---
license: cc-by-nc-4.0
task_categories:
- video-classification
language:
- en
- ja
tags:
- multimodal
- greenwashing
- climate
- environment
- video
- advertisement
- llm
size_categories:
- n<1K
---



# Dataset Card for Dataset Name

This is a dataset to reproduce our work, "A Multimodal Benchmark for Framing of Oil & Gas Advertising and Potential Greenwashing Detection", accepted to NeurIPS 2025 Benchmarks and Datasets Track.


## Dataset Details

### Dataset Description

- **Curated by:** The authors of the paper.
- **Language(s) (NLP):** Videos are usually in English, while a small portion of videos contain non-English languages such as Japanese.
- **License:** CC BY-NC 4.0. This license only applies to our contributions. We do not retain rights of the sourced artifcats such as the video content. 

### Dataset Sources [optional]

<!-- Provide the basic links for the dataset. -->

- **Repository:** https://github.com/climate-nlp/multimodal-oil-gas-benchmark
- **Paper:** A Multimodal Benchmark for Framing of Oil & Gas Advertising and Potential Greenwashing Detection (NeurIPS 2025)

## Uses

<!-- Address questions around how the dataset is intended to be used. -->
We created this annotated video dataset to benchmark VLMs for predicting obstruction and impressionistic framing by O&G entities. 


### Direct Use

<!-- This section describes suitable use cases for the dataset. -->
Research purpose.


### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
This dataset is made available for research purposes only and may not be used for commercial purposes.

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->
The dataset files are formatted in JSON-Line, one video sample for each line.
The following is the basic data structure for each video. You might see additional fields for meta data. 

```json
{
  "video_id": "video_001",  # The unique ID for the video
  "video_url": "[ANONYMIZED]",  # The URL of the video
  "labels": ["PA"],  # The annotated label(s)
  "video_length_seconds": 15,  # The video length
  "entity_name": "[ANONYMIZED]"  # The entity name of the video publisher
}
```


## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

Read the above.

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
The labels are annotated for video URLs sourced from YouTube and Facebook ads.

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
Read our paper for more detail.


#### Who are the source data producers?

<!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
The source videos are obtained from public ad videos on the social media. The labels of the Facebook dataset are originally created in previous literature (c.f., Holder et al. and Rowlands et. al.). Read our paper for more detail.


#### Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->
The labels for the YouTube dataset are manually annotated. The labels of the Facebook dataset are distantly annotated. Read our paper for more detail. 

#### Who are the annotators?

<!-- This section describes the people or systems who created the annotations. -->
For the YouTube dataset, the authors engaged in the annotations. The labels of the Facebook dataset are originally annotated in previous literature (c.f., Holder et al. and Rowlands et. al.).


#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->
N/A.

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->
We do not see significant risks. Read our paper for more detail.

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
N/A.

## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

```text
@inproceedings{morio-etal-2025-multimodal,
	author = {Morio, Gaku and Rowlands, Harri and Stammbach, Dominik and Manning, Christopher D and Henderson, Peter},
	booktitle = {Advances in Neural Information Processing Systems},
	title = {A Multimodal Benchmark for Framing of Oil \& Gas Advertising and Potential Greenwashing Detection},
	year = {2025}
}
```

## Dataset Card Contact

Please contact to the authors. The primary contact email can be found in the paper.