task_categories:text-classification task_categories:zero-shot-classification language:en license:mit size_categories:10K<n<100K format:parquet modality:text library:datasets library:pandas library:mlcroissant library:polars region:us climate
---
dataset_info:
  features:
  - name: quote
    dtype: string
  - name: label
    dtype: string
  - name: __index_level_0__
    dtype: int64
  splits:
  - name: train
    num_bytes: 26926412
    num_examples: 32212
  download_size: 13491004
  dataset_size: 26926412
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: mit
task_categories:
- text-classification
- zero-shot-classification
language:
- en
tags:
- climate
---

# Toxic Agent - Qwen Synthetic Data : Magpie-like Climate Disinformation Dataset

## Dataset Description

### Overview

This dataset contains synthetic climate change-related statements, including various forms of climate disinformation and denial. It was created by generating variations and transformations of real climate-related statements, producing a diverse set of synthetic examples across different categories of climate disinformation.

Total examples from `qwen2.5:14b-instruct-q4_K_M`: 32,212 entries


### Labels

The dataset includes the following categories of climate-related statements:

1. `1_not_happening`: Denial of climate change occurrence
2. `2_not_human`: Denial of human influence on climate
3. `3_not_bad`: Minimization of climate change impacts
4. `4_solutions_harmful_unnecessary`: Opposition to climate solutions
5. `5_science_is_unreliable`: Questioning climate science
6. `6_proponents_biased`: Criticism of climate scientists
7. `7_fossil_fuels_needed`: Pro-fossil fuel advocacy

### Data Fields

- `quote`: The synthetic climate-related statement (text)
- `label`: The category of climate disinformation (string)

### Data Splits

This dataset is provided as a single split containing all examples.

## Intended Uses

This dataset is intended for:
- Research on climate disinformation detection
- Development of content moderation systems
- Study of climate denial narratives
- Training classification models for climate-related content

## Limitations & Ethical Considerations

- This dataset contains synthetic examples of climate disinformation
- Should not be used to spread or promote climate denial
- Intended for research and educational purposes only
- May contain biases from the generation process

## Citation & Attribution

If you use this dataset, please cite:

```bibtex
@dataset{synthetic_climate_disinfo_2024,
  author       = {[Joseph Pollack]},
  title        = {Toxic Agent - Qwen Synthetic Data},
  year         = {2024},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/DataTonic/climate-guard-synthetic_data_qwen_toxic_agent}}
}
```

## Dataset Creation

### Data Generation Process

The dataset was created using:
1. Base examples from the QuotaClimat/frugalaichallenge-text-train dataset
2. Generation of variations using "qwen2.5:14b-instruct-q4_K_M" 
3. Preservation of original label categories
4. Multiple generation strategies for diversity

#### Prompts

```python
                    full_prompt = f"{prompts['prefix']}{quote}{prompts['suffix']}"
```

```python
        # category-specific prompts for same category variations
        self.same_category_prompts = {
            'long': {
                'prefix': "Generate a detailed, comprehensive climate statement that expands on this theme while maintaining the same perspective: ",
                'suffix': "\nMake it more detailed and thorough while preserving the original stance."
            },
            'medium': {
                'prefix': "Create a moderate-length climate statement based on this, keeping the same viewpoint: ",
                'suffix': "\nBalance detail and conciseness while maintaining the original position."
            },
            'small': {
                'prefix': "Write a concise version of this climate statement: ",
                'suffix': "\nKeep it brief but impactful, preserving the core message."
            }
        }
        
        # category-specific prompts for cross-category generation
        self.category_prompts = {
            '1_not_happening': {
                'prefix': "EXAMPLE :  \n \n",
                'suffix': "\nthe EXAMPLE above is a statement about climate change. Produce a fictional statement in the same style that denies climate change occurrence:  Focus on arguments against climate change evidence."
            },
            '2_not_human': {
                'prefix': "EXAMPLE : \n \n",
                'suffix': "\nthe EXAMPLE above is a statement about climate change. Produce a fictional  statement in the same style that argues against human-caused climate change: Emphasize natural climate variability over human influence."
            },
            '3_not_bad': {
                'prefix': "EXAMPLE :  \n \n",
                'suffix': "\n\nthe EXAMPLE above is a statement about climate change. Produce a  statement in the same style minimizing climate change impacts: a Highlight potential benefits and downplay negative effects."
            },
            '4_solutions_harmful_unnecessary': {
                'prefix': "EXAMPLE :  \n \n",
                'prefix': "Based on this climate topic, create a statement opposing climate action solutions: ",
                'suffix': "\n\n the EXAMPLE above is a statement about climate change. Produce a  statement in the same style opposing climate action solutions: focus on economic and social costs of climate policies."
            },
            '5_science_is_unreliable': {
                'prefix': "EXAMPLE :  \n \n",
                'suffix': "\n\n the EXAMPLE above is a statement about climate change. Produce a  statement in the same style questioning climate science Emphasize uncertainties and methodological concerns."
            },
            '6_proponents_biased': {
                'prefix': "EXAMPLE :  \n \n",
                'suffix': "\n\n the EXAMPLE above is a statement about climate change. Produce a  statement in the same style criticizing climate scientists: Question motivations and highlight potential conflicts of interest."
            },
            '7_fossil_fuels_needed': {
                'prefix': "EXAMPLE :  \n \n",
                'suffix': "\n\n\n the EXAMPLE above is a statement about climate change. Produce a   statement in the same style advocating for fossil fuels: Emphasize economic necessity and reliability of fossil fuels."
            }
        }

### Preprocessing

- Removed irrelevant examples
- Compose the prompt according to the above
- Maintained label consistency

## Dataset Structure

```python
DatasetDict({
    'train': Dataset({
        features: ['quote', 'label'],
        num_rows: 4835
    })
})
```

## Additional Information

### Dataset Curators

[Tonic](https://huggingface.co/Tonic) / [Datatonic](https://huggingface.co/DataTonic)

### Licensing Information

This dataset is released under the MIT License.

### Feedback & Contributions

For questions, feedback, or contributions, please:
1. Open an issue on the dataset repository
2. Contact the dataset maintainers
3. Submit a pull request with improvements

## Example Usage

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("Tonic/climate-guard-synthetic_data_qwen_toxic_agent")

# Access examples
for example in dataset['train'].select(range(3)):
    print(f"Quote: {example['quote']}")
    print(f"Label: {example['label']}")
    print("---")
```

## Version History

- v1.0.0 (2024-02-08): Initial release with 4,835 synthetic examples

## Acknowledgements

- Based on the QuotaClimat/frugalaichallenge-text-train dataset
- Generated using RunPod infrastructure
- Developed for climate disinformation research
