# Explain Thyself Bully: Sentiment Aided Cyberbullying Detection with Explanation

arXiv: https://arxiv.org/abs/2401.09023 · HTML: https://mlsanity.com/paper/2401.09023/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 47 references that are in the map.
- First posted on arXiv 17 Jan 2024.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [ERASER: A Benchmark to Evaluate Rationalized NLP Models](https://mlsanity.com/paper/1911.03429/) (2019)
- [e-SNLI: Natural Language Inference with Natural Language Explanations](https://mlsanity.com/paper/1812.01193/) (2018)
- [HateXplain: A Benchmark Dataset for Explainable Hate Speech Detection](https://mlsanity.com/paper/2012.10289/) (2020)
- [Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://mlsanity.com/paper/1906.02361/) (2019)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [Comparative Study of CNN and RNN for Natural Language Processing](https://mlsanity.com/paper/1702.01923/) (2017)
- [Detecting Harmful Memes and Their Targets](https://mlsanity.com/paper/2110.00413/) (2021)
- [Aggression-annotated Corpus of Hindi-English Code-mixed Data](https://mlsanity.com/paper/1803.09402/) (2018)
- [Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms](https://mlsanity.com/paper/1801.06482/) (2018)
- [DeepHateExplainer: Explainable Hate Speech Detection in Under-resourced Bengali Language](https://mlsanity.com/paper/2012.14353/) (2020)
- [Hate Speech Detection from Code-mixed Hindi-English Tweets Using Deep Learning Models](https://mlsanity.com/paper/1811.05145/) (2018)

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

- [Cyberbullying Detection via Aggression-Enhanced Prompting](https://mlsanity.com/paper/2508.06360/) (2025)
- [Cyberbullying Governance on Social Media: A Unified Framework from Content Identification to Intervention](https://mlsanity.com/paper/2605.27584/) (2026)
- [Conditional Reliability of Toxicity Signals for Multilingual and Code-Mixed Abuse Detection](https://mlsanity.com/paper/2607.15861/) (2026)

## Abstract (arXiv)

Cyberbullying has become a big issue with the popularity of different social media networks and online communication apps. While plenty of research is going on to develop better models for cyberbullying detection in monolingual language, there is very little research on the code-mixed languages and explainability aspect of cyberbullying. Recent laws like "right to explanations" of General Data Protection Regulation, have spurred research in developing interpretable models rather than focusing on performance. Motivated by this we develop the first interpretable multi-task model called em mExCB for automatic cyberbullying detection from code-mixed languages which can simultaneously solve several tasks, cyberbullying detection, explanation/rationale identification, target group detection and sentiment analysis. We have introduced em BullyExplain, the first benchmark dataset for explainable cyberbullying detection in code-mixed language. Each post in em BullyExplain dataset is annotated with four labels, i.e., em bully label, sentiment label, target and rationales (explainability), i.e., which phrases are being responsible for annotating the post as a bully. The proposed multitask framework (mExCB) based on CNN and GRU with word and sub-sentence (SS) level attention is able to outperform several baselines and state of the art models when applied on em BullyExplain dataset.