Table 2.

Negative binomial regression to explain the retweet count of COVID-19 tweets for authorities and experts (N=8251 tweets).

Variables		Authoritiesa			Expertsb
		IRRc	Z	P value	IRR	Z	P value
Model variable: constant		16.69	30.57	<.001	71.95	61.37	<.001
Structural variables
	Hashtag	0.64	–6.92	<.001	1.11	1.56	.12
	Images	1.06	1.32	.19	1.06	0.87	.38
	URL	0.82	–4.81	<.001	0.76	–4.27	<.001
	Mentions	0.81	–5.45	<.001	0.73	–5.27	<.001
Content variables
	Severity	1.40	8.09	<.001	1.18	3.34	<.001
	Susceptibility	1.02	0.46	.65	1.15	1.21	.23
	Efficacy	1.34	8.63	<.001	1.10	1.71	.09
	Technical information	1.45	4.23	<.001	1.00	0.06	.95
	Social	1.24	4.05	<.001	1.27	2.69	.01
	Political	0.71	–6.12	<.001	0.87	–1.12	.26
Style variables
	First person	0.93	–1.80	.07	1.10	0.02	.99
	Second person	1.88	6.96	<.001	1.03	0.23	.82
Other: followers count		1.00	28.74	<.001	1.00	25.99	<.001

^aAuthorities: –2 log-likelihood=–44365.18; Akaike information criterion=44,395; null model logistic regression χ²=1854.8 (P<.001); McFadden pseudo R²=0.04.

^bExperts: –2 log-likelihood=–33,752.49; Akaike information criterion=33,782; null model logistic regression χ²=956,66 (P<.001); McFadden pseudo R²=0.03.

^cIRR: incidence rate ratio.