metadata

dataset_info:
  features:
    - name: url
      dtype: string
    - name: headline
      dtype: string
    - name: image_link
      dtype: string
    - name: is_satire
      dtype: int32
  splits:
    - name: train
      num_bytes: 2841764
      num_examples: 10000
  download_size: 1268537
  dataset_size: 2841764
task_categories:
  - image-classification
language:
  - en
size_categories:
  - 1K<n<10K

Dataset card for "multimodal_satire"

This is the dataset for the paper A Multi-Modal Method for Satire Detection using Textual and Visual Cues. To obtain the full-text body of the articles, you need to scrape websites using the provided links in the dataset.

GitHub repository: https://github.com/lilyli2004/satire

Reference

If you use this dataset, please cite the following paper:

@inproceedings{li-etal-2020-multi-modal,
    title = "A Multi-Modal Method for Satire Detection using Textual and Visual Cues",
    author = "Li, Lily  and
      Levi, Or  and
      Hosseini, Pedram  and
      Broniatowski, David",
    booktitle = "Proceedings of the 3rd NLP4IF Workshop on NLP for Internet Freedom: Censorship, Disinformation, and Propaganda",
    month = dec,
    year = "2020",
    address = "Barcelona, Spain (Online)",
    publisher = "International Committee on Computational Linguistics (ICCL)",
    url = "https://aclanthology.org/2020.nlp4if-1.4",
    pages = "33--38",
    abstract = "Satire is a form of humorous critique, but it is sometimes misinterpreted by readers as legitimate news, which can lead to harmful consequences. We observe that the images used in satirical news articles often contain absurd or ridiculous content and that image manipulation is used to create fictional scenarios. While previous work have studied text-based methods, in this work we propose a multi-modal approach based on state-of-the-art visiolinguistic model ViLBERT. To this end, we create a new dataset consisting of images and headlines of regular and satirical news for the task of satire detection. We fine-tune ViLBERT on the dataset and train a convolutional neural network that uses an image forensics technique. Evaluation on the dataset shows that our proposed multi-modal approach outperforms image-only, text-only, and simple fusion baselines.",
}