Date of Award

August 2021

Degree Type

Thesis

Degree Name

Master of Science

Department

Computer Science

First Advisor

Hossein Hosseini

Second Advisor

Purushottam Papatla

Committee Members

Amol Mali

Keywords

COVID-19, Mask, Sentiment Analysis, Topic Modeling, Vaccine, Vader

Abstract

SARS CoV-2 (COVID-19) was identified as the cause of severe respiratory disease in China in 2019. It is a virus that will be transferred person-to-person by sneezing, coughing, or talking. This phenomenon not only affects public health and economics but also mental health as well. SARS-CoV-2 vaccines and wearing masks plays significant rolesin preventing the spread of the COVID-19 virus, but vaccine hesitancy and anti-mask beliefs threaten the efficacy of the government orders in prevention and immunization against Coronavirus. The impact of the COVID-19 pandemic has been investigated from different aspects, but few large-scale studies focus on the opinion of people toward government orders to wear face mask and get vaccination. The abundant data on online social media however enables researchers to analyze people's attitudes toward vaccination and the use of face mask. In this study, we use Twitter API and scrape 340 million COVID-19 tweets posted in the timeline of December 2020 to March 2021. Our goal is to investigate how people respond to tweets about masking and vaccines as a means of understanding sentiments towards both practices. Specifically, we focus on which tweets about the topics tend to become viral relative to those that are neither retweeted nor receive any replies. Toward this end, we split the dataset into three categories: 1) replied tweets 2) retweeted tweets, and 3) no-engagement tweets which are tweets that receive no response. We then deploy topic modeling to identify the most popular tweet topics in each category. Furthermore, we filter tweets for vaccine and mask related hashtags and use the algorithm,VADER to find the sentiment of these tweets. By applying topic modeling and Vader, we assess the vaccine and mask-related sentiment scores and visualize their progression during four months. Our analysis indicates a slight difference in the distribution of tweets with positive and negative sentiments with vaccination or mask hashtags, with the dominant polarity of positive sentiments. Despite the overall strength of positive stances, negative opinions about COVID-19 vaccines and masks remain among people who are hesitant towards wearing face masks and vaccination. We also investigate and show that sentiments among Twitter users shift from positive to negative and vice versa over time. The most probable reasons for the domination of positive sentiments in tweets with vaccine and mask hashtags, appears to be the belief that such tweets are providing accurate information and also because of the risks of COVID-19 as discussed by well-regarded organizations. At the same time, however, inaccurate information, mistrust of well-regarded organizations or media, and the influence of celebrities on their followers does push a segment of users into hesitancy and negative views about masks and vaccination.

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