## 4 Analysis and Results
### Blogging Activities at City Level  
Figure 6 shows the time series hourly rainfall and the hourly number of RRWP from 20 to 28 June. No RRWP was found on the social media platform before the rainstorm hit the city. Rainstorm-related blogging activities were first detected when the first rainstorm warning was issued at 4:20 p.m. on 21 June. The blogging activities significantly intensified, particularly when the rainstorm is most intensive during the time period from 19:00 on 21 June to 04:00 on 24 June. During this time period, the city rainfall amount accounts for 90.5% of the total rainfall brought to Beijing by the \"6.22\" rainstorm. About 91.6% of the RRWP was posted during this time period.  
Figure 6 also shows that variations in the rainfall amount are generally consistent with the blogging activities though there seems to be a 1-h time lag. The blogging activities are most intense in about 10 min before the release of the rainstorm warning. A high rainfall amount is not always accompanied by strong blogging activities, particularly when raining occurs from the late night to the early morning and the rainstorm hits suburbs with a small population flow.  
Figure 5: Spatial distribution of the rainfall amount (**a**), the number of Weibo posts (**b**), and the number of RRWP (**c**).  
Figure 6: Time series of hourly accumulated rainfall and Weibo posts.  
The confusion matrix (Figure 7) between the different levels of rainfall amount and the RRWP shows that higher rainfall levels are always associated with more RRWP. We found 38 h with higher rainfall amount and more RRWP. Lower rainfall levels are associated with fewer RRWP. A statistically significant weighted Kappa coefficient of 0.63 indicates that the levels of blogging activities are consistent with the rainfall levels across the city.  
Figure 8 shows the correlation coefficients between the number of RRWP and rainfall amount with a time lag up to 6 h. With increased time lags, the coefficients drop though the correlations are statistically significant at a confidence level of 0.01. The highest coefficient 0.653 was found when the time lag is 1 h, suggesting that more RRWP were posted one hour after the rainstorm. In other words, heavy rainfall usually triggers intensified blogging activities one hour later. It seems that, after 1 h of the rainstorm, the city starts to be afflicted by issues such as waterlogging and traffic congestion. Such issues tend to intensify rainstorm-related blogging activities.  
Figure 8: Correlation coefficients between the rainfall amount and the number of RRWP with different time lags.  
Figure 7: Consistency between rainfall and RRWP levels.  
Quantile regression analysis between the rainfall amount and the number of RRWP (Figure 9) shows a steeper slope for the higher percentile data. In other words, increased rainfall shows a more significant impact on the number of RRWP when the rainfall is heavier. By contrast, when the rainfall is less than 30 th percentile (the average rainfall of the grid in the study area is 8.6), an increase in the rainfall amount shows little impacts on the change of the RRWP. Once the rainfall exceeds the 30 th percentile, the RRWP starts to increase. As the rainfall percentile increases, the regression slope becomes steeper. In other words, once the rainfall is over the 30 th percentile, it tends to trigger Weibo users to post much more RRWP.