Detection of COVID-19-related conpiracy theories in tweets using transformer-based models and node embedding techniques

With the amount of information shared on the internet increasing on a daily basis, we are prone to face more misinformation online. This is especially true on social media websites, where users have good amount of freedom to share their opinion. During the COVID-19 pandemic, numerous conspiracy theories were shared on Twitter. In this “FakeNews Detection” task, the goal is to detect COVID-19- related conspiracy theories using tweet text and user interaction graph. We tackled this challenge using Transformer-based models (CT-BERT) and node embedding techniques (node2vec) with classification objective models. Our best model obtains a MCC score of 0.719 on the test data.