## 3 Proposed Framework
### Data Pre-Processing
#### 3.2.4 Machine Learning Classifiers
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Names and Definitions** & **Manual Label** & **Counts** \\ \hline Precautions and care: precautions from the public healthcare authorities to explain the pace of the epidemic, such the need to pay attention to different aspects of the containment measure, such as going out much less, using sanitizers to wash hands, wearing masks in public and responding to the government announcements. & 0 & 199 \\ \hline Announcements or Measures: pandemic announcements such as hospital conditions, the number of cases (recovered, infected or dead), measures taken by health departments, medical equipment reserves and the city and state wise tally of cases. & 2 & 63 \\ \hline Donated money, goods or services: donations from governments, government-relevant authorities who want to donate goods, money, or services for pandemic prevention and control, healthcare NGO- and health- related volunteer services announcements of donations are also include in this category. & 2 & 63 \\ \hline Emotional support to victims: shows of sympathy by public medical teams and health organizations who are supporting people in the US. & 3 & 199 \\ \hline Help Seeking: (a) medical institutions, public health care authorities, individuals, etc. seeking support such as demanding human resources in the form of medical workers and individuals seeking medical aid kits, virus test kits etc. (b) Patients want emotional support such as those seeking comfort and who express depression, etc. & 4 & 279 \\ \hline Criticizing authorities: criticizing or questioning the government on their performance in handling the pandemic so far, questioning the government’s initiatives or criticizing members of the public who mislead others such as blind supporters of specific political parties, etc. & 6 & 551 \\ \hline Non Situational Information: information that does not fall into any of the above stated and defined categories is classified as non situational information. & 6 & 551 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Category Names, definitions, manual label numbers, and number of tweets of each category. the supervised algorithms, the support vector machine (with radial basis kernel) performs better in terms of classification performance, compared to the other classifiers applied to the same dataset; therefore, we chose this classifier to automatically label the remaining dataset, using 3000 sampled labeled data. We summarized the six attributes of every type of information in Table 3 to represent what it actually is the public need generally. The attributes include the total (average) number of tweets, the verified user amount (the proportion of verified users), the total (average) amount of followers and following of the users who posted tweets, the total (average) number of retweets of tweets, and the total (average) amount of likes on tweets in each type of information. If we analyze the average amounts, we can see that, during the COVID-19 pandemic, the verified users were mostly involved in dissemination of Type 1 information (notifications and measures), so verified users dominated in the context of this type of information.