README.md

metadata

task_categories:
  - text-classification
language:
  - en
tags:
  - headlines
  - click-through-rate
  - preference-learning
  - engagement-prediction
size_categories:
  - 10K<n<100K
dataset_info:
  features:
    - name: test_id
      dtype: string
    - name: eyecatcher_id
      dtype: string
    - name: headline_A
      dtype: string
    - name: headline_B
      dtype: string
    - name: label_pairwise
      dtype: int32
    - name: ctr_diff
      dtype: float32
    - name: ctr_A
      dtype: float32
    - name: ctr_B
      dtype: float32
    - name: counts_A
      list: int32
    - name: counts_B
      list: int32
    - name: created_at
      dtype: int64
    - name: headlines_concat
      dtype: string
  splits:
    - name: train
      num_bytes: 4101477
      num_examples: 8781
    - name: validation
      num_bytes: 470679
      num_examples: 1019
    - name: test
      num_bytes: 2020847
      num_examples: 4357
  download_size: 4360373
  dataset_size: 6593003
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*

Headlines CTR Dataset

This dataset contains pairs of news headlines with labels indicating which headline received more clicks. It's designed for studying what makes headlines engaging and for training models to predict user preferences.

Dataset Description

Each example contains two competing headlines (A and B) that were shown to users, along with engagement metrics and a binary label indicating which performed better.

Dataset Statistics

• Train: 8,781 headline pairs
• Validation: 1,019 headline pairs
• Holdout: 4,357 headline pairs
• Total: 14,157 headline pairs

Features

Each example contains:

• headline_A: First headline text
• headline_B: Second headline text
• label_pairwise: 1 if headline A got more clicks, 0 if B got more
• ctr_A: Click-through rate for headline A
• ctr_B: Click-through rate for headline B
• ctr_diff: Difference in CTR (A - B)
• counts_A: [clicks, impressions] for headline A
• counts_B: [clicks, impressions] for headline B
• test_id: Unique identifier for the A/B test
• eyecatcher_id: Identifier for the content piece
• created_at: Timestamp of the test
• headlines_concat: Pre-concatenated text for convenience

Usage

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("Yanjo/headlines-ctr")

# Access different splits
train_data = dataset["train"]
val_data = dataset["validation"]
holdout_data = dataset["holdout"]

# Example: Get the first training example
example = train_data[0]
print(f"Headline A: {example['headline_A']}")
print(f"Headline B: {example['headline_B']}")
print(f"Winner: {'A' if example['label_pairwise'] == 1 else 'B'}")

Example

{
    'headline_A': 'Nearly 75% of our crops have vanished in the last 100 years...',
    'headline_B': '100 years ago, people were eating things that most of us will never taste.',
    'label_pairwise': 0,  # B performed better
    'ctr_A': 0.0013,
    'ctr_B': 0.0058,
    'ctr_diff': -0.0044
}

Applications

This dataset can be used for:

• Training models to predict headline engagement
• Studying linguistic features that drive clicks
• A/B testing analysis
• Preference learning research
• Natural language understanding of persuasive text

Citation

If you use this dataset, please cite:

@misc{headlines-ctr,
  title={Headlines CTR Dataset},
  author={Yanjo},
  year={2024},
  publisher={HuggingFace}
}

License

Please check with the original data source for licensing information.