Analysis of Twitter content related to Covid-19 vaccination

BSc. Addiskidan Tegegn

Master's thesis

Abstract:

This thesis examines the use of natural language processing (NLP) techniques to analyze tweets related to COVID-19 vaccination on Twitter. The study aims to identify the sentiments, topics, and opinions expressed by Twitter users regarding the COVID-19 vaccine, and to assess the impact of these expressions on public health communication efforts. Using a dataset of over one million tweets related to COVID-19 vaccination, the study employs various NLP techniques, including sentiment analysis, topic modelling, and opinion mining. The results show that there is a wide range of sentiments and opinions expressed on Twitter regarding the COVID-19 vaccine, with both positive and negative sentiments present. The study also identifies several key topics that are frequently discussed, including vaccine safety, vaccine efficacy, vaccine hesitancy, and vaccine mandates. The findings of the study highlight the potential of NLP techniques in analysing social media data to gain insights into public opinions and attitudes towards the COVID-19 vaccine. The results can help inform public health communication strategies aimed at addressing vaccine hesitancy and increasing vaccine uptake. Additionally, the study provides valuable insights for policymakers, healthcare professionals, and researchers who seek to understand the impact of social media on public health communication efforts

Keywords

COVID 19, Twitter, Vaccination, sentiment analysis

Jazyk práce: English

Datum vytvoření / odevzdání či podání práce: 3. 4. 2023

Obhajoba závěrečné práce

• Obhajoba proběhla 23. 5. 2023
• Vedúci: Ing. Miloš Ulman, Ph.D.
• Oponent: Dino Sodamin, externi

Czech University of Life Sciences Prague

Faculty of Economics and Management

Master programme:
Informatics