| --- |
| pretty_name: GerAV-Twitter |
| license: other |
| language: |
| - de |
| task_categories: |
| - text-classification |
|
|
| gated: true |
|
|
| configs: |
| - config_name: twitter |
| data_files: |
| - split: train |
| path: train-* |
| - split: validation |
| path: validation-* |
| - split: test |
| path: test-* |
|
|
| extra_gated_heading: Request access to GerAV-Twitter |
|
|
| extra_gated_description: > |
| GerAV-Twitter is available exclusively for academic |
| research. Please review the access conditions below before submitting |
| an access request. |
| |
| extra_gated_button_content: Request access |
|
|
| extra_gated_prompt: > |
| By submitting this request, you confirm that you have read and agree |
| to the access conditions described below. |
| |
| extra_gated_fields: |
| Full name: text |
| Institution: text |
| Institutional email: text |
|
|
| Intended use: |
| type: select |
| options: |
| - Academic research |
| - Other |
|
|
| Research purpose: |
| type: text |
|
|
| I agree to the GerAV-Twitter Access Conditions: checkbox |
| --- |
| |
| # GerAV-Twitter |
|
|
| ## Dataset Description |
|
|
| GerAV-Twitter is a German authorship verification benchmark belonging to |
| the publication [**Towards New Heights in German Authorship Verification |
| using Fine-Tuned LLMs on a New Benchmark (Kiefer et al., 2026)**](https://aclanthology.org/2026.findings-acl.1991/). |
| The dataset is designed for research on authorship verification and |
| related tasks in computational authorship analysis. |
|
|
| GerAV-Twitter contains authorship verification instances based on |
| German-language Twitter posts. Each instance consists of two posts, |
| referred to as `post_a` and `post_b`, together with information about |
| their respective authors and a binary authorship-verification label. |
|
|
| The dataset contains `train`, `validation`, and `test` splits. |
|
|
| The original text of the Twitter posts is not directly distributed with |
| the benchmark. Instead, posts are identified through IDs that can be |
| mapped to the corresponding texts in the [**original Twitter dataset**](https://zenodo.org/records/7528718). |
| Please refer to the paper and the corresponding |
| [**GerAV GitHub repository**](https://github.com/NL2G/GerAV) for |
| information on obtaining and reconstructing the original texts. |
|
|
| ## Data Format |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `label` | boolean | Whether the two posts were written by the same author | |
| | `post_a` | object | Information about the first Twitter post | |
| | `post_b` | object | Information about the second Twitter post | |
|
|
| Each `post_a` and `post_b` entry contains IDs belonging the the tweet and the author of the tweet. |
|
|
| The authorship verification task is therefore defined as determining |
| whether `post_a` and `post_b` were written by the same author. |
|
|
| ## Intended Use |
|
|
| GerAV-Twitter is intended for academic and non-commercial research |
| on authorship verification, stylometry, NLP, and related areas. |
|
|
| ## Access Conditions |
|
|
| You need to satisfy the following conditions for your access request |
| to be accepted: |
|
|
| - The dataset may be used for academic research only. |
| Such use must comply with the applicable Twitter terms of usage and user privacy as stated in the [dataset licence of the source dataset](https://zenodo.org/records/7528718). |
| - Access is restricted to researchers affiliated with a research or |
| educational institution. Please provide an institutional email |
| address with your request. |
| - The dataset may not be used for commercial purposes or for mass |
| surveillance. |
| - Access requests are reviewed individually. Additional restrictions |
| or exclusions may be applied based on the information provided in |
| the access request. |
| - The dataset may not be redistributed or made available to third |
| parties. |
|
|
| By requesting access, you confirm that you have read and agree to |
| these conditions. |
|
|
| ## Citation |
|
|
| If you use GerAV-Twitter in your research, please cite: |
|
|
| ```bibtex |
| @inproceedings{kiefer-etal-2026-gerav, |
| title = "{G}er{AV}: Towards New Heights in {G}erman Authorship Verification using Fine-Tuned {LLM}s on a New Benchmark", |
| author = "Kiefer, Lotta and |
| Leiter, Christoph and |
| Takeshita, Sotaro and |
| Schmidt, Elena and |
| Eger, Steffen", |
| editor = "Liakata, Maria and |
| Moreira, Viviane P. and |
| Zhang, Jiajun and |
| Jurgens, David", |
| booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026", |
| month = jul, |
| year = "2026", |
| address = "San Diego, California, United States", |
| publisher = "Association for Computational Linguistics", |
| url = "https://aclanthology.org/2026.findings-acl.1991/", |
| doi = "10.18653/v1/2026.findings-acl.1991", |
| pages = "40050--40069", |
| ISBN = "979-8-89176-395-1" |
| } |
| |
