Designed to Spread: A Generative Approach to Enhance Information Diffusion
This addresses the challenge of enhancing information diffusion for social media users and platforms, though it appears incremental as it builds on prior work in content generation and diffusion.
The paper tackles the problem of automatically generating content optimized for virality on social media by proposing a novel task (DOCG) and an information enhancement algorithm, with experiments showing significant improvements in diffusion effectiveness while preserving core semantics.
Social media has fundamentally transformed how people access information and form social connections, with content expression playing a critical role in driving information diffusion. While prior research has focused largely on network structures and tipping point identification, it provides limited tools for automatically generating content tailored for virality within a specific audience. To fill this gap, we propose the novel task of DOCG and introduce an information enhancement algorithm for generating content optimized for diffusion. Our method includes an influence indicator that enables content-level diffusion assessment without requiring access to network topology, and an information editor that employs reinforcement learning to explore interpretable editing strategies. The editor leverages generative models to produce semantically faithful, audience-aware textual or visual content. Experiments on real-world social media datasets and user study demonstrate that our approach significantly improves diffusion effectiveness while preserving the core semantics of the original content.