Speech Detection](https://mlsanity.com/paper/2211.00243/) (2022)
- [Modeling Trolling in Social Media Conversations](https://mlsanity.com/paper/1612.05310/) (2016)
- [Automated Speech Generation from UN General Assembly Statements: Mapping Risks in AI Generated Texts](https://mlsanity.com/paper/1906.01946/) (2019)
- [Hate speech detection using static BERT embeddings](https://mlsanity.com/paper/2106.15537/) (2021)
- [Writer Identification Using Microblogging Texts for Social Media Forensics](https://mlsanity.com/paper/2008.01533/) (2020)
- [Automated Detection of Doxing on Twitter](https://mlsanity.com/paper/2202.00879/) (2022)
- [Impersonation on Social Media: A Deep Neural Approach to Identify Ingenuine Content](https://mlsanity.com/paper/2010.08438/) (2020)
- [Keeping it Authentic: The Social Footprint of the Trolls Network](https://mlsanity.com/paper/2409.07720/) (2024)

## Built on by (most cited first, 3 of 3)

- [Contextualizing Search Queries In-Context Learning for Conversational Rewriting with LLMs](https://mlsanity.com/paper/2502.15009/) (2025)
- [Interpretable and Robust Dialogue State Tracking via Natural Language Summarization with LLMs](https://mlsanity.com/paper/2503.08857/) (2025)
- [Large Language Models for Zero-Shot Multicultural Name Recognition](https://mlsanity.com/paper/2507.04149/) (2025)

## Abstract (arXiv)

The success of social media platforms has facilitated the emergence of various forms of online abuse within digital communities. This abuse manifests in multiple ways, including hate speech, cyberbullying, emotional abuse, grooming, and sexting. In this paper, we present a comprehensive analysis of the different forms of abuse prevalent in social media, with a particular focus on how emerging technologies, such as Language Models (LMs) and Large Language Models (LLMs), are reshaping both the detection and generation of abusive content within these networks. We delve into the mechanisms through which social media abuse is perpetuated, exploring the psychological and social impact. Additionally, we examine the dual role of advanced language models-highlighting their potential to enhance automated detection systems for abusive behavior while also acknowledging their capacity to generate harmful content. This paper aims to contribute to the ongoing discourse on online safety and ethics, offering insights into the evolving landscape of cyberabuse and the technological innovations that both mitigate and exacerbate it.