mlsanityData to 14 Oct 2026

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

arXiv 2210.05401 ↗First posted 11 Oct 2022cs.SI cs.CL cs.IRcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 25 of its 72 references that are in the map. First posted on arXiv 11 Oct 2022.

Builds on

25 of 72 references.

References · 25

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13 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 1; 2026: 1.

4 papers.

  1. 2025When Detection Fails: The Power of Fine-Tuned Models to Generate Human-Like Social Media Textcited by 4
  2. 2023ARC-NLP at Multimodal Hate Speech Event Detection 2023: Multimodal Methods Boosted by Ensemble Learning, Syntactical and Entity Featurescited by 2
  3. 2023Unveiling Global Narratives: A Multilingual Twitter Dataset of News Media on the Russo-Ukrainian Conflictcited by 1
  4. 2026DIA-HARM: Dialectal Disparities in Harmful Content Detection Across 50 English Dialectscited by 1