README.md

---
license: cc-by-nc-sa-4.0
tags:
- text-classification
- bias-detection
- media
- news
- crowdsourcing
- gamification
task_categories:
- text-classification
task_ids:
- multi-class-classification
language:
- en
pretty_name: NEWS NINJA
size_categories:
- n<1K
---

# Dataset Card for NEWS NINJA (Game Study dataset)

## Dataset Summary
**NEWS NINJA** contains sentence-level labels of linguistic media bias collected for the paper  
*“News Ninja: Gamified Annotation of Linguistic Bias in Online News”* (https://doi.org/10.1145/3677092).

The dataset has **520** English news sentences: **370** re-labeled from BABE (first 370 entries) and **150** newly labeled sentences (last 150). Each record includes the sentence text, an identifier, a binary majority-vote label, and word-level bias highlights. Single-annotator labels are included as separate columns.

GitHub: https://github.com/Media-Bias-Group/News-Ninja/tree/main  
Dataset table (CSV): https://github.com/Media-Bias-Group/News-Ninja/blob/main/News%20Ninja%20Dataset/ExportNewsNinja.csv

## Use Cases
- Training/evaluating linguistic bias detection models  
- Auditing word-/sentence-level bias cues  
- Combining with BABE for larger training corpora

## Dataset Structure

### Data Fields
- `sentencePK` *(string/int)*: Unique sentence identifier.  
- `sentences` *(string)*: The news sentence text.  
- `majority_vote` *(int; 0 or 1)*: `1` = biased, `0` = not biased.  
- `words` *(string/list)*: Words marked as biased (delimited list in CSV).  
- *(plus)* per-annotator columns with individual labels (one column per annotator).

## Citation
```
@article{hinterreiter2024ninja,
  title     = {News Ninja: Gamified Annotation of Linguistic Bias in Online News},
  author    = {Hinterreiter, Smi and Spinde, Timo and Oberd{\"o}rfer, Sebastian and Echizen, Isao and Latoschik, Marc Erich},
  journal   = {Proceedings of the ACM on Human-Computer Interaction},
  volume    = {8},
  number    = {CHI PLAY},
  articleno = {327},
  year      = {2024},
  doi       = {10.1145/3677092},
  url       = {https://dl.acm.org/doi/10.1145/3677092}
}
```