README.md

metadata

license: cc-by-sa-4.0
task_categories:
  - text-classification
language:
  - en
pretty_name: Media Bias Identification Benchmark
configs:
  - cognitive-bias
  - fake-news
  - gender-bias
  - hate-speech
  - linguistic-bias
  - political-bias
  - racial-bias
  - text-level-bias

Dataset Card for Media-Bias-Identification-Benchmark

Table of Contents

• Dataset Card for Meida-Bias-Identification-Benchmark
  • Table of Contents
  • Dataset Description
    • Dataset Summary
    • Tasks and Information
    • Languages
  • Dataset Structure
    • Data Instances
      • cognitive-bias
    • Data Fields
  • Considerations for Using the Data
    • Social Impact of Dataset
    • Discussion of Biases
    • Other Known Limitations
    • Citation Information
    • Contributions

Dataset Description

• Homepage: https://github.com/Media-Bias-Group/Media-Bias-Identification-Benchmark
• Repository: https://github.com/Media-Bias-Group/Media-Bias-Identification-Benchmark
• Paper: TODO
• Point of Contact: Martin Wessel

Dataset Summary

TODO

Tasks and Information

Dataset	Source	Sub-domain	Task Type	Classes
ECtHR (Task A)	Chalkidis et al. (2019)	ECHR	Multi-label classification	10+1
ECtHR (Task B)	Chalkidis et al. (2021a)	ECHR	Multi-label classification	10+1
SCOTUS	Spaeth et al. (2020)	US Law	Multi-class classification	14
EUR-LEX	Chalkidis et al. (2021b)	EU Law	Multi-label classification	100
LEDGAR	Tuggener et al. (2020)	Contracts	Multi-class classification	100
UNFAIR-ToS	Lippi et al. (2019)	Contracts	Multi-label classification	8+1
CaseHOLD	Zheng et al. (2021)	US Law	Multiple choice QA	n/a

Baseline

Dataset	ECtHR A	ECtHR B	SCOTUS	EUR-LEX	LEDGAR	UNFAIR-ToS	CaseHOLD
Model	μ-F1 / m-F1	μ-F1 / m-F1	μ-F1 / m-F1	μ-F1 / m-F1	μ-F1 / m-F1	μ-F1 / m-F1	μ-F1 / m-F1
TFIDF+SVM	64.7 / 51.7	74.6 / 65.1	78.2 / 69.5	71.3 / 51.4	87.2 / 82.4	95.4 / 78.8	n/a
Medium-sized Models (L=12, H=768, A=12)
BERT	71.2 / 63.6	79.7 / 73.4	68.3 / 58.3	71.4 / 57.2	87.6 / 81.8	95.6 / 81.3	70.8
RoBERTa	69.2 / 59.0	77.3 / 68.9	71.6 / 62.0	71.9 / 57.9	87.9 / 82.3	95.2 / 79.2	71.4
DeBERTa	70.0 / 60.8	78.8 / 71.0	71.1 / 62.7	72.1 / 57.4	88.2 / 83.1	95.5 / 80.3	72.6
Longformer	69.9 / 64.7	79.4 / 71.7	72.9 / 64.0	71.6 / 57.7	88.2 / 83.0	95.5 / 80.9	71.9
BigBird	70.0 / 62.9	78.8 / 70.9	72.8 / 62.0	71.5 / 56.8	87.8 / 82.6	95.7 / 81.3	70.8
Legal-BERT	70.0 / 64.0	80.4 / 74.7	76.4 / 66.5	72.1 / 57.4	88.2 / 83.0	96.0 / 83.0	75.3
CaseLaw-BERT	69.8 / 62.9	78.8 / 70.3	76.6 / 65.9	70.7 / 56.6	88.3 / 83.0	96.0 / 82.3	75.4
Large-sized Models (L=24, H=1024, A=18)
RoBERTa	73.8 / 67.6	79.8 / 71.6	75.5 / 66.3	67.9 / 50.3	88.6 / 83.6	95.8 / 81.6	74.4

Languages

All datasets are in English

Dataset Structure

Data Instances

cognitive-bias

An example of one training instance looks as follows.

{
  "text": "A defense bill includes language that would require military hospitals to provide abortions on demand",
  "label": 1
}

Data Fields

• text: a sentence from various sources (eg., news articles, twitter, other social media).
• label: binary indicator of bias (0 = unbiased, 1 = biased)

Considerations for Using the Data

Social Impact of Dataset

TODO

Discussion of Biases

TODO

Other Known Limitations

TODO

Citation Information

TODO

Contributions

TODO