Designed to Spread: A Generative Approach to Enhance Information Diffusion

TL;DR

This paper introduces a novel generative approach called DOCG that automatically creates audience-tailored content optimized for virality, significantly enhancing information diffusion on social media without needing network topology data.

Contribution

It proposes the first framework for generating content specifically designed to maximize diffusion, using reinforcement learning and influence indicators for effective, interpretable content editing.

Findings

Improves diffusion effectiveness on real-world datasets

Generates semantically faithful, audience-aware content

Operates without access to network topology

Abstract

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.