The sharp increase in the influencer market on social media generates a huge amount of content, including text, audio, images, and video. However, how these contents are used to leverage consumer engagement is still unclear. In this study, we explored the potential usage of natural language processing and various computer vision models in understanding influencer UGC and marketing analysis. Through studying the influencers’ textual and visual posts on Instagram, our preliminary findings show that different formats of UGC may impact consumer engagement differently. Our work offers new knowledge on exploring suitable analysis tools for studying the influencer market, and the learned insights on content generation could shed light on enhancing marketing effectiveness in the influencer marketing industry.