  During the COVID-19 pandemic, social media platforms were ideal for
communicating due to social isolation and quarantine. Also, it was the primary
source of misinformation dissemination on a large scale, referred to as the
infodemic. Therefore, automatic debunking misinformation is a crucial problem.
To tackle this problem, we present two COVID-19 related misinformation datasets
on Twitter and propose a misinformation detection system comprising
network-based and content-based processes based on machine learning algorithms
and NLP techniques. In the network-based process, we focus on social
properties, network characteristics, and users. On the other hand, we classify
misinformation using the content of the tweets directly in the content-based
process, which contains text classification models (paragraph-level and
sentence-level) and similarity models. The evaluation results on the
network-based process show the best results for the artificial neural network
model with an F1 score of 88.68%. In the content-based process, our novel
similarity models, which obtained an F1 score of 90.26%, show an improvement in
the misinformation classification results compared to the network-based models.
In addition, in the text classification models, the best result was achieved
using the stacking ensemble-learning model by obtaining an F1 score of 95.18%.
Furthermore, we test our content-based models on the Constraint@AAAI2021
dataset, and by getting an F1 score of 94.38%, we improve the baseline results.
Finally, we develop a fact-checking website called Checkovid that uses each
process to detect misinformative and informative claims in the domain of
COVID-19 from different perspectives.

Machine Learning;Computation and Language;Social and Information Networks