📰 NewsMediaBias-Plus Dataset

🌐 Overview

NewsMediaBias-Plus is a multimodal dataset designed for the analysis of media bias and disinformation through the combination of textual and visual data from news articles. This dataset aims to foster research and development in detecting, categorizing, and understanding the nuances of biased reporting and the dissemination of information in media outlets.

📚 Dataset Description

The NewsMediaBias-Plus dataset comprises news articles paired with relevant images, complete with annotations that reflect perceived biases and the reliability of the content. It extends existing datasets by adding a multimodal dimension, offering new opportunities for comprehensive bias detection in news media.

🔗 Additional Resources

• 🌍 Dataset website
• 🖼️ Full Version and Images Download

📑 Contents

• unique_id: Unique identifier for each news item. Each unique_id is associated with the image (top image) for the same news article.
• outlet: Publisher of the news article.
• headline: Headline of the news article.
• article_text: Full text content of the news article.
• image_description: Description of the image paired with the article.
• image: File path of the image associated with the article.
• date_published: Publication date of the news article.
• source_url: Original URL of the news article.
• canonical_link: Canonical URL of the news article, if different from the source URL.
• new_categories: Categories assigned to the article.
• news_categories_confidence_scores: Confidence scores for the assigned categories.

🏷️ Annotation Labels

• text_label: Annotation for the textual content, indicating:

  • 'Likely': Likely to be disinformation.
  • 'Unlikely': Unlikely to be disinformation.

• multimodal_label: Annotation for the combined text snippet (first paragraph of the news story) and image content, assessing:

  • 'Likely': Likely to be disinformation.
  • 'Unlikely': Unlikely to be disinformation.

Benchmarking Results: Text-Based Models

Model	Configuration	Precision	Recall	F1	Test Accuracy
Small Language Models
BERT-base-uncased	FT	0.8887	0.8870	0.8878	0.8870
DistilBERT	FT	0.8665	0.8554	0.8609	0.8710
RoBERTa-base	FT	0.8940	0.8940	0.8940	0.8940
GPT2	FT	0.8762	0.8751	0.8756	0.8751
BART	FT	0.8762	0.8760	0.8761	0.8760
Large Language Models
Llama 3.1-8B-instruct	0-shot	0.8280	0.6890	0.7521	0.7200
	5-shot	0.8400	0.7700	0.8035	0.7905
	IFT	0.8019	0.8019	0.8019	0.8180
Llama 3.1-8B (base)	FT	0.8800	0.8600	0.8699	0.8320
Llama 3.2-3B-instruct	0-shot	0.7386	0.7550	0.7467	0.6897
	5-shot	0.7989	0.6840	0.7370	0.6133
	IFT	0.8390	0.7984	0.8182	0.8084
Llama 3.2-3B (base)	FT	0.8400	0.8500	0.8450	0.8200
Mistral-v0.3 7B-instruct	0-shot	0.8153	0.5250	0.6387	0.6990
	5-shot	0.8319	0.8134	0.8225	0.7830
	IFT	0.8890	0.9240	0.9062	0.7980
Mistral-v0.3 7B (base)	FT	0.8200	0.7400	0.7779	0.8014
Qwen2.5-7B	0-shot	0.8576	0.8576	0.8576	0.8576
	5-shot	0.8660	0.8790	0.8724	0.8900
	IFT	0.8357	0.8474	0.8415	0.8474

Table 1: Performance metrics for various language models and configurations. Configuration types: 0-shot = No prior examples used for inference, 5-shot = Five examples provided for context before inference, FT = Fine-tuned on task-specific data, IFT = Instruction fine-tuned with targeted training.

