While educators’ uses of social media for purposes such as professional learning and networking are now well-established, our understanding of how educational institutions use social media—especially to engage key stakeholders during periods of crisis—is limited. In this study, we used a public data mining research approach to examine how K-12 school districts in the United States used Twitter as a communication tool during a critical period of the COVID-19 pandemic, March-April, 2020. Through a three-step grounded theory approach of 1,357 district tweets from 492 school districts, we found that the themes of messages fell into three categories, announcements, community oriented, or unrelated. Announcements were more common during the early stages of the pandemic (and were engaged with more collaboratively), with community-building posts more common later on. This study demonstrates the potential of district social media use as a communication platform and a means to impact public perceptions and support.

There is no doubt that public education has suffered as a result of the COVID-19 pandemic. Researchers are comparing the COVID slide to summer learning loss, noting this loss could be much worse for those already underserved by U.S. schools (Kuhfeld & Tarasawa, 2020). In order to understand who this loss impacts and how, we need data on school, district, and statewide responses to the pandemic as it unfolded. Luckily, these data are available, as school districts across the country updated their communities about plans for spring 2020. To capture these updates, our team uses multiple approaches for collecting COVID-19-related information via school district websites and social media to create a new, nationwide dataset of district responses.We also analyze how these responses relate to contextual characteristics of districts and their surrounding communities, which will provide a picture of how district characteristics may drive disparities in access to and quality of schooling during the pandemic. Identifying these associations are critical for understanding and disrupting the reproduction and deepening of educational inequality caused by the COVID-19 crisis. The resulting dataset will provide researchers with the information necessary to understand how education during the pandemic may impact students for years to come.