Online Social Media Network’s Modularity Predict Spreading of Misinformation about COVID-19: Evidence from Russia (Preprint)

BACKGROUND The outbreak of SARS-CoV-2 in 2019 was associated with the growth of the popularity of conspiracy theories that undermine vaccination campaigns. There is evidence that the popularity of misinformation about COVID-19 is associated with online social media use. Online social media provides network effects on the spreading of information. It is important to distinguish between social network use effects and network effects within the platform. OBJECTIVE This study aimed to investigate the effects of online social network modularity of online social media on spreading and attitude towards information and misinformation about COVID-19. METHODS We used data for the social network structure of online social media VK to construct the adjusted modularity index (Fragmentation index) of 162 Russian towns. We combined network indexes on the town level with a poll “Research on COVID-19 in Russia’s Regions” (RoCIRR) that had 23’000 respondents. We measure knowledge of fake and true statements about COVID-19 and attitudes towards those statements. RESULTS We observe a positive association of town level fragmentation with individual knowledge of fake statements and a negative association with true statements. We observe a strong negative association with an average attitude towards true statements, but an insignificant positive association with an attitude towards fake statements. At the same time, we observe a strong association between network fragmentation and ideological differences in attitudes between true and fake statements. CONCLUSIONS While social media use plays an important role in the diffusion of information about health, social network structure could amplify such results. Social network modularity plays a key role in the spreading of information online, but differently for true and fake statements. Those changes in spreading trickle down into differences in attitudes about true and fake statements about COVID-19. Finally, we observe that fragmentation leads to individual polarization on medical topics. For future research interaction between online social media use and network effects could be studied further.