Benchmarking Results: Text-Image Models

Model	Config. (Text-Image)	Precision	Recall	F1	Test Accuracy
Small Language Models
SpotFake (XLNET + VGG-19)	FT	0.7415	0.6790	0.7089	0.8151
BERT + ResNet-34	FT	0.8311	0.6277	0.7152	0.8248
FND-CLIP (BERT and CLIP)	FT	0.6935	0.7151	0.7041	0.8971
Distill-RoBERTa and CLIP	FT	0.7000	0.6600	0.6794	0.8600
Large Vision Language Models
Phi-3-vision-128k-instruct	0-shot	0.7400	0.6700	0.7033	0.7103
Phi-3-vision-128k-instruct	5-shot	0.7600	0.7200	0.7395	0.7024
Phi-3-vision-128k-instruct	IFT	0.7800	0.8000	0.7899	0.7200
LLaVA-1.6	0-shot	0.7531	0.6466	0.6958	0.6500
LLaVA-1.6	5-shot	0.7102	0.6893	0.6996	0.6338
Llama-3.2-11B-Vision-Instruct	0-shot	0.6668	0.7233	0.6939	0.7060
Llama-3.2-11B-Vision-Instruct	5-shot	0.7570	0.7630	0.7600	0.7299
Llama-3.2-11B-Vision-Instruct	IFT	0.7893	0.8838	0.8060	0.9040

Table 2: Performance metrics for various small and large language models in text-image configurations. Configuration types: 0-shot = No prior examples used for inference, 5-shot = Five examples provided for context before inference, FT = Fine-tuning, IFT = Instruction Fine-tuning.

🚀 Getting Started

📋 Prerequisites

• Python 3.6 or later
• Pandas
• Datasets (from Hugging Face)
• Hugging Face Hub

💻 Installation

pip install pandas datasets

📖 Usage

Load the full dataset into your Python environment:

from datasets import load_dataset

ds = load_dataset("vector-institute/newsmediabias-plus")
print(ds)  # Displays structure and splits
print(ds['train'][0])  # Access the first element of the train split
print(ds['train'][:5])  # Access the first five elements of the train split

Load a few records:

from datasets import load_dataset

# Load the dataset in streaming mode
streamed_dataset = load_dataset("vector-institute/newsmediabias-plus", streaming=True)

# Get an iterable dataset
dataset_iterable = streamed_dataset['train'].take(5)  # Change 'train' to the appropriate split if needed

# Print the records
for record in dataset_iterable:
    print(record)

🤝 Contributions

Contributions to this dataset are welcome. You can contribute in several ways:

• Data Contribution: Add more data points to enhance the dataset’s utility.
• Annotation Improvement: Help refine the annotations for better accuracy.
• Usage Examples: Contribute usage examples to help the community understand how to leverage this dataset effectively.

To contribute, please fork the repository and create a pull request with your proposed changes.

📄 License

This dataset is released under a specific license CC BY-NC: Attribution-NonCommercial 4.0 that allows for non-commercial use.

📚 Citation

If you use the NewsMediaBias-Plus dataset in your research, please cite it using the following BibTeX entry:

@misc{vector_institute_2024_newsmediabias_plus,
  title={NewsMediaBias-Plus: A Multimodal Dataset for Analyzing Media Bias},
  author={Vector Institute Research Team},
  year={2024},
  url={https://huggingface.co/datasets/vector-institute/newsmediabias-plus}
}

📧 Contact

For any questions or support related to this dataset, please contact [email protected].

👥 Dataset Creators

Creators: Shaina Raza*, Franklin Ogidi, Emrul Hasan, Caesar Saleh, Veronica Chatrath, Maximus Powers, Marcelo Lotif
Advisors: Arash Afkanpour, Aditya Jain, Gias uddin, Deval Pandya

⚠️ Disclaimer and Guidance for Users

Disclaimer: The classifications of 'Likely' and 'Unlikely' disinformation are based on LLM-based annotations and assessments by content experts and are intended for informational purposes only. They should not be seen as definitive or used to label entities definitively without further analysis.

Guidance for Users: This dataset is intended to encourage critical engagement with media content. Users are advised to use these annotations as a starting point for deeper analysis and to cross-reference findings with reliable sources. Please approach the data with an understanding of its intended use as a tool for research and awareness, not as a conclusive judgment.