CasFT: Future Trend Modeling for Information Popularity Prediction with Dynamic Cues-Driven Diffusion Models

TL;DR

CasFT introduces a novel diffusion model guided by neural ODE-extracted dynamic cues to predict future information popularity trends, significantly enhancing prediction accuracy over existing methods.

Contribution

The paper presents CasFT, a new approach that models future popularity trends using observed cascades and neural ODE-driven dynamic cues within a diffusion framework.

Findings

CasFT outperforms state-of-the-art methods with 2.2%-19.3% accuracy improvement.

Extensive experiments validate the effectiveness of the proposed approach.

Dynamic cues effectively guide future trend generation in popularity prediction.

Abstract

The rapid spread of diverse information on online social platforms has prompted both academia and industry to realize the importance of predicting content popularity, which could benefit a wide range of applications, such as recommendation systems and strategic decision-making. Recent works mainly focused on extracting spatiotemporal patterns inherent in the information diffusion process within a given observation period so as to predict its popularity over a future period of time. However, these works often overlook the future popularity trend, as future popularity could either increase exponentially or stagnate, introducing uncertainties to the prediction performance. Additionally, how to transfer the preceding-term dynamics learned from the observed diffusion process into future-term trends remains an unexplored challenge. Against this background, we propose CasFT, which leverages observed information Cascades and dynamic cues extracted via neural ODEs as conditions to guide the generation of Future popularity-increasing Trends through a diffusion model. These generated trends are then combined with the spatiotemporal patterns in the observed information cascade to make the final popularity prediction. Extensive experiments conducted on three real-world datasets demonstrate that CasFT significantly improves the prediction accuracy, compared to state-of-the-art approaches, yielding 2.2%-19.3% improvement across different datasets.