Physical Sciences and Mathematics Commons^™

Exploring The Use Of Social Media To Infer Relationships Between Demographics, Psychographics And Vaccine Hesitancy, Abhimanyu Kapur

Computer Science Senior Theses

The growing popularity of social media as a platform to obtain information and share one's opinions on various topics makes it a rich source of information for research. In this study, we aimed to develop a framework to infer relationships between demographic and psychographic characteristics of a user and their opinion on a specific narrative - in this case, their stance on taking the COVID-19 vaccine. Twitter was the chosen platform due to the large USA user base and easily available data. Demographic traits included Race, Age, Gender, and Human-vs-Organization Status. Psychographic traits included the Big Five personality traits (Conscientiousness, …

Public Discourse Against Masks In The Covid-19 Era: Infodemiology Study Of Twitter Data, Mohammad A. Al-Ramahi, Ahmed El Noshokaty, Omar El-Gayar, Tareq Nasralah, Abdullah Wahbeh

Computer Information Systems Faculty Publications

Background:

Despite scientific evidence supporting the importance of wearing masks to curtail the spread of COVID-19, wearing masks has stirred up a significant debate particularly on social media.

Objective:

This study aimed to investigate the topics associated with the public discourse against wearing masks in the United States. We also studied the relationship between the anti-mask discourse on social media and the number of new COVID-19 cases.

Methods:

We collected a total of 51,170 English tweets between January 1, 2020, and October 27, 2020, by searching for hashtags against wearing masks. We used machine learning techniques to analyze the data …

Analyzing Tweets On New Norm: Work From Home During Covid-19 Outbreak, Swapna Gottipati, Kyong Jin Shim, Hui Hian Teo, Karthik Nityanand, Shreyansh Shivam

Research Collection School Of Computing and Information Systems

The COVID-19 pandemic triggered a large-scale work-from-home trend globally in recent months. In this paper, we study the phenomenon of “work-from-home” (WFH) by performing social listening. We propose an analytics pipeline designed to crawl social media data and perform text mining analyzes on textual data from tweets scrapped based on hashtags related to WFH in COVID-19 situation. We apply text mining and NLP techniques to analyze the tweets for extracting the WFH themes and sentiments (positive and negative). Our Twitter theme analysis adds further value by summarizing the common key topics, allowing employers to gain more insights on areas of …