MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation Detection
This addresses the need for better datasets to detect misinformation on social media, which can harm public health and safety, though it is incremental as it builds on existing data collection efforts.
The authors tackled the problem of misinformation detection by constructing MiDe22, a new human-annotated dataset of 10,348 tweets in English and Turkish from events like the Russia-Ukraine war and COVID-19, and provided benchmark evaluation results.
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.