MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation Detection
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
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2022
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| _version_ | 1866913425651662848 |
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| author | Toraman, Cagri Ozcelik, Oguzhan Şahinuç, Furkan Can, Fazli |
| author_facet | Toraman, Cagri Ozcelik, Oguzhan Şahinuç, Furkan Can, Fazli |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_05401 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation Detection Toraman, Cagri Ozcelik, Oguzhan Şahinuç, Furkan Can, Fazli Social and Information Networks Computation and Language Information Retrieval 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. |
| title | MiDe22: An Annotated Multi-Event Tweet Dataset for Misinformation Detection |
| topic | Social and Information Networks Computation and Language Information Retrieval |
| url | https://arxiv.org/abs/2210.05401 |