Table 4.

Regression results grouped by season.

Explanatory variable	Explained Variable: Number of Negative Weibo Posts
	Spring Sample	Summer Sample	Autumn Sample	Winter Sample
	Model 1	Model 2	Model 3	Model 4
Weekend	−2.2482	0.0871	6.2589	−8.4189
	(2.6921)	(2.1938)	(11.2871)	(13.5364)
Holiday	−5.6358	−1.2324	−47.9146 ***	−67.8414 ***
	(3.6301)	(3.0269)	(13.7614)	(20.1369)
Temperature	0.6360	8.0492 **	7.9258 ***	5.6999 **
	(1.0657)	(3.3116)	(2.2704)	(2.3145)
Square of temperature	−0.0424	−0.1510 **	−0.3072 ***	−0.2588
	(0.0296)	(0.0656)	(0.0700)	(0.2622)
Humidity	−0.1998 *	0.1838 ***	−0.3889	2.5306 ***
	(0.1091)	(0.0656)	(0.4263)	(0.7840)
Precipitation	0.4642 *	−0.0656	1.9974 *	−20.4136 ***
	(0.2790)	(0.0478)	(1.0437)	(7.0949)
Sea level pressure	0.6597 **	0.4025 **	1.2726	2.8981 **
	(0.3241)	(0.2010)	(1.2203)	(1.3610)
Wind speed	0.4996	1.2986 **	5.2688 ***	4.3613 ***
	(0.3360)	(0.6216)	(1.2799)	(1.2222)
Major event	0.6764	−0.4948	6.8629	1.5558
	(4.9188)	(1.7036)	(11.7907)	(18.1195)
PM2.5	0.5568 ***	0.1989 ***	1.8648 ***	0.8980 ***
	(0.0430)	(0.0558)	(0.2196)	(0.1695)
R2	0.7682	0.3350	0.6598	0.5936

Note: The values in the table represent the correlation coefficients of the variables of the regression model, and the standard error of each coefficient is in parentheses. ***, **, * indicate significance at the levels of 0.01, 0.05 and 0.1, respectively. Winter is the default variable.