## 5 Discussion  
Table 6 shows the effect of the selected features for all categories. Specifically, it shows the selected features that are selected based on the regression model with good evaluation measure scores and their effects on the left column for each type of situational information. For example, seven features are selected for type 1 situational information, named: likes, verified, length, negemo, NearState, percept, and follower. Moreover, feature effects are determined by analyzing the coefficients of features using the best regression model which is negative binomial regression. Table 6 shows the following detail: \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**Features** & **Type 0** & **Type 1** & **Type 2** & **Type 3** & **Type 4** & **Type 5** \\ \hline affect & 4.91 & 3.54 & 3.72 & 3.67 & 4.48 & 4.78 \\ posemo & 1.88 & 1.8 & 1.94 & 1.81 & 3.04 & 1.92 \\ negemo & 1.94 & 1.72 & 1.84 & 1.91 & 2.89 & 3.05 \\ anx & 0.3 & 0.26 & 0.27 & 0.26 & 0.28 & 0.31 \\ anger & 0.6 & 0.6 & 0.59 & 0.6 & 0.67 & 0.77 \\ sad & 0.34 & 0.31 & 0.35 & 0.33 & 0.38 & 0.34 \\ certain & 1.03 & 0.99 & 1.08 & 0.94 & 1.04 & 0.99 \\ differ & 1.58 & 1.56 & 1.58 & 1.7 & 1.93 & 1.69 \\ percept & 1.34 & 1.38 & 1.34 & 1.34 & 1.21 & 1.43 \\ see & 0.58 & 0.64 & 0.66 & 0.59 & 0.56 & 0.69 \\ hear & 0.5 & 0.48 & 0.45 & 0.49 & 0.41 & 0.51 \\ feel & 0.21 & 0.19 & 0.15 & 0.21 & 0.18 & 0.17 \\ affiliation & 1.4 & 1.39 & 1.46 & 1.5 & 1.72 & 1.56 \\ achieve & 1.09 & 1.06 & 1.26 & 1.22 & 1.16 & 1.04 \\ power & 2.67 & 2.53 & 2.64 & 2.7 & 2.66 & 2.86 \\ reward & 0.84 & 0.78 & 0.73 & 0.77 & 0.82 & 0.8 \\ risk & 0.74 & 0.68 & 0.73 & 0.69 & 0.78 & 0.73 \\ drives & 6.14 & 5.82 & 6.07 & 6.16 & 6.47 & 6.29 \\ Likes & 7.13 & 5.43 & 5.04 & 7.73 & 2.46 & 7.31 \\ Verified & 0.123 & 0.33 & 0.210 & 0.188 & 0.18 & 0.16 \\ Followers (Log) & 11.837 & 9.621 & 10.765 & 10.213 & 10.341 & 9.321 \\ Following (Log) & 14.312 & 16.21 & 14.212 & 13.211 & 15.122 & 16.623 \\ Near State & 0.039 & 0.036 & 0.049 & 0.036 & 0.049 & 0.058 \\ length & 107.3 & 102.12 & 83.2 & 69.53 & 78.2 & 83.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Full Summary of Attributes (features) of each Type of Situational Information. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**RMSE** & **Type 0** & **Type 1** & **Type 2** & **Type 3** & **Type 4** & **Type 5** \\ \hline Linear Regression & 6.85 & 3.87 & 2.20 & 6.29 & 1.86 & 1.56 \\ Negative Binomial & 0.62 & 0.67 & 0.80 & 0.74 & 0.77 & 0.72 \\ Regression & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Root Mean Square Error Values of Regression Algorithms for Prediction. For Type 1 (notifications or measures were taken), Type 3 (emotional support), and Type 4 (help seeking) information; the more the verified users promote a tweet, the more it enlarges the number of retweets, so it indicates that verified users have an impact on these specific types of situational information and authorities need to pay attention to the verified users, specifically for their greater influence. These might be even local government people who might be seeking help in the form of emergency medical equipment and human resources. Type 5 information (criticizing the government and government agencies) receives a larger number of retweets for unverified users, which shows most common people affect this type of information; also, the usage of the negative words enlarge this type of information. It implies that there is mostly common public and residents from hardest-hit state criticizing the government for their performance in response to COVID-19. From the perspective of authorities, they should pay attention to these rationally thinking criticizers, because, while developing COVID-19 response strategies, their opinions might be valuable. For Type 2 (donations: goods, money, and services), it comes from users who have a large number of followers and are from California state, which was the hardest-hit state and which is considered to be one of the most developed states in the US; this will enlarge the amount of retweets.