  Successful navigation of the Covid-19 pandemic is predicated on public
cooperation with safety measures and appropriate perception of risk, in which
emotion and attention play important roles. Signatures of public emotion and
attention are present in social media data, thus natural language analysis of
this text enables near-to-real-time monitoring of indicators of public risk
perception. We compare key epidemiological indicators of the progression of the
pandemic with indicators of the public perception of the pandemic constructed
from ~20 million unique Covid-19-related tweets from 12 countries posted
between 10th March -- 14th June 2020. We find evidence of psychophysical
numbing: Twitter users increasingly fixate on mortality, but in a decreasingly
emotional and increasingly analytic tone. Semantic network analysis based on
word co-occurrences reveals changes in the emotional framing of Covid-19
casualties that are consistent with this hypothesis. We also find that the
average attention afforded to national Covid-19 mortality rates is modelled
accurately with the Weber-Fechner and power law functions of sensory
perception. Our parameter estimates for these models are consistent with
estimates from psychological experiments, and indicate that users in this
dataset exhibit differential sensitivity by country to the national Covid-19
death rates. Our work illustrates the potential utility of social media for
monitoring public risk perception and guiding public communication during
crisis scenarios.
