Social media influencers, those with verified accounts or with more than 10,000 followers, played a crucial role in the propagation of narratives during the COVID-19 pandemic. We investigate their impact by characterizing and contrasting the differences in content patterns between influential individuals versus public organizations during the pandemic, analyzing emotions, sentiments, and scientific claims expressed in their Tweets. Advanced machine learning approaches, including customized transformer models, few-shot learning, and large language models such as GPT-3.5, were used. The findings reveal a stark contrast in sentiment usage across sub-domains like vaccines and lockdowns, with organizations predominantly employing neutral tones while individuals displaying a significant negative sentiment bias. Individuals often conveyed more negative emotions, whereas organizations exhibited greater optimism. However, many claims from both groups were not verified, highlighting the need to combat misinformation.