Diverse contents are generated from a wide variety of sources online as new media platforms give people the capacity to create, reconfigure, and spread information in unprecedented amounts. To help users overcome this information overload, online platforms take user preferences and attitudes into account and use this information to program algorithms that mediate and facilitate content promotion (Cinelli et al., 2020). YouTube can also determine what people watch with its recommendation algorithm that is based on user responses and settings such as geography. In this sense, considering the differences in user reaction and recommendations in four different countries during the coronavirus pandemic is the main contribution that this research makes in researching YouTube. Specifically, this study questions how YouTube videos are connected by content type, and how the connection affects user responses in different countries. With the use of a mixed method approach that combines network and content analysis, this study describes the spread of coronavirus information on YouTube in the United States, Mexico, Korea and India, focusing on how differing interactions can reconfigure the architecture of the YouTube network in different countries.

doi: https://www.doi.org/10.22720/hnmr.2020.5.1.025