ABSTRACT
Misinformation in online media has become a major research topic the last few years, especially during the COVID-19 pandemic. Indeed, false or misleading news about coronavirus have been characterized as an infodemic1 by the World Health Organization, because of how fast it can spread online. A considerable vector of spreading misinformation is represented by conspiracy theories. During this challenge, we tackled the problem of detecting COVID-19-related conspiracy theories in tweets. To perform this task, we used different approaches such as a combination of TFIDF and machine learning algorithms, transformer-based neural networks or Natural Language Inference. Our best model obtains a MCC score of 0.726 for the main task on the validation set and a MCC score of 0.775 on the test set making it the best performing method among the challenge competitors. Copyright 2021 for this paper by its authors.