
# Dataset Card: News Headlines and Justifications for Fake News Classification

## Overview

In an effort to combat fake news, researchers from the University of Leeds have curated and collected a dataset of fake news along with the justification why a certain news headline is fake or accurate. This dataset contains news headlines along with corresponding justifications to determine if the news is fake or not fake (factual). The dataset aims to balance political news records with 7971 instances and scientific misconceptions with 626 instances, providing a diverse set of information for training and evaluating models for fake news detection and fact-checking tasks.

## Dataset Description

- **Dataset Size**: 8597 records
  - 7971 political news headlines with justifications (not fake and factual)
  - 626 scientific misconception headlines with proofs (fake)

- **Data Format**:
  The data is provided in JSONL format, and each record consists of two main components:

  1. `"prompt"`: The news headline or scientific misconception statement that requires classification.
  2. `"completion"`: The corresponding justification or proof that either validates the information as factual or identifies it as a fake/false claim.

## Example Records

### Political News Example:
```
{
  "prompt": "Since Trump labeled ANTIFA a terrorist organization their rights were stripped by the Patriot Act which states if someone is labeled a terrorist they are not afforded due process. - a statement by Instagram posts.",
  "completion": "The statement claims that since Trump labeled ANTIFA a terrorist organization, their rights were stripped by the Patriot Act. This is false. The Patriot Act does not give federal law enforcement officials the right to detain suspected terrorists who are U.S. citizens without probable cause. Also, Antifa is not a designated terrorist organization and is not listed on the State Department's list of foreign terrorist organizations. Even if it were, American activists would still have due process rights thanks to the Fifth and Fourteenth Amendments. Antifa is not a structured organization and has no leaders, it is organized into autonomous local groups. Therefore, the statement that rights were stripped by the Patriot Act is false."
}
```

### Scientific Misconception Example:
```
{
  "prompt": "Economic recessions always lead to long-term declines in productivity.",
  "completion": "The belief that economic recessions always lead to long-term declines in productivity is a misconception. While recessions can have negative short-term effects on productivity due to factors such as layoffs, reduced investments, and business closures, they can also lead to structural adjustments, technological advancements, and efficiency gains. Proper policy responses, including supportive measures for businesses and investments in human capital, can help mitigate the short-term productivity effects and pave the way for long-term productivity growth."
}
```

## Potential Use Cases

- Fake News Detection: This dataset can be used to train machine learning models for fake news classification, helping identify misleading or false information in political news headlines.
- Fact-Checking: The dataset can aid in building fact-checking systems that analyze news statements and verify their accuracy with accompanying justifications.
- NLP Research: Researchers can use this dataset to explore natural language processing techniques for misinformation detection and debunking scientific misconceptions.

## Data Collection Method

The data collection method for this dataset is not provided in this card. However, it's essential to ensure that the data collection process follows ethical guidelines, respects privacy, and avoids any biases.

## Limitations and Ethical Considerations

- Dataset Bias: Care should be taken to avoid any potential biases present in the dataset, whether in terms of the sources or the selection of news headlines.
- Fact-Checking Integrity: The dataset's accuracy relies on the trustworthiness of the sources used for fact-checking political news headlines.
- Privacy Concerns: Personal information, if present, should be anonymized to protect individuals' privacy and adhere to data protection regulations.

## Citation

```bibtex
@misc{PolNewsJustification,
  title = {PolNewsJustification: An Open Dataset of news headline justification},
  author = {Waleed Kareem},
  year = {2023},
  publisher = {HuggingFace},
  journal = {HuggingFace repository},
}
```
license:mit region:us
{"license": "mit"}
TAGS
#license-mit #region-us 


# Dataset Card: News Headlines and Justifications for Fake News Classification

## Overview

In an effort to combat fake news, researchers from the University of Leeds have curated and collected a dataset of fake news along with the justification why a certain news headline is fake or accurate. This dataset contains news headlines along with corresponding justifications to determine if the news is fake or not fake (factual). The dataset aims to balance political news records with 7971 instances and scientific misconceptions with 626 instances, providing a diverse set of information for training and evaluating models for fake news detection and fact-checking tasks.

## Dataset Description

- Dataset Size: 8597 records
  - 7971 political news headlines with justifications (not fake and factual)
  - 626 scientific misconception headlines with proofs (fake)

- Data Format:
  The data is provided in JSONL format, and each record consists of two main components:

  1. '"prompt"': The news headline or scientific misconception statement that requires classification.
  2. '"completion"': The corresponding justification or proof that either validates the information as factual or identifies it as a fake/false claim.

## Example Records

### Political News Example:


### Scientific Misconception Example:


## Potential Use Cases

- Fake News Detection: This dataset can be used to train machine learning models for fake news classification, helping identify misleading or false information in political news headlines.
- Fact-Checking: The dataset can aid in building fact-checking systems that analyze news statements and verify their accuracy with accompanying justifications.
- NLP Research: Researchers can use this dataset to explore natural language processing techniques for misinformation detection and debunking scientific misconceptions.

## Data Collection Method

The data collection method for this dataset is not provided in this card. However, it's essential to ensure that the data collection process follows ethical guidelines, respects privacy, and avoids any biases.

## Limitations and Ethical Considerations

- Dataset Bias: Care should be taken to avoid any potential biases present in the dataset, whether in terms of the sources or the selection of news headlines.
- Fact-Checking Integrity: The dataset's accuracy relies on the trustworthiness of the sources used for fact-checking political news headlines.
- Privacy Concerns: Personal information, if present, should be anonymized to protect individuals' privacy and adhere to data protection regulations.


