# Cyberbullying Detection -- Technical Report 2/2018, Department of Computer Science AGH, University of Science and Technology

arXiv: https://arxiv.org/abs/1808.00926 · HTML: https://mlsanity.com/paper/1808.00926/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2018.

## Built on by (most cited first, 5 of 5)

- [Transfer Language Selection for Zero-Shot Cross-Lingual Abusive Language Detection](https://mlsanity.com/paper/2206.00962/) (2022)
- [Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density](https://mlsanity.com/paper/2111.01689/) (2021)
- [Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges](https://mlsanity.com/paper/2401.09244/) (2024)
- [Deep Learning Approaches for Detecting Adversarial Cyberbullying and Hate Speech in Social Networks](https://mlsanity.com/paper/2406.17793/) (2024)
- [Exploring the Potential of Feature Density in Estimating Machine Learning Classifier Performance with Application to Cyberbullying Detection](https://mlsanity.com/paper/2206.01949/) (2022)

## Abstract (arXiv)

The research described in this paper concerns automatic cyberbullying detection in social media. There are two goals to achieve: building a gold standard cyberbullying detection dataset and measuring the performance of the Samurai cyberbullying detection system. The Formspring dataset provided in a Kaggle competition was re-annotated as a part of the research. The annotation procedure is described in detail and, unlike many other recent data annotation initiatives, does not use Mechanical Turk for finding people willing to perform the annotation. The new annotation compared to the old one seems to be more coherent since all tested cyberbullying detection system performed better on the former. The performance of the Samurai system is compared with 5 commercial systems and one well-known machine learning algorithm, used for classifying textual content, namely Fasttext. It turns out that Samurai scores the best in all measures (accuracy, precision and recall), while Fasttext is the second-best performing algorithm.