Predicting Trends in Social Networks via Dynamic Activeness Model

TL;DR

This paper introduces a Dynamic Activeness model to predict social network trends, effectively capturing intensity, coverage, and duration, and demonstrates improved accuracy over existing methods on real datasets.

Contribution

The paper proposes a novel DA model based on activeness to predict social trends, addressing limitations of previous approaches in capturing trend characteristics.

Findings

The DA model accurately predicts trend dynamics in real social networks.

The prediction algorithm outperforms state-of-the-art approaches in accuracy.

The method is computationally efficient due to the stacking principle.

Abstract

With the effect of word-of-the-mouth, trends in social networks are now playing a significant role in shaping people's lives. Predicting dynamic trends is an important problem with many useful applications. There are three dynamic characteristics of a trend that should be captured by a trend model: intensity, coverage and duration. However, existing approaches on the information diffusion are not capable of capturing these three characteristics. In this paper, we study the problem of predicting dynamic trends in social networks. We first define related concepts to quantify the dynamic characteristics of trends in social networks, and formalize the problem of trend prediction. We then propose a Dynamic Activeness (DA) model based on the novel concept of activeness, and design a trend prediction algorithm using the DA model. Due to the use of stacking principle, we are able to make the prediction algorithm very efficient. We examine the prediction algorithm on a number of real social network datasets, and show that it is more accurate than state-of-the-art approaches.