Generative predictive modeling for audience-to-curator personal branding trajectories in participatory digital curation

Various social media platforms have offered digital spaces for every online user to express themselves and interact with others. In the participatory digital culture that consisted of a large number of interactive behaviors, some users have successfully made the transition from audience to curator. This transformation has become a typical trajectory of personal branding. Based on this path, this study proposes a generative predictive framework that integrates behavioral data modeling with trajectory inference to simulate individual brand development from passive consumption to active curatorship. We explored the relationship between participation, creative content, and branding construction by using Convolutional Neural Networks (CNNs), Transformers, and temporal modeling. By training on data from various platforms, the model can, to a certain extent, predict the development path of individuals from audience to curator, and demonstrate its adaptability across different platforms, which provides ordinary users a viable strategic reference to gain influence online and make true the personal branding.