Funding for this project was provided by NOAA/Office of Oceanic and Atmospheric Research under NOAA–University of Oklahoma Cooperative Agreements NA11OAR4320072 and NA12OAR4590120, U.S. Department of Commerce. The statements, findings, conclusions, and recommendations are those of the author(s) and do not necessarily reflect the views of NOAA or the U.S. Department of Commerce.
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The collection of these data and the protection of the user identity are undertaken using a protocol approved by the University of Oklahoma Internal Review Board for the protection of human subjects.
Note that the “blank” spots in Fig. 1 represent days when our program was unable to collect and archive Twitter data because of errors in the data collection process. In most instances, these errors were caused by interruptions in our access to Twitter’s API. In the analysis that follows, data associated with these days are coded as missing (NA).
Like most regression techniques, negative binomial regression models are sensitive to autocorrelation and nonlinearity. To ensure that our models were robust to these sensitivities, we specified a number of alternative models that included 1- and 2-day distributed lag terms and a variety of transformed and polynomial terms. These additions did not improve the fit of the models, which suggests that they are robust to these sensitivities.
We used the regression-based tests outlined by Cameron and Trivedi (1990) to test for overdispersion.
Incident rate ratios were calculated by exponentiating the coefficients in the model.
As suggested by Gelman and Hill (2006), we used analysis of variance (deviance) to confirm this “null” finding. As expected, adding the tornado parameters to models 7 and 8 did not produce a statistically significant improvement in model fit. It is possible, however, that our sample size (n = 190) is too small to detect an independent and significant effect for tornadoes, which are (by design) correlated with the other variables in the model (warnings and watches). As such, readers should interpret this null finding for tornadoes with some caution.