The onset of the COVID-19 pandemic has attracted significant attention on social media platforms as these platforms provide users unparalleled access to ‘information’ from around the globe. In spite of demographic differences, people have been expressing and shaping their opinions using social media on topics ranging from the plight of migrant workers to vaccine development. However, the social media induced polarisation owing to selective online exposure to information during the COVID-19 pandemic has been a major cause of concern for countries across the world. In this paper, we analyse the temporal dynamics of polarisation in online discourse related to the COVID-19. We use random network theory-based simulation to investigate the evolution of opinion formation in comments posted on different COVID-19-related YouTube videos. Our findings reveal that as the pandemic unfolded, the extent of polarisation in the online discourse increased with time. We validate our experimental model using real-world complex networks and compare consensus formation on these networks with equivalent random networks. This study has several implications as polarisation around socio-cultural issues in crises such as pandemic can exacerbate the social divide. The framework proposed in this study can aid regulatory agencies to take required actions and mitigate social media-induced polarisation.