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Recent progress in generative artificial intelligence (gen-AI) has enabledthe generation of photo-realistic and artistically-inspiring photos at a singleclick, catering to millions of users online. To explore how people use gen-AImodels such as DALLE and StableDiffusion, it is critical to understand thethemes, contents, and variations present in the AI-generated photos. In thiswork, we introduce TWIGMA (TWItter Generative-ai images with MetadatA), acomprehensive dataset encompassing over 800,000 gen-AI images collected fromJan 2021 to March 2023 on Twitter, with associated metadata (e.g., tweet text,creation date, number of likes), available athttps://zenodo.org/records/8031785. Through a comparative analysis of TWIGMAwith natural images and human artwork, we find that gen-AI images possessdistinctive characteristics and exhibit, on average, lower variability whencompared to their non-gen-AI counterparts. Additionally, we find that thesimilarity between a gen-AI image and natural images is inversely correlatedwith the number of likes. Finally, we observe a longitudinal shift in thethemes of AI-generated images on Twitter, with users increasingly sharingartistically sophisticated content such as intricate human portraits, whereastheir interest in simple subjects such as natural scenes and animals hasdecreased. Our findings underscore the significance of TWIGMA as a unique dataresource for studying AI-generated images.

TWIGMA: A dataset of AI-Generated Images with Metadata From Twitter