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

Date

2024-01-01

Author

Toraman, Çağrı
Ozcelik, Oguzhan
Şahinuç, Furkan
Can, Fazli

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Human-annotation, Misinformation detection, Multi-event dataset, Tweet

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85195974364&origin=inward
https://hdl.handle.net/11511/110179

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Department of Computer Engineering, Conference / Seminar