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Vector Space Semantic Models Predict Subjective Probability Judgments for Real-World Events
We examine how people judge the probabilities of real-world
events, such as natural disasters in different countries. We
find that the associations between the words and phrases that
constitute these events, as assessed by vector space semantic
models, strongly correlate with the probabilities assigned to
these events by participants. Thus, for example, the semantic
proximity of āearthquakeā and āJapanā accurately predicts
judgments regarding the probability of an earthquake in
Japan. Our results suggest that the mechanisms and
representations at play in language are also active in high-
level domains, such as judgment and decision making, and
that existing insights regarding these representations can be
used to make precise, quantitative, a priori predictions
regarding the probability estimates of individuals
Public risk perception and emotion on Twitter during the Covid-19 pandemic
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 ā¼20 million unique Covid-19-related tweets from 12 countries posted between 10th March and 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 WeberāFechner 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