# One-step and Two-step Classification for Abusive Language Detection on Twitter

arXiv: https://arxiv.org/abs/1706.01206 · HTML: https://mlsanity.com/paper/1706.01206/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2017.

## Built on by (most cited first, 30 of 46)

- [Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Models](https://mlsanity.com/paper/2304.03738/) (2023)
- [Disentangling Hate in Online Memes](https://mlsanity.com/paper/2108.06207/) (2021)
- [DeepHate: Hate Speech Detection via Multi-Faceted Text Representations](https://mlsanity.com/paper/2103.11799/) (2021)
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- ["Subverting the Jewtocracy": Online Antisemitism Detection Using Multimodal Deep Learning](https://mlsanity.com/paper/2104.05947/) (2021)
- [AngryBERT: Joint Learning Target and Emotion for Hate Speech Detection](https://mlsanity.com/paper/2103.11800/) (2021)
- [Abuse is Contextual, What about NLP? The Role of Context in Abusive Language Annotation and Detection](https://mlsanity.com/paper/2103.14916/) (2021)
- [Emotion Based Hate Speech Detection using Multimodal Learning](https://mlsanity.com/paper/2202.06218/) (2022)
- [Deep Learning for Hate Speech Detection: A Comparative Study](https://mlsanity.com/paper/2202.09517/) (2022)
- [Model-Agnostic Meta-Learning for Multilingual Hate Speech Detection](https://mlsanity.com/paper/2303.02513/) (2023)
- [Interpretable Multi-Modal Hate Speech Detection](https://mlsanity.com/paper/2103.01616/) (2021)
- [Assessing the impact of contextual information in hate speech detection](https://mlsanity.com/paper/2210.00465/) (2022)
- [Toxicity Detection is NOT all you Need: Measuring the Gaps to Supporting Volunteer Content Moderators](https://mlsanity.com/paper/2311.07879/) (2023)
- [Predicting Hate Intensity of Twitter Conversation Threads](https://mlsanity.com/paper/2206.08406/) (2022)
- [Reliable Decision from Multiple Subtasks through Threshold Optimization: Content Moderation in the Wild](https://mlsanity.com/paper/2208.07522/) (2022)
- [NLRG at SemEval-2021 Task 5: Toxic Spans Detection Leveraging BERT-based Token Classification and Span Prediction Techniques](https://mlsanity.com/paper/2102.12254/) (2021)
- [Toxicity Detection can be Sensitive to the Conversational Context](https://mlsanity.com/paper/2111.10223/) (2021)
- [User-Aware Multilingual Abusive Content Detection in Social Media](https://mlsanity.com/paper/2410.21321/) (2024)
- [Sexism detection: The first corpus in Algerian dialect with a code-switching in Arabic/ French and English](https://mlsanity.com/paper/2104.01443/) (2021)
- [Abusive Language Detection in Heterogeneous Contexts: Dataset Collection and the Role of Supervised Attention](https://mlsanity.com/paper/2105.11119/) (2021)
- [ToxBuster: In-game Chat Toxicity Buster with BERT](https://mlsanity.com/paper/2305.12542/) (2023)
- [Towards Detecting Contextual Real-Time Toxicity for In-Game Chat](https://mlsanity.com/paper/2310.18330/) (2023)
- [Enhancing LLM-based Hatred and Toxicity Detection with Meta-Toxic Knowledge Graph](https://mlsanity.com/paper/2412.15268/) (2024)
- [LLM-C3MOD: A Human-LLM Collaborative System for Cross-Cultural Hate Speech Moderation](https://mlsanity.com/paper/2503.07237/) (2025)
- [Modeling Users and Online Communities for Abuse Detection: A Position on Ethics and Explainability](https://mlsanity.com/paper/2103.17191/) (2021)
- [Cross-lingual hate speech detection based on multilingual domain-specific word embeddings](https://mlsanity.com/paper/2104.14728/) (2021)
- [Ceasing hate withMoH: Hate Speech Detection in Hindi-English Code-Switched Language](https://mlsanity.com/paper/2110.09393/) (2021)
- [Selecting and combining complementary feature representations and classifiers for hate speech detection](https://mlsanity.com/paper/2201.06721/) (2022)
- [An Investigation Into Explainable Audio Hate Speech Detection](https://mlsanity.com/paper/2408.06065/) (2024)
- [IIITG-ADBU@HASOC-Dravidian-CodeMix-FIRE2020: Offensive Content Detection in Code-Mixed Dravidian Text](https://mlsanity.com/paper/2107.14336/) (2021)

## Abstract (arXiv)

Automatic abusive language detection is a difficult but important task for online social media. Our research explores a two-step approach of performing classification on abusive language and then classifying into specific types and compares it with one-step approach of doing one multi-class classification for detecting sexist and racist languages. With a public English Twitter corpus of 20 thousand tweets in the type of sexism and racism, our approach shows a promising performance of 0.827 F-measure by using HybridCNN in one-step and 0.824 F-measure by using logistic regression in two-steps.