# SOLID: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification

arXiv: https://arxiv.org/abs/2004.14454 · HTML: https://mlsanity.com/paper/2004.14454/

## Key facts

- Cited by 53 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2020.

## Built on by (most cited first, 30 of 53)

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- [Predicting the Type and Target of Offensive Social Media Posts in Marathi](https://mlsanity.com/paper/2211.12570/) (2022)
- [OffMix-3L: A Novel Code-Mixed Dataset in Bangla-English-Hindi for Offensive Language Identification](https://mlsanity.com/paper/2310.18387/) (2023)
- [Guardians of Discourse: Evaluating LLMs on Multilingual Offensive Language Detection](https://mlsanity.com/paper/2410.15623/) (2024)
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- [Muted: Multilingual Targeted Offensive Speech Identification and Visualization](https://mlsanity.com/paper/2312.11344/) (2023)

## Abstract (arXiv)

The widespread use of offensive content in social media has led to an abundance of research in detecting language such as hate speech, cyberbullying, and cyber-aggression. Recent work presented the OLID dataset, which follows a taxonomy for offensive language identification that provides meaningful information for understanding the type and the target of offensive messages. However, it is limited in size and it might be biased towards offensive language as it was collected using keywords. In this work, we present SOLID, an expanded dataset, where the tweets were collected in a more principled manner. SOLID contains over nine million English tweets labeled in a semi-supervised fashion. We demonstrate that using SOLID along with OLID yields sizable performance gains on the OLID test set for two different models, especially for the lower levels of the taxonomy.