# Dataset Card: News Headlines and Justifications for Fake News Classification ## Overview

In an effort to combat fake news, researchers from the University of Leeds have curated and collected a dataset of fake news along with the justification why a certain news headline is fake or accurate. This dataset contains news headlines along with corresponding justifications to determine if the news is fake or not fake (factual). The dataset aims to balance political news records with 7971 instances and scientific misconceptions with 626 instances, providing a diverse set of information for training and evaluating models for fake news detection and fact-checking tasks. ## Dataset Description

- Dataset Size: 8597 records
  - 7971 political news headlines with justifications (not fake and factual)
  - 626 scientific misconception headlines with proofs (fake)

- Data Format:
  The data is provided in JSONL format, and each record consists of two main components:

  1. '"prompt"': The news headline or scientific misconception statement that requires classification.
  2. '"completion"': The corresponding justification or proof that either validates the information as factual or identifies it as a fake/false claim. ## Example Records ### Political News Example: ### Scientific Misconception Example: ## Potential Use Cases

- Fake News Detection: This dataset can be used to train machine learning models for fake news classification, helping identify misleading or false information in political news headlines.
- Fact-Checking: The dataset can aid in building fact-checking systems that analyze news statements and verify their accuracy with accompanying justifications.
- NLP Research: Researchers can use this dataset to explore natural language processing techniques for misinformation detection and debunking scientific misconceptions. ## Data Collection Method

The data collection method for this dataset is not provided in this card. However, it's essential to ensure that the data collection process follows ethical guidelines, respects privacy, and avoids any biases. ## Limitations and Ethical Considerations

- Dataset Bias: Care should be taken to avoid any potential biases present in the dataset, whether in terms of the sources or the selection of news headlines.
- Fact-Checking Integrity: The dataset's accuracy relies on the trustworthiness of the sources used for fact-checking political news headlines.
- Privacy Concerns: Personal information, if present, should be anonymized to protect individuals' privacy and adhere to data protection regulations.
TAGS
#license-mit #region-us 
 # Dataset Card: News Headlines and Justifications for Fake News Classification ## Overview

In an effort to combat fake news, researchers from the University of Leeds have curated and collected a dataset of fake news along with the justification why a certain news headline is fake or accurate. This dataset contains news headlines along with corresponding justifications to determine if the news is fake or not fake (factual). The dataset aims to balance political news records with 7971 instances and scientific misconceptions with 626 instances, providing a diverse set of information for training and evaluating models for fake news detection and fact-checking tasks. ## Dataset Description

- Dataset Size: 8597 records
  - 7971 political news headlines with justifications (not fake and factual)
  - 626 scientific misconception headlines with proofs (fake)

- Data Format:
  The data is provided in JSONL format, and each record consists of two main components:

  1. '"prompt"': The news headline or scientific misconception statement that requires classification.
  2. '"completion"': The corresponding justification or proof that either validates the information as factual or identifies it as a fake/false claim. ## Example Records ### Political News Example: ### Scientific Misconception Example: ## Potential Use Cases

- Fake News Detection: This dataset can be used to train machine learning models for fake news classification, helping identify misleading or false information in political news headlines.
- Fact-Checking: The dataset can aid in building fact-checking systems that analyze news statements and verify their accuracy with accompanying justifications.
- NLP Research: Researchers can use this dataset to explore natural language processing techniques for misinformation detection and debunking scientific misconceptions. ## Data Collection Method

The data collection method for this dataset is not provided in this card. However, it's essential to ensure that the data collection process follows ethical guidelines, respects privacy, and avoids any biases. ## Limitations and Ethical Considerations

- Dataset Bias: Care should be taken to avoid any potential biases present in the dataset, whether in terms of the sources or the selection of news headlines.
- Fact-Checking Integrity: The dataset's accuracy relies on the trustworthiness of the sources used for fact-checking political news headlines.
- Privacy Concerns: Personal information, if present, should be anonymized to protect individuals' privacy and adhere to data protection regulations.
passage: TAGS
#license-mit #region-us 
# Dataset Card: News Headlines and Justifications for Fake News Classification## Overview

In an effort to combat fake news, researchers from the University of Leeds have curated and collected a dataset of fake news along with the justification why a certain news headline is fake or accurate. This dataset contains news headlines along with corresponding justifications to determine if the news is fake or not fake (factual). The dataset aims to balance political news records with 7971 instances and scientific misconceptions with 626 instances, providing a diverse set of information for training and evaluating models for fake news detection and fact-checking tasks.## Dataset Description

- Dataset Size: 8597 records
  - 7971 political news headlines with justifications (not fake and factual)
  - 626 scientific misconception headlines with proofs (fake)

- Data Format:
  The data is provided in JSONL format, and each record consists of two main components:

  1. '"prompt"': The news headline or scientific misconception statement that requires classification.
  2. '"completion"': The corresponding justification or proof that either validates the information as factual or identifies it as a fake/false claim.## Example Records### Political News Example:### Scientific Misconception Example:## Potential Use Cases

- Fake News Detection: This dataset can be used to train machine learning models for fake news classification, helping identify misleading or false information in political news headlines.
- Fact-Checking: The dataset can aid in building fact-checking systems that analyze news statements and verify their accuracy with accompanying justifications.
- NLP Research: Researchers can use this dataset to explore natural language processing techniques for misinformation detection and debunking scientific misconceptions.## Data Collection Method

The data collection method for this dataset is not provided in this card. However, it's essential to ensure that the data collection process follows ethical guidelines, respects privacy, and avoids any biases.