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

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 labelled in a semisupervised fashion.

If you are using this dataset, please cite the following paper.

@inproceedings{rosenthal-etal-2021-solid,
    title = "{SOLID}: A Large-Scale Semi-Supervised Dataset for Offensive Language Identification",
    author = "Rosenthal, Sara  and
      Atanasova, Pepa  and
      Karadzhov, Georgi  and
      Zampieri, Marcos  and
      Nakov, Preslav",
    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.findings-acl.80",
    doi = "10.18653/v1/2021.findings-acl.80",
    pages = "915--928",
}