Paper Info
Title
Exploring The Collective Human Behavior In Cascading Systems: A Comprehensive Framework
Abstract
The collective behavior describing spontaneously emerging social processes and events is ubiquitous in both physical society and online social media. The knowledge of collective behavior is critical in understanding and predicting social movements, fads, riots, and so on. However, detecting, quantifying, and modeling the collective behavior in cascading systems at large scale are seldom explored. In this paper, we examine a real-world online social media with more than 1.7 million information spreading records. We observe evident collective behavior in information cascading systems and then propose metrics to quantify the collectivity. We find that previous information cascading models cannot capture the collective behavior in the real-world data and thus never utilize it. Furthermore, we propose a comprehensive generative framework with a latent user interest layer to capture the collective behavior. Our framework accurately models the information cascades with respect to dynamics, popularity, structure, and collectivity. By leveraging the knowledge behind collective behavior, our model successfully predicts the popularity and participants of information cascades without temporal features or early stage information. Our framework may serve as a more generalized one in modeling cascading systems, and, together with empirical discovery and applications, advance our understanding of human behavior.
Year
DOI
Venue
2020
10.1007/s10115-020-01506-8
KNOWLEDGE AND INFORMATION SYSTEMS
Keywords
DocType
Volume
Collective human behavior, Information cascades, Generative framework, Point process
Journal
62
Issue
ISSN
Citations 
12
0219-1377
0
PageRank 
References 
Authors
0.34
20
7
Name
Order
Citations
PageRank
Yunfei Lu151.44
Linyun Yu21024.83
Tianyang Zhang3574.35
Chengxi Zang4487.60
Peng Cui52317110.00
Chaoming Song658023.58
Wenwu Zhu74399300.42