  Recent progress in generative artificial intelligence (gen-AI) has enabled
the generation of photo-realistic and artistically-inspiring photos at a single
click, catering to millions of users online. To explore how people use gen-AI
models such as DALLE and StableDiffusion, it is critical to understand the
themes, contents, and variations present in the AI-generated photos. In this
work, we introduce TWIGMA (TWItter Generative-ai images with MetadatA), a
comprehensive dataset encompassing over 800,000 gen-AI images collected from
Jan 2021 to March 2023 on Twitter, with associated metadata (e.g., tweet text,
creation date, number of likes), available at
https://zenodo.org/records/8031785. Through a comparative analysis of TWIGMA
with natural images and human artwork, we find that gen-AI images possess
distinctive characteristics and exhibit, on average, lower variability when
compared to their non-gen-AI counterparts. Additionally, we find that the
similarity between a gen-AI image and natural images is inversely correlated
with the number of likes. Finally, we observe a longitudinal shift in the
themes of AI-generated images on Twitter, with users increasingly sharing
artistically sophisticated content such as intricate human portraits, whereas
their interest in simple subjects such as natural scenes and animals has
decreased. Our findings underscore the significance of TWIGMA as a unique data
resource for studying AI-generated images.
