IAD: Interaction-Aware Diffusion Framework in Social Networks

TL;DR

This paper introduces IAD, a framework that models interactions among users, contagion contents, and sentiments in social networks to improve diffusion predictions and understanding.

Contribution

It develops an interaction-aware diffusion model incorporating user, content, and sentiment interactions, enhancing prediction accuracy and interpretability.

Findings

Outperforms state-of-the-art baselines in prediction accuracy.

Effectively learns complex interactions in large-scale social data.

Provides insights into how interactions influence information spread.

Abstract

In networks, multiple contagions, such as information and purchasing behaviors, may interact with each other as they spread simultaneously. However, most of the existing information diffusion models are built on the assumption that each individual contagion spreads independently, regardless of their interactions. Gaining insights into such interaction is crucial to understand the contagion adoption behaviors, and thus can make better predictions. In this paper, we study the contagion adoption behavior under a set of interactions, specifically, the interactions among users, contagions' contents and sentiments, which are learned from social network structures and texts. We then develop an effective and efficient interaction-aware diffusion (IAD) framework, incorporating these interactions into a unified model. We also present a generative process to distinguish user roles, a co-training method to determine contagions' categories and a new topic model to obtain topic-specific sentiments. Evaluation on large-scale Weibo dataset demonstrates that our proposal can learn how different users, contagion categories and sentiments interact with each other efficiently. With these interactions, we can make a more accurate prediction than the state-of-art baselines. Moreover, we can better understand how the interactions influence the propagation process and thus can suggest useful directions for information promotion or suppression in viral marketing.