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.
@article{arxiv.2210.05401,
title = {MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation Detection},
author = {Cagri Toraman and Oguzhan Ozcelik and Furkan Şahinuç and Fazli Can},
journal= {arXiv preprint arXiv:2210.05401},
year = {2024}
}