# Contextual-Lexicon Approach for Abusive Language Detection

arXiv: https://arxiv.org/abs/2104.12265 · HTML: https://mlsanity.com/paper/2104.12265/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 40 references that are in the map.
- First posted on arXiv 25 Apr 2021.

## Builds on (most cited first, 8 of 8)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Bag of Tricks for Efficient Text Classification](https://mlsanity.com/paper/1607.01759/) (2016)
- [Automated Hate Speech Detection and the Problem of Offensive Language](https://mlsanity.com/paper/1703.04009/) (2017)
- [Predicting the Type and Target of Offensive Posts in Social Media](https://mlsanity.com/paper/1902.09666/) (2019)
- [Detecting Online Hate Speech Using Context Aware Models](https://mlsanity.com/paper/1710.07395/) (2017)
- [Offensive Language Identification in Greek](https://mlsanity.com/paper/2003.07459/) (2020)
- [A Unified Deep Learning Architecture for Abuse Detection](https://mlsanity.com/paper/1802.00385/) (2018)
- [Exploring Word Embeddings for Unsupervised Textual User-Generated Content Normalization](https://mlsanity.com/paper/1704.02963/) (2017)

## Built on by (most cited first, 7 of 7)

- [Self-Explaining Hate Speech Detection with Moral Rationales](https://mlsanity.com/paper/2601.03481/) (2026)
- [SenteCon: Leveraging Lexicons to Learn Human-Interpretable Language Representations](https://mlsanity.com/paper/2305.14728/) (2023)
- [The Constant in HATE: Analyzing Toxicity in Reddit across Topics and Languages](https://mlsanity.com/paper/2404.18726/) (2024)
- [Cracking the Code: Enhancing Implicit Hate Speech Detection through Coding Classification](https://mlsanity.com/paper/2506.04693/) (2025)
- [Grounding Toxicity in Real-World Events across Languages](https://mlsanity.com/paper/2405.13754/) (2024)
- [Mitigating Biases to Embrace Diversity: A Comprehensive Annotation Benchmark for Toxic Language](https://mlsanity.com/paper/2410.13313/) (2024)
- [Hate Speech Detection in Turkish and Arabic: A Comprehensive Study](https://mlsanity.com/paper/2607.00143/) (2026)

## Abstract (arXiv)

Since a lexicon-based approach is more elegant scientifically, explaining the solution components and being easier to generalize to other applications, this paper provides a new approach for offensive language and hate speech detection on social media. Our approach embodies a lexicon of implicit and explicit offensive and swearing expressions annotated with contextual information. Due to the severity of the social media abusive comments in Brazil, and the lack of research in Portuguese, Brazilian Portuguese is the language used to validate the models. Nevertheless, our method may be applied to any other language. The conducted experiments show the effectiveness of the proposed approach, outperforming the current baseline methods for the Portuguese language.