Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The COVMis-Stance dataset: Stance Detection on Twitter for COVID-19
  Misinformation   During the COVID-19 pandemic, large amounts of COVID-19 misinformation are
spreading on social media. We are interested in the stance of Twitter users
towards COVID-19 misinformation. However, due to the relative recent nature of
the pandemic, only a few stance detection datasets fit our task. We have
constructed a new stance dataset consisting of 2631 tweets annotated with the
stance towards COVID-19 misinformation. In contexts with limited labeled data,
we fine-tune our models by leveraging the MNLI dataset and two existing stance
detection datasets (RumourEval and COVIDLies), and evaluate the model
performance on our dataset. Our experimental results show that the model
performs the best when fine-tuned sequentially on the MNLI dataset and the
combination of the undersampled RumourEval and COVIDLies datasets. Our code and
dataset are publicly available at
https://github.com/yanfangh/covid-rumor-stance

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The categories of this paper are: hep-ph
9
The categories of this paper are: nlin
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
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The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
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The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
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The categories of this paper are: atom-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: cond-mat
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The categories of this paper are: q-alg
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The categories of this paper are: funct-an
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The categories of this paper are: cmp-lg