# Automatic Detection of Cyberbullying in Social Media Text

arXiv: https://arxiv.org/abs/1801.05617 · HTML: https://mlsanity.com/paper/1801.05617/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2018.

## Built on by (most cited first, 26 of 26)

- [Civil Rephrases Of Toxic Texts With Self-Supervised Transformers](https://mlsanity.com/paper/2102.05456/) (2021)
- [Data Expansion using Back Translation and Paraphrasing for Hate Speech Detection](https://mlsanity.com/paper/2106.04681/) (2021)
- [AOMD: An Analogy-aware Approach to Offensive Meme Detection on Social Media](https://mlsanity.com/paper/2106.11229/) (2021)
- [Session-based Cyberbullying Detection in Social Media: A Survey](https://mlsanity.com/paper/2207.10639/) (2022)
- [Hate Speech Detection in Roman Urdu](https://mlsanity.com/paper/2108.02830/) (2021)
- [Cyberbullying detection across social media platforms via platform-aware adversarial encoding](https://mlsanity.com/paper/2204.00334/) (2022)
- [Bandits for Online Calibration: An Application to Content Moderation on Social Media Platforms](https://mlsanity.com/paper/2211.06516/) (2022)
- [Sexism detection: The first corpus in Algerian dialect with a code-switching in Arabic/ French and English](https://mlsanity.com/paper/2104.01443/) (2021)
- [Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations](https://mlsanity.com/paper/2201.06384/) (2022)
- [Detection and Analysis of Offensive Online Content in Hausa Language](https://mlsanity.com/paper/2311.10541/) (2023)
- [Enriching Abusive Language Detection with Community Context](https://mlsanity.com/paper/2206.08445/) (2022)
- [SS-BERT: Mitigating Identity Terms Bias in Toxic Comment Classification by Utilising the Notion of "Subjectivity" and "Identity Terms"](https://mlsanity.com/paper/2109.02691/) (2021)
- [Analysing Cyberbullying using Natural Language Processing by Understanding Jargon in Social Media](https://mlsanity.com/paper/2107.08902/) (2021)
- [ID-XCB: Data-independent Debiasing for Fair and Accurate Transformer-based Cyberbullying Detection](https://mlsanity.com/paper/2402.16458/) (2024)
- [Identifying Cyberbullying Roles in Social Media](https://mlsanity.com/paper/2412.16417/) (2024)
- [A Multi-Task Benchmark for Abusive Language Detection in Low-Resource Settings](https://mlsanity.com/paper/2505.12116/) (2025)
- [SynBullying: A Multi LLM Synthetic Conversational Dataset for Cyberbullying Detection](https://mlsanity.com/paper/2511.11599/) (2025)
- [Social Fraud Detection Review: Methods, Challenges and Analysis](https://mlsanity.com/paper/2111.05645/) (2021)
- [IISERB Brains at SemEval 2022 Task 6: A Deep-learning Framework to Identify Intended Sarcasm in English](https://mlsanity.com/paper/2203.02244/) (2022)
- [The Unappreciated Role of Intent in Algorithmic Moderation of Social Media Content](https://mlsanity.com/paper/2405.11030/) (2024)
- [AI Enabled User-Specific Cyberbullying Severity Detection with Explainability](https://mlsanity.com/paper/2503.10650/) (2025)
- [Reinforcement Learning for Efficient Toxicity Detection in Competitive Online Video Games](https://mlsanity.com/paper/2503.20968/) (2025)
- [A Hybrid DeBERTa and Gated Broad Learning System for Cyberbullying Detection in English Text](https://mlsanity.com/paper/2506.16052/) (2025)
- [Cyberbullying Governance on Social Media: A Unified Framework from Content Identification to Intervention](https://mlsanity.com/paper/2605.27584/) (2026)
- [Do Social Patterns Hold in Synthetic Data? Analyzing Cyberbullying Dynamics in LLM-Generated and Authentic Dialogues](https://mlsanity.com/paper/2609.17549/) (2026)
- [BenCoDiC: A Large-Scale Contextual Cyberbullying Corpus for Bengali Political News Discourse FULL VERSION](https://mlsanity.com/paper/2610.13121/) (2026)

## Abstract (arXiv)

While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberbullying constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of potentially harmful messages and the information overload on the Web requires intelligent systems to identify potential risks automatically. The focus of this paper is on automatic cyberbullying detection in social media text by modelling posts written by bullies, victims, and bystanders of online bullying. We describe the collection and fine-grained annotation of a training corpus for English and Dutch and perform a series of binary classification experiments to determine the feasibility of automatic cyberbullying detection. We make use of linear support vector machines exploiting a rich feature set and investigate which information sources contribute the most for this particular task. Experiments on a holdout test set reveal promising results for the detection of cyberbullying-related posts. After optimisation of the hyperparameters, the classifier yields an F1-score of 64% and 61% for English and Dutch respectively, and considerably outperforms baseline systems based on keywords and word unigrams.