Generative anime style football videos show uneven attention and reuse
The 2026 World Cup, Anime Edition: Production, Engagement, and Circulation of Generative Memetic Sports Content
Social and Information Networks
Summary
This paper looks at how generative AI lets people create anime-style videos featuring football players, mixing sports and popular anime worlds. The authors found that some players and anime shows get much more attention than others. They also discovered that many videos are reused and shared between accounts, but newer posts tend to get less engagement. Overall, the study shows how creating and sharing these videos is shaped by popular characters and repeated content, rather than by broad cultural diversity.
What this means in practice
- •For social media marketers: Identify which sports and anime characters attract the most engagement to tailor visual content strategies on platforms like Instagram.
- •For digital content creators: Understand how video content reuse impacts audience engagement to optimize timing and originality of generative AI creations.
Authors
Azneef Chowdhury, Ho-Chun Herbert Chang
Abstract
Generative AI gives audiences new ways to portray real sporting figures through familiar fictional worlds. Expanded opportunities to create, however, need not broaden cultural representation or distribute attention evenly. We examine this tension through the supply, demand, and circulation of anime style football videos on Instagram. Using 2,021 posts and associated video and account data, we combine caption entity extraction, human-reviewed visual identity clustering, engagement analysis, and content-reuse networks. Representation is uneven across athletes, while Blue Lock, Naruto, and One Piece account for 60.1% of explicit franchise mentions. Among posts referencing generative tools, rankings by total engagement differ from rankings by average engagement per video, distinguishing aggregate visibility from response to individual posts. Reuse is extensive: 772 of 1,548 evaluated videos belong to 124 detected groups, including 71 spanning multiple known accounts. Shared material connects a large account component alongside smaller groups, and later observed uploads receive lower median normalized engagement than first observed appearances. These findings characterize generative cultural crossover as a practice organized around concentrated cultural resources, uneven attention, and extensive recirculation. More broadly, they distinguish opportunities for visual authorship from the processes through which particular representations become widely visible.