# Session-based Cyberbullying Detection in Social Media: A Survey

arXiv: https://arxiv.org/abs/2207.10639 · HTML: https://mlsanity.com/paper/2207.10639/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 129 references that are in the map.
- First posted on arXiv 14 Jul 2022.

## Builds on (most cited first, 23 of 23)

- [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)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Longformer: The Long-Document Transformer](https://mlsanity.com/paper/2004.05150/) (2020)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [BERTweet: A pre-trained language model for English Tweets](https://mlsanity.com/paper/2005.10200/) (2020)
- [Ex Machina: Personal Attacks Seen at Scale](https://mlsanity.com/paper/1610.08914/) (2016)
- [Towards generalisable hate speech detection: a review on obstacles and solutions](https://mlsanity.com/paper/2102.08886/) (2021)
- [Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms](https://mlsanity.com/paper/1801.06482/) (2018)
- [Mean Birds: Detecting Aggression and Bullying on Twitter](https://mlsanity.com/paper/1702.06877/) (2017)
- [Automatic Detection of Cyberbullying in Social Media Text](https://mlsanity.com/paper/1801.05617/) (2018)
- [Cyberbullying Detection with Fairness Constraints](https://mlsanity.com/paper/2005.06625/) (2020)
- [Improving Cyberbully Detection with User Interaction](https://mlsanity.com/paper/2011.00449/) (2020)
- [Aggressive, Repetitive, Intentional, Visible, and Imbalanced: Refining Representations for Cyberbullying Classification](https://mlsanity.com/paper/2004.01820/) (2020)
- [Current Limitations in Cyberbullying Detection: on Evaluation Criteria, Reproducibility, and Data Scarcity](https://mlsanity.com/paper/1910.11922/) (2019)
- [Unsupervised Cyberbullying Detection via Time-Informed Gaussian Mixture Model](https://mlsanity.com/paper/2008.02642/) (2020)
- [HENIN: Learning Heterogeneous Neural Interaction Networks for Explainable Cyberbullying Detection on Social Media](https://mlsanity.com/paper/2010.04576/) (2020)
- [Cyberbullying detection across social media platforms via platform-aware adversarial encoding](https://mlsanity.com/paper/2204.00334/) (2022)
- [Graph-based Features for Automatic Online Abuse Detection](https://mlsanity.com/paper/1708.01060/) (2017)
- [Towards Understanding Cyberbullying Behavior in a Semi-Anonymous Social Network](https://mlsanity.com/paper/1404.3839/) (2014)
- [Detecting state of aggression in sentences using CNN](https://mlsanity.com/paper/1604.06650/) (2016)

## Built on by (most cited first, 10 of 10)

- [SCCD: A Session-based Dataset for Chinese Cyberbullying Detection](https://mlsanity.com/paper/2501.15042/) (2025)
- [Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges](https://mlsanity.com/paper/2401.09244/) (2024)
- [A Multi-faceted Semi-Synthetic Dataset for Automated Cyberbullying Detection](https://mlsanity.com/paper/2402.10231/) (2024)
- [Hatred Stems from Ignorance! Distillation of the Persuasion Modes in Countering Conversational Hate Speech](https://mlsanity.com/paper/2403.15449/) (2024)
- [ID-XCB: Data-independent Debiasing for Fair and Accurate Transformer-based Cyberbullying Detection](https://mlsanity.com/paper/2402.16458/) (2024)
- [Detecting harassment and defamation in cyberbullying with emotion-adaptive training](https://mlsanity.com/paper/2501.16925/) (2025)
- [The Unappreciated Role of Intent in Algorithmic Moderation of Social Media Content](https://mlsanity.com/paper/2405.11030/) (2024)
- [Cyberbullying Governance on Social Media: A Unified Framework from Content Identification to Intervention](https://mlsanity.com/paper/2605.27584/) (2026)
- [Can Artificial Intelligence Support Healthcare and Mental Health Through Early Cyberbullying Detection ? The Impact of Emotion-Aware AI on Proactive Online Safety](https://mlsanity.com/paper/2609.09735/) (2026)
- [Do Social Patterns Hold in Synthetic Data? Analyzing Cyberbullying Dynamics in LLM-Generated and Authentic Dialogues](https://mlsanity.com/paper/2609.17549/) (2026)

## Abstract (arXiv)

Cyberbullying is a pervasive problem in online social media, where a bully abuses a victim through a social media session. By investigating cyberbullying perpetrated through social media sessions, recent research has looked into mining patterns and features for modeling and understanding the two defining characteristics of cyberbullying: repetitive behavior and power imbalance. In this survey paper, we define the Session-based Cyberbullying Detection framework that encapsulates the different steps and challenges of the problem. Based on this framework, we provide a comprehensive overview of session-based cyberbullying detection in social media, delving into existing efforts from a data and methodological perspective. Our review leads us to propose evidence-based criteria for a set of best practices to create session-based cyberbullying datasets. In addition, we perform benchmark experiments comparing the performance of state-of-the-art session-based cyberbullying detection models as well as large pre-trained language models across two different datasets. Through our review, we also put forth a set of open challenges as future research directions.