This study investigates how genre preferences and sentiment influence fanfiction popularity across multiple languages, focusing on English, Mandarin, Russian, and Spanish datasets. Leveraging advanced natural language processing techniques, including multilingual sentiment analys
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This study investigates how genre preferences and sentiment influence fanfiction popularity across multiple languages, focusing on English, Mandarin, Russian, and Spanish datasets. Leveraging advanced natural language processing techniques, including multilingual sentiment analysis, genre classification, and topic modeling, this research explores the interplay between cultural and linguistic factors in storytelling. Preprocessing steps, such as translation and named entity recognition, ensured consistency and reduced noise across the multilingual dataset. Key findings reveal cross-linguistic patterns, such as the popularity of genres like Alter- nate Universe and Romance, alongside cultural distinctions in sentiment and engagement. This work contributes to computational fan studies by demonstrating how linguistic and cultural factors influence storytelling trends and audience preferences in fanfiction.