Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Eating Garlic Prevents COVID-19 Infection: Detecting Misinformation on
  the Arabic Content of Twitter   The rapid growth of social media content during the current pandemic provides
useful tools for disseminating information which has also become a root for
misinformation. Therefore, there is an urgent need for fact-checking and
effective techniques for detecting misinformation in social media. In this
work, we study the misinformation in the Arabic content of Twitter. We
construct a large Arabic dataset related to COVID-19 misinformation and
gold-annotate the tweets into two categories: misinformation or not. Then, we
apply eight different traditional and deep machine learning models, with
different features including word embeddings and word frequency. The word
embedding models (\textsc{FastText} and word2vec) exploit more than two million
Arabic tweets related to COVID-19. Experiments show that optimizing the area
under the curve (AUC) improves the models' performance and the Extreme Gradient
Boosting (XGBoost) presents the highest accuracy in detecting COVID-19
misinformation online.

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The categories of this paper are: astro-ph
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
33
The categories of this paper are: plasm-ph
23
The categories of this paper are: solv-int
3
The categories of this paper are: physics
7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con