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). 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 (i) is inversely correlated with the number of likes; and (ii) can be used to identify human images that served as inspiration for the gen-AI creations. 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 analyses and findings underscore the significance of TWIGMA as a unique data resource for studying AI-generated images.

翻译：近年来，生成式人工智能（gen-AI）的进展使得只需点击一下即可生成照片级逼真且富有艺术感染力的图像，满足了数百万在线用户的需求。为了探究用户如何使用DALLE和StableDiffusion等gen-AI模型，理解AI生成图像所呈现的主题、内容及多样性至关重要。本文介绍了TWIGMA（Twitter Generative-AI Images with Metadata）——一个包含从2021年1月至2023年3月在Twitter上收集的超过80万张gen-AI图像的综合数据集，并附有相关元数据（例如推文文本、创建日期、点赞数）。通过将TWIGMA与自然图像及人类艺术作品进行对比分析，我们发现gen-AI图像具有独特特征，且总体上其变异性低于非gen-AI图像。进一步研究发现，gen-AI图像与自然图像之间的相似性（i）与点赞数呈负相关；（ii）可用于识别作为gen-AI创作灵感来源的人类图像。最后，我们观察到Twitter上AI生成图像的主题存在纵向变化：用户越来越多地分享诸如复杂人物肖像等艺术性更丰富的内容，而对自然场景、动物等简单主题的兴趣则有所下降。我们的分析与发现强调了TWIGMA作为研究AI生成图像的独特数据资源的重要性。