Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study
  of COVID-19 Infodemic   The spreading COVID-19 misinformation over social media already draws the
attention of many researchers. According to Google Scholar, about 26000
COVID-19 related misinformation studies have been published to date. Most of
these studies focusing on 1) detect and/or 2) analysing the characteristics of
COVID-19 related misinformation. However, the study of the social behaviours
related to misinformation is often neglected. In this paper, we introduce a
fine-grained annotated misinformation tweets dataset including social
behaviours annotation (e.g. comment or question to the misinformation). The
dataset not only allows social behaviours analysis but also suitable for both
evidence-based or non-evidence-based misinformation classification task. In
addition, we introduce leave claim out validation in our experiments and
demonstrate the misinformation classification performance could be
significantly different when applying to real-world unseen misinformation.

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The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
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The categories of this paper are: atom-ph
4
The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
34
The categories of this paper are: chem-ph
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The categories of this paper are: hep-ex
2
The categories of this paper are: math
33
The categories of this paper are: plasm-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: eess
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The categories of this paper are: bayes-an
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The categories of this paper are: mtrl-th