## 4 Situational Information Propagation Level Prediction  
Until now we have seen the general idea of the necessity of information of the public by analyzing the total and average values of each type of information. To more precisely predict the propagation scale (retweeted amount) of situational information, we need key features from our dataset and we extracted different types of features to accurately predict the retweeted amount. We selected the retweeted attribute for propagation because, on Twitter, it means sharing the content further; it is the attribute that satisfies our need to accurately predict the propagation scale of each category of situational information. The extracted key features to predict the propagation scale are as follows: \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Name of Algorithm** & **Mean Accuracy** & **F1** & **Precision** & **Recall** \\ \hline SVM & 67\% & 69\% & 69\% & 66\% \\ SVM (linear kernel) & 70\% & 69\% & 71\% & 68\% \\
**SVM (radial basis kernel)** & **77\%** & **76\%** & **77\%** & **73\%** \\ SVM (Sigmoid Kernel) & 57\% & 59\% & 62\% & 59\% \\ Random Forest & 56\% & 61\% & 63\% & 60\% \\ Multinomial Naive Bayes & 55\% & 57\% & 60\% & 56\% \\ K-nearest neighbor & 54\% & 56\% & 59\% & 55\% \\ Logistic Regression Classifier & 56\% & 57\% & 60\% & 55\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of classification measures of different algorithms which were used to classify the manually labelled sampled data, the value in bold values indicate the highest scores achieved by specified algorithm. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Types** & **Tweets** & **Verified** & **Follower** & **Fre-** & **Following** & **RT** & **Like** \\  & **Frequency** & **Frequency** & **quency** & **Frequency** & **Frequency** & **Frequency** \\ \hline Type 0: Precation and & 6120 & 610 (9.96\%) & 9,104,201 (2266) & 6.131831e +08 (190,183. & 139,431 & 440,375 (167) \\ Care & & & & 549) & (51.955) & \\ Type 1: Announcements & **33,830** & **4660** (12.1\%) & 79,371,572 & 6.418501e +09 (262,889 & 1,701,310 & 6,172,700 (313) \\ or Measures & & (5171) & 0,172) & (103.765) & (103.765) \\ Type 2: Donations & 4720 & 535 (11.33\%) & 7,316,652 (4159) & 5.509554e +08 (159,384 & 187,212 (87,31) & 631,910 (267) \\  & & & & 0.823) & & \\ Type 3: Emotional support & 4120 & 459 (11.14\%) & 4,549,173 (3863) & 7.197526e +08 (**390,513**) & 257,310 & 1,071,331 (497) \\  & & **0.864** & & (127.86) & (127.86) & \\ Type 4: Help Seeking & 2217 & 229 (10.33\%) & 3,325,963 (4221) & 2.318969e +03 (170,904 & 247,310 & 968,137 (771) \\  & & & 0.651) & & (**213.146**) & \\  & & & & (129) & & \\ Type 5: Criticizing the Government & & & & & (120.221) & 1,193,404 (278) \\  & & & & & (170.531) & \\  & & & & (3831) & & (177.34) & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Mean values of extracted dataset features of each information category, and bold values indicates the highest values in that specific column. 1. Emotional-related features: effect, positive emotion (posemo), negative emotion (negemo), anxiety (aux), anger and sadness (sad) words in the posts. 2. Perception type features: perception, seeing, hearing and feeling in the posts. 3. Affiliation type features: driving, affiliation, achievements, power, rewards and risk in the posts. 4. Cognitive processes features: certainty(certain) and differentiation(diff). 5. User-related features: if users are verified or not, followers (log) amount,following (log) amount and NearState. 6. Content-related features: length of the post (word count), number of retweets (log) and likes on the posts. Specifically, the linguistic information features are extracted from Linguistic Inquiry and Word Count (LIWC); this is the mostly used software created by researchers [54]. It is used to extract the linguistic information from text related to different psychological factors [55]. It reveals the psychological, negative, emotional, and cognitive perspectives that people describe in the text data.