REVIEW 1 cited by
MM-COVID: A Multilingual and Multimodal Data Repository for Combating COVID-19 Disinformation
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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.
Forward citations
Cited by 1 Pith paper
-
When Scale Meets Diversity: Evaluating Language Models on Fine-Grained Multilingual Claim Verification
A 270M-parameter encoder model (XLM-R) achieves 57.7% macro-F1 on the X-Fact multilingual claim verification benchmark, beating the best tested 7-12B LLM (16.9%) and the prior state of the art (41.9%).
Discussion (0). Continue with ORCID to comment.