Toraman, ÇağrıÖzçelik, OğuzhanŞahinuç, FurkanCan, Fazlı2025-02-232025-02-232024978-249381410-4https://hdl.handle.net/11693/116710Conference Name:Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024Date of Conference::20 May 2024through 25 May 2024The 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. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.EnglishCC BY-NC 4.0 (Attribution 4.0 International Deed)https://creativecommons.org/licenses/by-nc/4.0/Human-annotationMisinformation detectionMulti-event datasetTweetConference Paper