MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation Detection
Cagri Toraman, Oguzhan Ozcelik, Furkan Sahinuc, Fazli Can
Abstract
The rapid dissemination of misinformation through online social networks poses a pressing issue with harmful consequences jeopardizing human health, public safety, democracy, and the economy; therefore, urgent action is required to address this problem. In this study, we construct a new human-annotated dataset, called MiDe22, having 5,284 English and 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. We also provide a detailed data analysis with descriptive statistics and the experimental results of a benchmark evaluation for misinformation detection.
BibTeX
@inproceedings{toraman-etal-2024-mide22,
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",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
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"
}