FITMag: A Framework for Generating Fashion Journalism Using Multimodal LLMs, Social Media Influence, and Graph RAG

As generative artificial intelligence (AI) reshapes the landscape of media and communication, its integration with social media opens new possibilities for human-centered content creation. In the field of fashion journalism, which relies heavily on style, nuance, and visual culture, we present FITMag, a framework that combines multimodal large language models (LLMs) with real-time social media influence and graph data to generate fashion articles approaching professional quality.FITMag builds on the FITNet and FITViz datasets, which identify fashion influencer subgraphs on Twitter. It uses multimodal inputs including influencer metadata, retweets, mentions, hashtag trends (such as #NYFW and #sustainability), and image content to create structured prompts for both text and image generation. Leading LLMs such as ChatGPT, Claude, DeepSeek, and LLaMA are paired with Stable Diffusion to generate content in three primary formats: event-driven articles, niche community pieces, and trend-based narratives. Graph Retrieval-Augmented Generation (Graph RAG) is used to enhance contextual alignment by connecting influencer activity with fashion discourse.To assess FITMag’s effectiveness, we conducted a human-centered study with 15 fashion professionals including editors, stylists, bloggers, and researchers. Participants evaluated 52 fashion articles using 5-point Likert scales across three dimensions: authenticity, coherence, and style. They also completed a blind identification task to determine whether each article was human-written or generated by AI. Quantitative results show that GPT-4o with FITNet data achieved the highest overall performance among AI models, closely matching human-written content in stylistic quality. Participants frequently misclassified AI-generated text as human-written, especially when produced by GPT-4o and Claude, suggesting strong perceived realism. However, vision and language alignment remained a limitation. Participants observed that AI-generated images sometimes lacked contextual relevance or omitted recognizable influencers due to licensing restrictions.These findings highlight both the capabilities and current limitations of multimodal systems. While AI-generated articles can reach professional-level quality in text, challenges in image and text coherence persist. FITMag contributes to ongoing conversations about AI-assisted journalism by integrating social influence data, generative models, and user-centered evaluation. The research provides insight into how AI can support rather than replace human creativity in fashion media.Ultimately, FITMag serves as a testbed for studying AI and human collaboration in social media contexts. It offers practical tools and theoretical foundations for designing future systems that balance generative power, editorial integrity, and cultural sensitivity across digital platforms.