Information Seeking Patterns and COVID-19 in the United States


  • Bianca Reisdorf UNC Charlotte
  • Grant Blank
  • Johannes Bauer
  • Shelia Cotten
  • Craig Robertson
  • Megan Knittel



information seeking, COVID-19, information sources, knowledge gaps


In this paper, we describe how socioeconomic background and political leaning are related to how U.S. residents look for information on COVID-19. Using representative survey data from 2,280 U.S. internet users, collected in fall 2020, we examine how factors, such as age, gender, race, income, education, political leaning, and internet skills are related to how many different types of sources and what types of sources respondents use to find information on COVID-19. Moreover, we describe how many checking actions individuals use to verify information, and how all of these factors are related to knowledge about COVID-19. Results show that men, those with higher education, higher incomes, and higher self-perceived internet ability, and those who are younger used more types of information sources. Similar patterns emerged for checking actions. When we examined different types of sources (mainstream media, conservative sources, medical sources, and TV sources), three patterns emerged: 1) respondents who have more resources used more types of sources; 2) demographic factors made less difference for conservative media consumers; and 3) conservative media were the only type of source used less by younger age groups than older age groups. Finally, availability of resources and types of information sources were related to differences in factual knowledge. Respondents who had fewer resources, those who used conservative news media, and those who engaged in more checking actions got fewer answers right. This difference could lead to information divides and associated knowledge gaps in the United States regarding the coronavirus pandemic.




How to Cite

Reisdorf, B., Blank, G., Bauer, J., Cotten, S., Robertson, C., & Knittel, M. (2021). Information Seeking Patterns and COVID-19 in the United States. Journal of Quantitative Description: Digital Media , 1.