This study analyzes the spread of information about the COVID-19 pandemic across five social media platforms: Twitter, Instagram, YouTube, Reddit, and Gab. The research uses massive data analysis to examine user engagement and interest in the topic, and to assess the evolution of discourse on a global scale for each platform. The study also models the spread of information using epidemic models to characterize the basic reproduction number (R₀) for each platform, which indicates the average number of secondary cases generated by an individual posting about the pandemic. The research finds that information from both reliable and questionable sources does not show different spreading patterns. However, information from questionable sources is more likely to spread on certain platforms. The study also identifies that the amplification of rumors varies across platforms, with some platforms amplifying misinformation more than others. The study highlights the critical role of information diffusion in the management of disease outbreaks, and the potential for misinformation to influence public behavior and the effectiveness of government responses. The research also shows that the interaction patterns of each platform and the characteristics of the audience on each platform play a significant role in the spread of information and misinformation. The study concludes that the main drivers of information spreading are related to the specific characteristics of each platform and the dynamics of user groups engaged with the topic. The findings suggest that understanding the interaction patterns between content consumption and social media platforms is important for designing more effective epidemic models and communication strategies during crises.This study analyzes the spread of information about the COVID-19 pandemic across five social media platforms: Twitter, Instagram, YouTube, Reddit, and Gab. The research uses massive data analysis to examine user engagement and interest in the topic, and to assess the evolution of discourse on a global scale for each platform. The study also models the spread of information using epidemic models to characterize the basic reproduction number (R₀) for each platform, which indicates the average number of secondary cases generated by an individual posting about the pandemic. The research finds that information from both reliable and questionable sources does not show different spreading patterns. However, information from questionable sources is more likely to spread on certain platforms. The study also identifies that the amplification of rumors varies across platforms, with some platforms amplifying misinformation more than others. The study highlights the critical role of information diffusion in the management of disease outbreaks, and the potential for misinformation to influence public behavior and the effectiveness of government responses. The research also shows that the interaction patterns of each platform and the characteristics of the audience on each platform play a significant role in the spread of information and misinformation. The study concludes that the main drivers of information spreading are related to the specific characteristics of each platform and the dynamics of user groups engaged with the topic. The findings suggest that understanding the interaction patterns between content consumption and social media platforms is important for designing more effective epidemic models and communication strategies during crises.