  Leveraging Large Multimodal Models (LMMs) to simulate human behaviors when
processing multimodal information, especially in the context of social media,
has garnered immense interest due to its broad potential and far-reaching
implications. Emojis, as one of the most unique aspects of digital
communication, are pivotal in enriching and often clarifying the emotional and
tonal dimensions. Yet, there is a notable gap in understanding how these
advanced models, such as GPT-4V, interpret and employ emojis in the nuanced
context of online interaction. This study intends to bridge this gap by
examining the behavior of GPT-4V in replicating human-like use of emojis. The
findings reveal a discernible discrepancy between human and GPT-4V behaviors,
likely due to the subjective nature of human interpretation and the limitations
of GPT-4V's English-centric training, suggesting cultural biases and inadequate
representation of non-English cultures.
