Prediction of "Forwarding Whom" Behavior in Information Diffusion

TL;DR

This paper models and predicts individual forwarding choices in social media information diffusion, achieving over 91% accuracy by integrating structural, temporal, historical, and content features.

Contribution

It introduces a novel approach to predict 'forwarding whom' behavior considering multiple features, advancing understanding of message spread in social networks.

Findings

Achieved 91.3% prediction accuracy.

Effective integration of multiple feature types.

Enhanced understanding of forwarding behavior.

Abstract

Follow-ship network among users underlies the diffusion dynamics of messages on online social networks. Generally, the structure of underlying social network determines the visibility of messages and the diffusion process. In this paper, we study forwarding behavior of individuals, taking Sina Weibo as an example. We investigate multiple exposures in information diffusion and the "forwarding whom" problem associated with multiple exposures. Finally, we model and predict the "forwarding whom" behavior of individuals, combining structural, temporal, historical, and content features. Experimental results demonstrate that our method achieves a high accuracy 91.3%.