README.md

metadata

license: cc-by-nc-nd-4.0
task_categories:
  - text-classification
language:
  - en
tags:
  - media
  - mediabias
  - media-bias
  - media bias
size_categories:
  - 1M<n<10M
dataset_info:
  config_name: plain_text
  splits:
    - name: cognitive_bias
    - name: fake_news
    - name: gender_bias
    - name: hate_speech
    - name: linguistic_bias
    - name: political_bias
    - name: racial_bias
    - name: text_level_bias
configs:
  - config_name: default
    data_files:
      - split: cognitive_bias
        path: mbib-aggregated/cognitive-bias.csv
      - split: fake_news
        path: mbib-aggregated/fake-news.csv
      - split: gender_bias
        path: mbib-aggregated/gender-bias.csv
      - split: hate_speech
        path: mbib-aggregated/hate-speech.csv
      - split: linguistic_bias
        path: mbib-aggregated/linguistic-bias.csv
      - split: political_bias
        path: mbib-aggregated/political-bias.csv
      - split: racial_bias
        path: mbib-aggregated/racial-bias.csv
      - split: text_level_bias
        path: mbib-aggregated/text-level-bias.csv

Dataset Card for Media-Bias-Identification-Benchmark

Table of Contents

• Dataset Card for Media-Bias-Identification-Benchmark
  • Table of Contents
  • Dataset Description
    • Dataset Summary
    • Tasks and Information
    • Baseline
    • 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: https://doi.org/10.1145/3539618.3591882
• Point of Contact: Martin Wessel

Baseline

Task	Model	Micro F1	Macro F1
cognitive-bias	ConvBERT/ConvBERT	0.7126	0.7664
fake-news	Bart/RoBERTa-T	0.6811	0.7533
gender-bias	RoBERTa-T/ELECTRA	0.8334	0.8211
hate-speech	RoBERTA-T/Bart	0.8897	0.7310
linguistic-bias	ConvBERT/Bart	0.7044	0.4995
political-bias	ConvBERT/ConvBERT	0.7041	0.7110
racial-bias	ConvBERT/ELECTRA	0.8772	0.6170
text-leve-bias	ConvBERT/ConvBERT	0.7697	0.7532

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

We believe that MBIB offers a new common ground for research in the domain, especially given the rising amount of (research) attention directed toward media bias

Citation Information

@inproceedings{
    title = {Introducing MBIB - the first Media Bias Identification Benchmark Task and Dataset Collection},
    author = {Wessel, Martin and Spinde, Timo and Horych, Tomáš and Ruas, Terry and Aizawa, Akiko and Gipp, Bela},
    year = {2023},
    note = {[in review]}
}