Analyzing the Impact of Emotional Content in Tweets on User Engagement and Online Behavior

The widespread adoption of social media platforms has provided a valuable source of data for understanding public sentiment and engagement towards various topics, including healthcare and vaccination. In this paper, we analyze the impact of sentiment in tweets related to COVID-19 vaccination in Saudi Arabia on user engagement metrics, such as likes, replies, and retweets. We t using web crawling techniques and applied sentiment analysis using BERT (Bidirectional Encoder Representations from Transformers) transformer models. Descriptive analysis of the data revealed the distribution of sentiment labels and user engagement metrics. We found that positive sentiment was associated with higher average likes and replies, while neutral sentiment showed a lower engagement level. Furthermore, we conducted statistical tests, including ANOVA (Analysis of Variance), to determine significant differences in user engagement based on sentiment labels. The results indicated a significant difference in likes count among different sentiment categories. This study provides insights into tailoring messaging strategies, monitoring sentiment trends, and collaborating with influencers to enhance the success of vaccination campaigns. The results emphasize the need for incorporating sentiment analysis into public health strategies to effectively engage the population and promote vaccination acceptance.