# Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms

arXiv: https://arxiv.org/abs/1801.06482 · HTML: https://mlsanity.com/paper/1801.06482/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jan 2018.

## Built on by (most cited first, 30 of 37)

- [Towards generalisable hate speech detection: a review on obstacles and solutions](https://mlsanity.com/paper/2102.08886/) (2021)
- [Large-Scale Hate Speech Detection with Cross-Domain Transfer](https://mlsanity.com/paper/2203.01111/) (2022)
- [DeepHate: Hate Speech Detection via Multi-Faceted Text Representations](https://mlsanity.com/paper/2103.11799/) (2021)
- [Deep Learning for Hate Speech Detection: A Comparative Study](https://mlsanity.com/paper/2202.09517/) (2022)
- [Model Positionality and Computational Reflexivity: Promoting Reflexivity in Data Science](https://mlsanity.com/paper/2203.07031/) (2022)
- [Meme-ingful Analysis: Enhanced Understanding of Cyberbullying in Memes Through Multimodal Explanations](https://mlsanity.com/paper/2401.09899/) (2024)
- [Session-based Cyberbullying Detection in Social Media: A Survey](https://mlsanity.com/paper/2207.10639/) (2022)
- [Generalizing Hate Speech Detection Using Multi-Task Learning: A Case Study of Political Public Figures](https://mlsanity.com/paper/2208.10598/) (2022)
- [Assessing the impact of contextual information in hate speech detection](https://mlsanity.com/paper/2210.00465/) (2022)
- [An Information Retrieval Approach to Building Datasets for Hate Speech Detection](https://mlsanity.com/paper/2106.09775/) (2021)
- [Uncovering the Limits of Text-based Emotion Detection](https://mlsanity.com/paper/2109.01900/) (2021)
- [On The Robustness of Offensive Language Classifiers](https://mlsanity.com/paper/2203.11331/) (2022)
- [Transfer Language Selection for Zero-Shot Cross-Lingual Abusive Language Detection](https://mlsanity.com/paper/2206.00962/) (2022)
- [Evaluation of Deep Learning Models for Hostility Detection in Hindi Text](https://mlsanity.com/paper/2101.04144/) (2021)
- [A Comprehensive Study on NLP Data Augmentation for Hate Speech Detection: Legacy Methods, BERT, and LLMs](https://mlsanity.com/paper/2404.00303/) (2024)
- [Unsupervised Domain Adaptation for Hate Speech Detection Using a Data Augmentation Approach](https://mlsanity.com/paper/2107.12866/) (2021)
- [BERT-Beta: A Proactive Probabilistic Approach to Text Moderation](https://mlsanity.com/paper/2109.08805/) (2021)
- [Toxicity Detection can be Sensitive to the Conversational Context](https://mlsanity.com/paper/2111.10223/) (2021)
- [Cyberbullying detection across social media platforms via platform-aware adversarial encoding](https://mlsanity.com/paper/2204.00334/) (2022)
- [Cross-lingual hate speech detection based on multilingual domain-specific word embeddings](https://mlsanity.com/paper/2104.14728/) (2021)
- [Self-Supervised Pretraining for Differentially Private Learning](https://mlsanity.com/paper/2206.07125/) (2022)
- [The Use of a Large Language Model for Cyberbullying Detection](https://mlsanity.com/paper/2402.04088/) (2024)
- [A Neighbourhood Framework for Resource-Lean Content Flagging](https://mlsanity.com/paper/2103.17055/) (2021)
- [Improving Classifier Training Efficiency for Automatic Cyberbullying Detection with Feature Density](https://mlsanity.com/paper/2111.01689/) (2021)
- [Automated Detection of Doxing on Twitter](https://mlsanity.com/paper/2202.00879/) (2022)
- [Cross-Lingual and Cross-Domain Crisis Classification for Low-Resource Scenarios](https://mlsanity.com/paper/2209.02139/) (2022)
- [Explain Thyself Bully: Sentiment Aided Cyberbullying Detection with Explanation](https://mlsanity.com/paper/2401.09023/) (2024)
- [Efficient Hate Speech Detection: Evaluating 38 Models from Traditional Methods to Transformers](https://mlsanity.com/paper/2509.14266/) (2025)
- [Rethinking and Recomputing the Value of Machine Learning Models](https://mlsanity.com/paper/2209.15157/) (2022)
- [ID-XCB: Data-independent Debiasing for Fair and Accurate Transformer-based Cyberbullying Detection](https://mlsanity.com/paper/2402.16458/) (2024)

## Abstract (arXiv)

Harassment by cyberbullies is a significant phenomenon on the social media. Existing works for cyberbullying detection have at least one of the following three bottlenecks. First, they target only one particular social media platform (SMP). Second, they address just one topic of cyberbullying. Third, they rely on carefully handcrafted features of the data. We show that deep learning based models can overcome all three bottlenecks. Knowledge learned by these models on one dataset can be transferred to other datasets. We performed extensive experiments using three real-world datasets: Formspring (12k posts), Twitter (16k posts), and Wikipedia(100k posts). Our experiments provide several useful insights about cyberbullying detection. To the best of our knowledge, this is the first work that systematically analyzes cyberbullying detection on various topics across multiple SMPs using deep learning based models and transfer learning.