Text Analysis of Evolving Emotions and Sentiments in COVID-19 Twitter Communication

Abstract

Scientists and regular citizens alike search for ways to manage the widespread effects of the COVID-19 pandemic. While scientists are busy in their labs, other citizens often turn to online sources to report their experiences and concerns and to seek and share knowledge of the virus. The text generated by those users in online social media platforms can provide valuable insights about evolving users' opinions and attitudes. The objective of this research is to analyze text of such user disclosures to study human communication during a pandemic in four primary ways. First, we analyze Twitter tweet information, generated throughout the pandemic, to understand users' communications concerning COVID-19 and how those communications have evolved during the pandemic. Second, we analyze linguistic sentiment concepts (analytic, authentic, clout, and tone concepts) in different Twitter settings (sentiment in tweets with pictures or no pictures and tweets versus retweets). Third, we investigate the relationship between Twitter tweets with additional forms of internet activity, namely, Google searches and Wikipedia page views. Finally, we create and use a dictionary of specific COVID-19-related concepts (e.g., symptom of lost taste) to assess how the use of those concepts in tweets are related to the spread of information and the resulting influence of Twitter users. The analysis showed a surprisingly lack of emotion in the initial phases of the pandemic as people were information seeking. As time progressed, there were more expressions of sentiment, including anger. Further, tweets with and without pictures and/or video had statistically significant differences in text sentiment characteristics. Similarly, there were differences between the sentiment in tweets and retweets and tweets. We also found that Google and Wikipedia searches were predictive of sentiment in the tweets. Finally, a variable representing a dictionary of COVID-related concepts was statistically significant when related to users' Twitter influence score and number of retweets, illustrating the general impact of COVID-19 on Twitter and human communication. Overall, the results provide insights into human communication as well as models of human internet and social media use. These findings could be useful for the management of global challenges beyond, or different from, a pandemic.

Keywords: COVID-19; Coronavirus; Coronavirus dictionary; Google; Human communication; Linguistic Inquiry and Word Count (LIWC); Pandemic; Scherer’s ontology; Sentiment analysis; Social media use; Text analysis; Twitter; Wikipedia.