## 7 Conclusions and Future Work  
Identifying the situational information during natural disasters is the main aim of authorities and practitioners. The unparalleled use of social media by the public provides opportunities for authorities and data scientists to leverage social media platforms to analyze and enhance their response. We combined machine learning algorithms and natural language techniques to take advantage of this opportunity. In this work, we leveraged Twitter data to classify the information into situational information categories. We employed different ML classifiers and the highest accuracy achieved was 77%, and the lowest performing classifiers achieved 54% accuracy; the best performing classifier (SVM) was then used to label the remaining datasets, after which we identified different linguistic, content and user features, then predicted the number of retweets to characterize the propagation scale of situational information by using the selected features. We observed that emotional features have negative correlations with the number of retweets for almost all types of information. User, cognitive and content features have positive relations with the propagation scale of the number of retweets.  
The data-oriented insights from this article indicate the need for utilizing information publishing strategies for different types of situational information. The authorities could also learn how to manage the COVID-19-related posts to enlarge or decrease the number of retweets of their posts using the selected feature results. Hence, practitioners can use this study to develop information dissemination strategies during the COVID-19 pandemic. This novel approach to applying machine learning classifiers and regression algorithms to multiple categories of the same data paved a new way to analyze the data of this nature.  
The first limitation of this approach is relatively a medium-sized dataset. Another is the fact that we only trained machine learning classifiers that give achievable results; however, in future we will use more deep learning methods and apply them to a bigger datasets to achieve better results in terms of accuracy by collecting more data from other countries as well [58]. Human-annotated labels give more assured results, but it takes time, so in future we will apply automatic labeling to our situational information data to achieve better efficiency in terms of time cost and better annotation. The results support this new framework of identifying situational information categories, thereby indicating the necessity of extending the study to different types of natural disasters such as floods, earthquakes, hurricanes, other languages and other countries.  
Conceptualization, J.A.W. and Y.G. and L.S.; methodology, J.A.W., Y.G. and L.S.; investigation, J.A.W. and S.H.; validation, B.Y., Y.T. and L.W.; writing--original draft preparation, J.A.W. and L.S.; writing-- review and editing, Y.G., B.Y. and L.W.; visualization--charts and tables, J.A.W., S.H., Y.T.; supervision, L.S., Y.G.; funding acquisition, L.S.; resources, B.Y., L.W., Y.T. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by National Key R&D Program of China 2018 and Key Scientific and Technological Research Projects in Henan Province of China under grant number 192102310216. This work was supported in part by the National Key Technologies R&D Program (2020 YFB1712401, 2018 YFB1701401), in part by the Nature Science Foundation of China (62006210), and in part by the major project of Zhengzhou Collaborative Innovation (20 XTZX-009, 20 XTZX-X010). the National Key R&D Program of China (2018********* 02 ).  
Not applicable.  
The authors declare no conflict of interest.