MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation
November 08, 2020 Β· Declared Dead Β· π arXiv.org
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Authors
Yichuan Li, Bohan Jiang, Kai Shu, Huan Liu
arXiv ID
2011.04088
Category
cs.SI: Social & Info Networks
Cross-listed
cs.CY
Citations
96
Venue
arXiv.org
Last Checked
4 months ago
Abstract
The COVID-19 epidemic is considered as the global health crisis of the whole society and the greatest challenge mankind faced since World War Two. Unfortunately, the fake news about COVID-19 is spreading as fast as the virus itself. The incorrect health measurements, anxiety, and hate speeches will have bad consequences on people's physical health, as well as their mental health in the whole world. To help better combat the COVID-19 fake news, we propose a new fake news detection dataset MM-COVID(Multilingual and Multidimensional COVID-19 Fake News Data Repository). This dataset provides the multilingual fake news and the relevant social context. We collect 3981 pieces of fake news content and 7192 trustworthy information from English, Spanish, Portuguese, Hindi, French and Italian, 6 different languages. We present a detailed and exploratory analysis of MM-COVID from different perspectives and demonstrate the utility of MM-COVID in several potential applications of COVID-19 fake news study on multilingual and social media.
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