Identifying stances, factors, and argumentative traps on vaccine discourse through Tweets

The primary aim of the research is to study vaccine discourse on social media platform through an argumentation theory perspective. The thesis utilizes digital methods like a digital self-ethnography to create a dataset that is intended to be broken down and studied using argumentation theory. Rising anti-vaccination content on Twitter (X) has led to the need to develop models for identifying anti-vaccine tweets to potentially curb misinformation and vaccine hesitancy. (NLP) Natural Language Processing models like the Bidirectional Encoder Representations from Transformers or BERT can process large datasets of over 20,000 Tweets and accurately identify the stances (