Data mining twitter for COVID-19 sentiments concerning college online education

Abstract

In the last decade there has been a large increase in corporate and public reliance on social media for information, rather than on the traditional news and information sources such as print and broadcast media. People freely express their views, moods, activities, likes/dislikes on social media about diverse topics. Rather than surveys and other structured data gathering methods, text data mining is now commonly used by businesses to go through their unstructured text in the form of emails, blogs, tweets, likes, etc. to find out how their customers feel about their company and their products/services. This paper reports upon a study using Twitter (recently renamed to “X”) data to determine if meaningful and actionable information could be gained from such social media data in regard to pandemic issues and how that information compares to a traditional survey. In early 2020, the COVID-19 pandemic hit and forced colleges to move classes to an online format. While there is considerable literature in regard to using social media to communicate geo-political issues and in particular pandemics, there is not a study using social media to explore public sentiment in regard to COVID’s forcing online education upon the public. In this study, text data mining was used to gain some insight into the feeling of Twitter users in regard to the effect of COVID-19 and the switch to online education in colleges. This study found that Twitter data mining did produce actionable information similar to the traditional survey, and the study is important since its results may influence organizations to explore the use of Twitter (and possibly other social media) to obtain people’s sentiments instead of (or in addition to) traditional surveys and other traditional means of gathering such information. This paper demonstrates both the process of text data mining social media and its application to current real-world issues.