Abstract: The Coronavirus pandemic has taken the world by storm as also the social media. As the awareness about the ailment increased, so did messages, videos and posts acknowledging its presence. The social networking site, Twitter, demonstrated similar effect with the number of posts related to coronavirus showing an unprecedented growth in a very short span of time. This paper presents a statistical analysis of the twitter messages related to this disease posted since January 2020. Two types of empirical studies have been performed. The first is on word frequency and the second on sentiments of the individual tweet messages. Inspection of the word frequency is useful in characterizing the patterns or trends in the words used on the site. This would also reflect on the psychology of the twitter users at this critical juncture. Unigram, bigram and trigram frequencies have been modeled by power law distribution. The results have been validated by Sum of Square Error (SSE), R2 and Root Mean Square Error (RMSE). High values of R2 and low values of SSE and RMSE lay the grounds for the goodness of fit of this model. Sentiment analysis has been conducted to understand the general attitudes of the twitter users at this time. Both tweets by general public and WHO were part of the corpus. The results showed that the majority of the tweets had a positive polarity and only about 15% were negative.

Introduction: Catastrophic global circumstances have a pronounced effect on the lives of human beings across the world. The ramifications of such a scenario are experienced in diverse and multiplicative ways spanning routine tasks, media and news reports, detrimental physical and mental health, and also routine conversations. A similar footprint has been left by the global pandemic Coronavirus particularly since February 2020. The outbreak has not only created havoc in the economic conditions, physical health, working conditions, and manufacturing sector to name a few but has also created a niche in the minds of the people worldwide. It has had serious repercussions on the psychological state of the humans that is most evident now. One of the best possible mechanisms of capturing human emotions is to analyze the content they post on social media websites like Twitter and Facebook. Not to be surprised, social media is ablaze with myriad content on Coronavirus reflecting facts, fears, numbers, and the overall thoughts dominating the people's minds at this time. This paper is an effort towards analysis of the present textual content posted by people on social media from a statistical perspective. Two techniques have been deployed to undertake statistical interpretation of text messages posted on twitter; first being word frequency analysis and second sentiment analysis. A well known and profoundly researched as well as used statistical tool for quantitative linguistics is word frequency analysis. Determining word frequencies in any document gives a strong idea about the patterns of word used and the sentimental content of the text. The analysis can be carried out in computational as well as statistical settings. An investigation of the probability distribution of word frequencies extracted from the Twitter text messages posted by different users during the coronavirus outbreak in 2020 has been presented. Power law has been shown to capture patterns in the text analysis BIBREF0, BIBREF1. Sentiment analysis is a technique to gauge the sentimental content of a writing. It can help understand attitudes in a text related to a particular subject. Sentiment analysis is a highly intriguing field of research that can assist in inferring the emotional content of a text. Sentiment analysis has been performed on two datasets, one pertaining to tweets by World Health Organization (WHO) and the other tweets with 1000 retweets. The sentimental quotient from the tweets has been deduced by computing the positive and negative polarities from the messages. The paper has been structured as follows. The next section presents a brief overview of some work in the area of word frequency analysis and emergence of power law. Section 3 details the analysis of the twitter data. Section 4 provides a discussion on the obtained results. Section 5 provides a discussion on the scope of sentiment analysis and outlines the methodology of sentiment analysis adopted in this paper. Section 6 presents the results of the sentiment analysis performed on the two datasets mentioned above. The final section concludes the paper.

Question: How do they measure correlation between the prediction and explanation quality?
Answer: They look at the performance accuracy of explanation and the prediction performance