---
task_categories:
- text-classification
language:
- de
tags:
- toxic
- offensive
- hate
- perspectivism
- subjective
- spans
---

# AustroTox: A Dataset for Target-Based Austrian German Offensive Language Detection

This is the data for the paper [AustroTox: A Dataset for Target-Based Austrian German Offensive Language Detection](https://aclanthology.org/2024.findings-acl.713/). 


## Dataset description

#### Index
The index of the comment

#### Article Title
The title of the article under which it was posted

#### Comment
The text from the Jigsaw Toxic Comment Classification Challenge which was classified

#### Label
Denotes whether the aggregated label is Toxic / Offensive (1) or Not toxic / Not offensive (0)

#### Annotators not toxic
The annotator ids of the annotators who labelled the text as not toxic / not offensive 

#### Annotators toxic
The annotator ids of the annotators who labelled the text as toxic / offensive

### Manually cleaned
Indicates whether the annotations were manually cleaned

### Label fine
The fine-grained label of the post

#### Tags
The spans and the labels of the spans



## Citation

If you use this dataset, please cite us: 

```
@inproceedings{pachinger-etal-2024-austrotox,
    title = "{A}ustro{T}ox: A Dataset for Target-Based {A}ustrian {G}erman Offensive Language Detection",
    author = "Pachinger, Pia  and
      Goldzycher, Janis  and
      Planitzer, Anna  and
      Kusa, Wojciech  and
      Hanbury, Allan  and
      Neidhardt, Julia",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-acl.713/",
    doi = "10.18653/v1/2024.findings-acl.713",
    pages = "11990--12001",
    abstract = "Model interpretability in toxicity detection greatly profits from token-level annotations. However, currently, such annotations are only available in English. We introduce a dataset annotated for offensive language detection sourced from a news forum, notable for its incorporation of the Austrian German dialect, comprising 4,562 user comments. In addition to binary offensiveness classification, we identify spans within each comment constituting vulgar language or representing targets of offensive statements. We evaluate fine-tuned Transformer models as well as large language models in a zero- and few-shot fashion. The results indicate that while fine-tuned models excel in detecting linguistic peculiarities such as vulgar dialect, large language models demonstrate superior performance in detecting offensiveness in AustroTox."
}
```