Past studies in Sarcasm Detection mostly make use of Twitter datasets collected using hashtag based supervision but such datasets are noisy in terms of labels and language. Furthermore, many tweets are replies to other tweets and detecting sarcasm in these requires the availability of contextual tweets.

To overcome the limitations related to noise in Twitter datasets, this News Headlines dataset for Sarcasm Detection is collected from two news website. TheOnion aims at producing sarcastic versions of current events and we collected all the headlines from News in Brief and News in Photos categories (which are sarcastic). We collect real (and non-sarcastic) news headlines from HuffPost.

This new dataset has following advantages over the existing Twitter datasets:

• Since news headlines are written by professionals in a formal manner, there are no spelling mistakes and informal usage. This reduces the sparsity and also increases the chance of finding pre-trained embeddings.

• Furthermore, since the sole purpose of TheOnion is to publish sarcastic news, we get high-quality labels with much less noise as compared to Twitter datasets.

• Unlike tweets which are replies to other tweets, the news headlines we obtained are self-contained. This would help us in teasing apart the real sarcastic elements.

Content

Each record consists of three attributes:

• is_sarcastic: 1 if the record is sarcastic otherwise 0

• headline: the headline of the news article

• article_link: link to the original news article. Useful in collecting supplementary data

General statistics of data, instructions on how to read the data in python, and basic exploratory analysis could be found at this GitHub repo. A hybrid NN architecture trained on this dataset can be found at this GitHub repo.

Citation

If you're using this dataset for your work, please cite the following articles:

Citation in text format:

1. Misra, Rishabh and Prahal Arora. "Sarcasm Detection using News Headlines Dataset." AI Open (2023).
2. Misra, Rishabh and Jigyasa Grover. "Sculpting Data for ML: The first act of Machine Learning." ISBN 9798585463570 (2021).

Citation in BibTex format:

@article{misra2023Sarcasm,
  title = {Sarcasm Detection using News Headlines Dataset},
  journal = {AI Open},
  volume = {4},
  pages = {13-18},
  year = {2023},
  issn = {2666-6510},
  doi = {https://doi.org/10.1016/j.aiopen.2023.01.001},
  url = {https://www.sciencedirect.com/science/article/pii/S2666651023000013},
  author = {Rishabh Misra and Prahal Arora},
}
@book{misra2021sculpting,
  author = {Misra, Rishabh and Grover, Jigyasa},
  year = {2021},
  month = {01},
  pages = {},
  title = {Sculpting Data for ML: The first act of Machine Learning},
  isbn = {9798585463570}
}

Please link to rishabhmisra.github.io/publications as the source of this dataset. Thanks!

Inspiration

Can you identify sarcastic sentences? Can you distinguish between fake news and legitimate news?

Reading the data

Following code snippet could be used to read the data:

import json

def parse_data(file):
    for l in open(file,'r'):
        yield json.loads(l)

data = list(parse_data('./Sarcasm_Headlines_Dataset.json'))