MBBMD: Media Bias Bias-Mitigated Dataset

MBBMD (Media Bias Bias-Mitigated Dataset) is a dataset designed for bias detection in Spanish-language news articles. The dataset is structured hierarchically into two annotation levels, enabling research in binary bias classification as well as fine-grained bias categorization.

This dataset has been created to support Natural Language Processing (NLP) research, bias detection models, and media studies by offering a structured and annotated corpus of news articles covering diverse political perspectives and a range of bias types.

MBBMD consists of 100 news articles sourced from multiple Spanish-language media outlets, annotated using a perspectivist approach (LeWiDi) at the document level. The dataset is divided into two main phases, each containing training, testing, and control subsets.

MBBMD is structured into two levels of analysis:

1. Level 1: Document-level binary classification
   This level determines whether a news article is biased or not, based on majority vote agreement among annotators. It includes percentage scores reflecting the degree of agreement.

2. Level 2: Multilabel bias classification
   This level categorizes bias into five specific types: intentional bias, spin bias, statement bias, coverage bias, and gatekeeping bias. Each type includes binary majority vote annotations and percentage agreement scores from annotators.

To enhance annotation robustness, Counterfactual Data Augmentation (CDA) techniques were applied to a subset of the dataset. These modifications involve outlet swaps, entity swaps, and terminological changes, allowing for the assessment of how these factors influence annotator perceptions of bias.

Dataset File Structure and Field Descriptions

The Multilevel Bias Detection Dataset for Spanish Media (MBBMD) is organized into two main directories, corresponding to the two annotation phases:

• Phase 1 (Document-level annotations): Focused on binary and multilabel bias classification at the article level.
• Phase 2 (Sentence-level annotations): Provides fine-grained sentence-level bias annotations.

Each directory contains three subsets:

• train: The primary dataset for training models.
• test: A reserved subset for model evaluation.
• control: Contains articles modified using Counterfactual Data Augmentation (CDA) to analyze annotation robustness.

The dataset files are stored in TSV (Tab-Separated Values) format, encoded in <st