Social Media Mining with Dynamic Clustering: A Case Study by COVID-19 Tweets

Recently Social Networking Service (SNS) is used extensively due to proliferation of the Internet and cheaper, compact, easy to use computing devices. Texting, especially via Twitter, is very popular among people of all ages all over the world, and enormous text data is generated regularly which contains various types of information, rumors, sentimental expressions etc. The variety of topics related to the contents of the social media data are prone to changes with the passing of time and sometimes fade out completely after a certain time. Such time varying topics may include beneficial information that could be used for various decision making by general public as well as governmental organization. Especially for the recent pandemic of COVID-19, extraction and visualization of the changing needs of people might help them making some better countermeasures. In this study, COVID-19 related tweets have been collected and analyzed in units of time (hour, day and month) by means of various clustering models to visualize the dynamic changes of topics with time. It is found that Sentence-Bert is the most effective tool among the techniques used here though it is not yet enough for clear understanding of the topics semantically.