Hugging Face's logo

Datasets:
ogozcelik
/
turkish-fake-news-detection

Tasks:
Text Classification
Modalities:
Text
Formats:
csv
Languages:
Turkish
Size:
1K - 10K
Libraries:
Datasets
pandas
License:
Dataset card Viewer Files Community
turkish-fake-news-detection / README.md
ogozcelik's picture
ogozcelik
Update README.md
6b6bb45 verified
preview code
|
raw
history blame contribute delete
2.33 kB
metadata
license: mit
task_categories:
  - text-classification
language:
  - tr
size_categories:
  - 1K<n<10K
pretty_name: mide22-tr

MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation Detection

5,064 Turkish tweets with their misinformation labels for several recent events between 2020 and 2022, including the Russia-Ukraine war, COVID-19 pandemic, and Refugees. The dataset includes user engagements with the tweets in terms of likes, replies, retweets, and quotes. For user engagements please use contact at the end of the dataset card.

Data Fields

  • tweet: a string feature.
  • label: a classification label, with possible values including True, False, Other.

Data Size

classes true false other
tweets 669 1,732 2,663

Annotations

Tweets are labeled in terms of three classes: True, False, and Other.

  • True: tweets with the correct information regarding the corresponding event.
  • False: tweets with misinformation on the corresponding event.
  • Other: tweets that cannot be categorized under false or true information.

Annotation process

This dataset is annotated by five annotators. Each tweet is annotated by at least two annotators, we calculate Krippendorf’s alpha reliability to measure interannotator agreement (IAA). The resulting alpha coefficient is 0.791. Details are given in our paper.

Dataset Sources

Citation 

@inproceedings{toraman-etal-2024-mide22-annotated,
    title = "{M}i{D}e22: An Annotated Multi-Event Tweet Dataset for Misinformation Detection",
    author = "Toraman, Cagri  and
      Ozcelik, Oguzhan  and
      Sahinuc, Furkan  and
      Can, Fazli",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.986",
    pages = "11283--11295",
}

Dataset Card Contact

ogozcelik[at]gmail[dot]com