Update README.md

README.md

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 ---
 license: cc-by-nc-sa-4.0
+tags:
+  - text-classification
+  - bias-detection
+  - media
+  - news
+task_categories:
+  - text-classification
+task_ids:
+  - multi-class-classification
+language:
+  - en
+pretty_name: NEWSUNFOLD
+size_categories:
+  - n<1K
 ---
+
+# Dataset Card for NEWSUNFOLD
+
+## Dataset Summary
+**NEWSUNFOLD** contains sentence-level labels of linguistic media bias collected for the paper  
+*“NewsUnfold: Creating a News-Reading Application That Indicates Linguistic Media Bias and Collects Feedback”* (https://doi.org/10.1609/icwsm.v19i1.35847).
+
+The dataset has **310** English news sentences with binary bias labels derived from user feedback on the NewsUnfold web app. Each record includes the sentence text, a bias label, an identifier, the source outlet, the original article URL, and a topic tag.
+
+- **Collection window:** March 4-11, 2023  
+- **Label meaning:** `1` = biased, `0` = not biased  
+- **Selection rule:** Only sentences with a clear decision (≥5 votes) are included.
+
+GitHub: https://github.com/Media-Bias-Group/NewsUnfold/
+
+## Use Cases
+- Training/evaluating bias-detection models
+- Auditing or probing linguistic bias signals in news sentences
+- Complementing existing datasets (e.g., BABE) with user-feedback–grounded labels
+
+## Dataset Structure
+
+### Data Fields
+- `id` *(string)*: Unique sentence identifier.  
+- `text` *(string)*: The news sentence.  
+- `label` *(int; 0 or 1)*: Bias label (`1` = biased, `0` = not biased).  
+- `outlet` *(string)*: News outlet/source.  
+- `news_link` *(string/url)*: Link to the original article.  
+- `topic` *(string)*: Topic/category of the article/sentence.
+
+## Citation
+```
+@article{Hinterreiter_Wessel_Schliski_Echizen_Latoschik_Spinde_2025,
+  title    = {NewsUnfold: Creating a News-Reading Application That Indicates Linguistic Media Bias and Collects Feedback},
+  volume   = {19},
+  url      = {https://doi.org/10.1609/icwsm.v19i1.35847},
+  number   = {1},
+  journal  = {Proceedings of the International AAAI Conference on Web and Social Media},
+  author   = {Hinterreiter, Maximilian and Wessel, Daniel and Schliski, Johanna and Echizen, Isao and Latoschik, Marc Erich and Spinde, Timo},
+  year     = {2025},
+  doi      = {10.1609/icwsm.v19i1.35847},
+  pages    = {1237-1248},
+  abstract = {We present NewsUnfold, a news-reading web application that (1) indicates sentence-level linguistic media bias predictions to readers, (2) collects reader feedback on those predictions, and (3) releases a new dataset of sentences with community labels indicating perceived bias. In an in-the-wild study, participants used the app to read curated articles while seeing sentences highlighted as biased or not biased (per a baseline classifier). They could then agree or disagree with these highlights, producing crowd-sourced labels. Using only sentences with a clear decision (≥5 votes), we provide a 310-sentence dataset and report inter-annotator agreement (Krippendorff’s α = .504). We further train a classifier combining the BABE dataset with our new sentences, improving F1 to .824 (+2.49% vs. BABE alone), demonstrating that community feedback can refine bias-detection models and support transparent, user-in-the-loop media analysis.},
+  keywords = {media bias, bias detection, user feedback, dataset, news, sentence-level labeling}
+}
+```