###### Abstract  
During the recent pandemic of COVID-19, an increasing amount of information has been propagated on social media. This situational information is valuable for public authorities. Therefore, this study characterized the propagation scale of situational information types by harnessing the power of natural language processing techniques and machine learning algorithms. We observed that the length of the post has a positive correlation with type 1 information (announcements), and negative words were mostly used in type 5 information (criticizing the government), whereas anxiety-related words have a negative effect on the amount of retweeted type 0 (precautions) and type 2 (donations) information. This type of research study not only contributes to the situational information literature by comprehensively defining categories but also provides data-oriented practical insights into information so that management authorities can formulate response strategies after the pandemic. Our approach is one of its kind and combines Twitter content features, user features and LIWC linguistic features with machine learning algorithms to analyze the propagation scale of situational information, and it achieved 77% accuracy with SVM while classifying the information categories. social media analysis; machine learning; natural language processing; Twitter; text analytic; COVID-19; situational information +
Footnote †: journal: Medical  
SVM algorithm [5] to forecast daily precipitation in Turkey. During emergency situations such as the COVID-19 pandemic, people look for real time and fast information; therefore, they tend to use modern world communication platforms such as social media to obtain information [6]. They want real-time situational information through social media about the crisis going on, so they learn about the situation and obtain information about what steps the government is taking in response to the crisis. In the social computing perspective, on social networks, there is a lot of information of different nature such as public opinion, breaking news about any events or disasters, public health-related information and also situation-related information which assist the authorities to understand the situation and find out ways to solve and fulfil public needs [7; 8]. Researchers in [9] developed a framework to extract and analyze the public sentiments for different events on the basis of various type of communities. The situation-related information includes information about certain situations such as health announcements, donations, help needed etc. [10; 11]. To detect these types of situational information and determine their propagation scale can help the relevant government authorities to assist them in decision making in response to COVID-19. By identifying these types of situational information and determining their propagation scale, relevant authorities and agencies can sense the public sentiments and take proper action on that [12]. There is no such proper agreement on situational information definition; some researchers such as in [13] they defined it as help-seeking, some identified it as emotional support but they ignore the other aspects of information like public criticism that implies the public concern and emotional support that is related to empathy for affected people, because the public gives an opinion in forms of criticism whether it be positive or negative, and also public gives opinion related to empathy by showing emotional support for others [14]. Therefore, it is important to properly define the situation-related information. For example, information about criticism can assist the relevant authorities to respond quickly by analyzing the public sentiments about the situation. Emotional support-type information could assist them to understand the social media users' usage and take advantage of social services such as voluntary services by social media users [15].