# Deep Learning Approaches for Detecting Adversarial Cyberbullying and Hate Speech in Social Networks

arXiv: https://arxiv.org/abs/2406.17793 · HTML: https://mlsanity.com/paper/2406.17793/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 62 references that are in the map.
- First posted on arXiv 30 May 2024.

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

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [Practical Black-Box Attacks against Machine Learning](https://mlsanity.com/paper/1602.02697/) (2016)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection](https://mlsanity.com/paper/2203.09509/) (2022)
- [TextBugger: Generating Adversarial Text Against Real-world Applications](https://mlsanity.com/paper/1812.05271/) (2018)
- [Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers](https://mlsanity.com/paper/1801.04354/) (2018)
- [Synthetic and Natural Noise Both Break Neural Machine Translation](https://mlsanity.com/paper/1711.02173/) (2017)
- [Ex Machina: Personal Attacks Seen at Scale](https://mlsanity.com/paper/1610.08914/) (2016)
- [Crafting Adversarial Input Sequences for Recurrent Neural Networks](https://mlsanity.com/paper/1604.08275/) (2016)
- [Deceiving Google's Perspective API Built for Detecting Toxic Comments](https://mlsanity.com/paper/1702.08138/) (2017)
- [A BERT-Based Transfer Learning Approach for Hate Speech Detection in Online Social Media](https://mlsanity.com/paper/1910.12574/) (2019)
- [DolphinAtack: Inaudible Voice Commands](https://mlsanity.com/paper/1708.09537/) (2017)
- [Demoting Racial Bias in Hate Speech Detection](https://mlsanity.com/paper/2005.12246/) (2020)
- [All You Need is "Love": Evading Hate-speech Detection](https://mlsanity.com/paper/1808.09115/) (2018)
- [Towards Crafting Text Adversarial Samples](https://mlsanity.com/paper/1707.02812/) (2017)
- [HABERTOR: An Efficient and Effective Deep Hatespeech Detector](https://mlsanity.com/paper/2010.08865/) (2020)
- [Cyberbullying Detection -- Technical Report 2/2018, Department of Computer Science AGH, University of Science and Technology](https://mlsanity.com/paper/1808.00926/) (2018)
- [LiteLSTM Architecture for Deep Recurrent Neural Networks](https://mlsanity.com/paper/2201.11624/) (2022)
- [Adversarial Text Normalization](https://mlsanity.com/paper/2206.04137/) (2022)
- [Adversarial Attacks and Defenses for Social Network Text Processing Applications: Techniques, Challenges and Future Research Directions](https://mlsanity.com/paper/2110.13980/) (2021)

## Built on by (most cited first, 2 of 2)

- [EVADE-Bench: Multimodal Benchmark for Evaluating and Enhancing Evasive Content Detection](https://mlsanity.com/paper/2505.17654/) (2025)
- [Adversarially Robust Detection of Harmful Online Content: A Computational Design Science Approach](https://mlsanity.com/paper/2512.17367/) (2025)

## Abstract (arXiv)

Cyberbullying is a significant concern intricately linked to technology that can find resolution through technological means. Despite its prevalence, technology also provides solutions to mitigate cyberbullying. To address growing concerns regarding the adverse impact of cyberbullying on individuals' online experiences, various online platforms and researchers are actively adopting measures to enhance the safety of digital environments. While researchers persist in crafting detection models to counteract or minimize cyberbullying, malicious actors are deploying adversarial techniques to circumvent these detection methods. This paper focuses on detecting cyberbullying in adversarial attack content within social networking site text data, specifically emphasizing hate speech. Utilizing a deep learning-based approach with a correction algorithm, this paper yielded significant results. An LSTM model with a fixed epoch of 100 demonstrated remarkable performance, achieving high accuracy, precision, recall, F1-score, and AUC-ROC scores of 87.57%, 88.73%, 87.57%, 88.15%, and 91% respectively. Additionally, the LSTM model's performance surpassed that of previous studies.