Analyzing the Privacy Concerns of Contact Tracing Apps - Natural Language Processing Study Based on Sentiment Analysis and Structural Topic Modeling

The recent COVID-19 pandemic has made us realize how important contact tracing apps plays an important role in controlling health crises. Therefore, it is important for app makers, marketers, and government officials to understand the common people's perception of contact tracing apps. In this research paper, we have analyzed the privacy concerns shared by social media users regarding contact tracing apps. Social media posts discussing privacy concerns of contact tracing apps were used for this study. In part one, sentiment analysis was done to analyze the general public's opinions while discussing privacy issues in contact tracing apps. In part two, the structural topic modeling technique was used to analyze the unique concerns and aspects shared by the general public regarding the privacy issue in contact tracing apps. Fear of data leakage and the necessity of contact tracing were the two main issues voiced out by social media users while discussing privacy. The majority of the sentiments regarding privacy concerns about contact tracing apps were neutral. This research is the first to use novel structural topic modeling in understanding the privacy concerns shared by social media users regarding contact tracing apps