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Table 4 The results from the fit of a power law [see Eq. (11)] to the relationship between the Death NLS and the national daily death count

From: Public risk perception and emotion on Twitter during the Covid-19 pandemic

Power law \(\beta\) \(\nu\) 95% CI \(t\) \(P>|t|\) \(R^2\)* NRMSE \(n\)
Country    (\(\beta\)) (\(\beta\)) (\(\beta\))    
Argentina 0.164 2.21 0.121–0.208 7.59 0.0 0.411 0.114 75
Australia 0.363 0.99 0.181–0.546 4.02 0.0002 0.259 0.173 43
Canada 0.288 0.37 0.252–0.323 16.29 0.0 0.678 0.106 87
Chile 0.085 2.47 0.06–0.109 6.97 0.0 0.382 0.151 78
Colombia 0.112 1.81 0.083–0.142 7.57 0.0 0.425 0.143 75
India 0.126 0.77 0.101–0.15 10.33 0.0 0.558 0.126 81
Mexico 0.141 1.52 0.126–0.157 18.04 0.0 0.78 0.1 78
Nigeria 0.104 1.56 0.037–0.172 3.09 0.0031 0.143 0.223 60
South Africa 0.087 1.11 0.037–0.136 3.52 0.0008 0.16 0.184 66
Spain 0.014 2.14 − 0.03 to 0.059 0.64 0.5241 − 0.042 0.202 82
UK 0.356 0.16 0.302–0.409 13.21 0.0 0.514 0.149 91
USA 0.309 0.21 0.279–0.339 20.54 0.0 0.608 0.139 88
  1. This is the best model in some cases, though it is outperformed by the Weber–Fechner law most times. *While we fit this model assuming a log-log relationship between p and s, we compute \(R^2\) with linear p to make it comparable to the model implied by the Weber–Fechner law [see Eq. (14) in “Appendix 1” for details]. This may cause negative values of \(R^2\) as is the case for Spain