Meneame Media Bias Dataset: Interaction Features and Bias Labels

A processed dataset of news articles submitted to Meneame (Spanish social news aggregator) with automatic media bias labels and rich interaction features derived from user comments.

Contents

• articles_with_features.parquet: 14,995 articles with 38 columns including bias labels (from DistilBERT trained on MBBMD), interaction features (karma statistics, comment engagement metrics), and metadata (outlet, tags, timestamp).
• articles_labeled.parquet: Articles with bias probability scores.
• karma_features.parquet: Advanced karma distribution features per article (entropy, Gini, bimodality, skewness) for 183K+ articles.
• comments_with_sentiment.parquet: 20K comment sample with sentiment (POS/NEG/NEU) and emotion (joy, anger, sadness, fear) scores from pysentimiento/robertuito.
• user_profiles.parquet: User-level bias exposure metrics.
• user_outlet_interactions.parquet: Bipartite graph data (user-outlet comment counts).

Pipeline

Data was collected from meneame.net (2005-2021), processed through a 5-step pipeline: ingestion, filtering, automatic bias labeling (franfj/fdtd_media_bias_E), interaction feature extraction, and statistical analysis. See the GitHub repository for full reproducibility.

Key Statistics

• 14,995 articles from 2,868 media outlets
• 13.2M comments from 96K unique users
• 61.5% articles labeled as biased (automatic labeling)
• Timespan: 2005-2021