Background: COVID-19 has continued to spread in the United States and globally. Closely monitoring public engagement and perceptions of COVID-19 and preventive measures using social media data could provide important information for understanding the progress of current interventions and planning future programs.
Objective: The aim of this study is to measure the public's behaviors and perceptions regarding COVID-19 and its effects on daily life during 5 months of the pandemic.
Methods: Natural language processing (NLP) algorithms were used to identify COVID-19-related and unrelated topics in over 300 million online data sources from June 15 to November 15, 2020. Posts in the sample were geotagged by NetBase, a third-party data provider, and sensitivity and positive predictive value were both calculated to validate the classification of posts. Each post may have included discussion of multiple topics. The prevalence of discussion regarding these topics was measured over this time period and compared to daily case rates in the United States.
Results: The final sample size included 9,065,733 posts, 70% of which were sourced from the United States. In October and November, discussion including mentions of COVID-19 and related health behaviors did not increase as it had from June to September, despite an increase in COVID-19 daily cases in the United States beginning in October. Additionally, discussion was more focused on daily life topics (n=6,210,255, 69%), compared with COVID-19 in general (n=3,390,139, 37%) and COVID-19 public health measures (n=1,836,200, 20%).
Conclusions: There was a decline in COVID-19-related social media discussion sourced mainly from the United States, even as COVID-19 cases in the United States increased to the highest rate since the beginning of the pandemic. Targeted public health messaging may be needed to ensure engagement in public health prevention measures as global vaccination efforts continue.
Keywords: COVID-19; COVID-19 public health measures; Facebook; Facebook data; NLP; Reddit; Reddit data; United States; behavior; cross-sectional; engagement; infodemic; infodemiology; infoveillance; natural language processing; perception; public health; public perception; social listening; social media; social media analysis; social media research; surveillance.
©Daisy Massey, Chenxi Huang, Yuan Lu, Alina Cohen, Yahel Oren, Tali Moed, Pini Matzner, Shiwani Mahajan, César Caraballo, Navin Kumar, Yuchen Xue, Qinglan Ding, Rachel Dreyer, Brita Roy, Harlan Krumholz. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 21.06.2021.