While there is ample research on the influence of retracted scientific publications on author reputation, less is known about how a health organization's retraction of scientific guidance can impact public perceptions of the organization. This study centers on the aerosol guidance retraction of the Centers for Disease Control and Prevention (CDC) in 2020. X/Twitter social media data were collected via ForSight from September 15 to October 8, 2020, with a machine learning algorithm specifically developed and used to detect sentiment toward the CDC. Regression analyses of the non-bot sample (N = 265,326) tested for differences in CDC sentiment across four stages: 1) baseline; 2) CDC guidance change; 3) CDC retraction of the prior guidance change; and 4) CDC reversion to a tempered form of the initial guidance change. The results show that sentiment toward the CDC increased from Time 1 to Time 2, then decreased for Time 3 with the "posted in error" retraction, but then increased for Time 4 back to a level similar to Time 2. That public perceptions of the CDC could improve after these changes in scientific guidance may be attributed to its self-report of the retraction and reporting that the retraction was a result of unintentional error. This study connects theories of reputation management and trust repair with the growing empirical research on retractions of published scientific research to provide a theoretical explanation for how a major public health organization can mitigate damage to its reputation in the short term.
Keywords: social media, retraction, CDC, COVID-19, aeroso, reputation, machine learning.