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During the COVID-19 pandemic, social media platforms were ideal forcommunicating due to social isolation and quarantine. Also, it was the primarysource of misinformation dissemination on a large scale, referred to as theinfodemic. Therefore, automatic debunking misinformation is a crucial problem.To tackle this problem, we present two COVID-19 related misinformation datasetson Twitter and propose a misinformation detection system comprisingnetwork-based and content-based processes based on machine learning algorithmsand NLP techniques. In the network-based process, we focus on socialproperties, network characteristics, and users. On the other hand, we classifymisinformation using the content of the tweets directly in the content-basedprocess, which contains text classification models (paragraph-level andsentence-level) and similarity models. The evaluation results on thenetwork-based process show the best results for the artificial neural networkmodel with an F1 score of 88.68%. In the content-based process, our novelsimilarity models, which obtained an F1 score of 90.26%, show an improvement inthe misinformation classification results compared to the network-based models.In addition, in the text classification models, the best result was achievedusing the stacking ensemble-learning model by obtaining an F1 score of 95.18%.Furthermore, we test our content-based models on the Constraint@AAAI2021dataset, 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 eachprocess to detect misinformative and informative claims in the domain ofCOVID-19 from different perspectives.

Checkovid: A COVID-19 misinformation detection system on Twitter using network and content mining perspectives