COVID-19 Induced Misinformation on YouTube: An Analysis of User Commentary

Abstract

Several scholars have demonstrated a positive link between political polarization and the resistance to COVID-19 prevention measures. At the same time, political polarization has also been associated with the spread of misinformation. This study investigates the theoretical linkages between polarization and misinformation and measures the flow of misinformation about COVID-19 in the comment sections of four popular YouTube channels for over 16 months using big data sources and methods. For the analysis, we downloaded about 3.5M English language YouTube comments posted in response to videos about the pandemic. We then classified the comments into one of the two following categories by applying a supervised Natural Language Processing classifier: (1)fake: comments that contain claims and speculation which are verifiably not true; and (2)legitimate:comments that do not fall into the fake category. The results show that the level of misinformation in YouTube comment sections has increased during the pandemic, that fake comments attract statistically more likes, and that the ratio of fake comments increased by 0.4% per month. These findings suggest that once introduced into an online discussion, misinformation potentially leads to an escalating spiral of misinformation comments, which undermines public policy. Overall, the results signal alarming pandemic-related misinformation and, potentially, rising levels of affective polarization. We place these results in context and point out the limitations of our approach.