TY - JOUR
T1 - Exploring the collective human behavior in cascading systems
T2 - a comprehensive framework
AU - Lu, Yunfei
AU - Yu, Linyun
AU - Zhang, Tianyang
AU - Zang, Chengxi
AU - Cui, Peng
AU - Song, Chaoming
AU - Zhu, Wenwu
N1 - Funding Information:
The authors thank anonymous reviewers for many useful discussions and insightful suggestions. This work was supported in part by National Key R&D Program of China (No. 2018AAA0102004), National Natural Science Foundation of China (Nos. U1936219, 61772304, 61531006, U1611461), Beijing Academy of Artificial Intelligence (BAAI ), and a grant from the Institute for Guo Qiang, Tsinghua University. Thanks for the research fund of Tsinghua-Tencent Joint Laboratory for Internet Innovation Technology and the Young Elite Scientist Sponsorship Program by CAST. Song was partly supported by the National Science Foundation (IBSS-L-1620294). Wenwu Zhu is the corresponding author. All opinions, findings, and conclusions in this paper are those of the authors and do not necessarily reflect the views of the funding agencies.
Funding Information:
The authors thank anonymous reviewers for many useful discussions and insightful suggestions. This work was supported in part by National Key R&D Program of China (No. 2018AAA0102004), National Natural Science Foundation of China (Nos. U1936219, 61772304, 61531006, U1611461), Beijing Academy of Artificial Intelligence (BAAI ), and a grant from the Institute for Guo Qiang, Tsinghua University. Thanks for the research fund of Tsinghua-Tencent Joint Laboratory for Internet Innovation Technology and the Young Elite Scientist Sponsorship Program by CAST. Song was partly supported by the National Science Foundation (IBSS-L-1620294). Wenwu Zhu is the corresponding author. All opinions, findings, and conclusions in this paper are those of the authors and do not necessarily reflect the views of the funding agencies.
Publisher Copyright:
© 2020, Springer-Verlag London Ltd., part of Springer Nature.
PY - 2020/12/1
Y1 - 2020/12/1
N2 - 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.
AB - 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.
KW - Collective human behavior
KW - Generative framework
KW - Information cascades
KW - Point process
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U2 - 10.1007/s10115-020-01506-8
DO - 10.1007/s10115-020-01506-8
M3 - Article
AN - SCOPUS:85089894504
VL - 62
SP - 4599
EP - 4623
JO - Knowledge and Information Systems
JF - Knowledge and Information Systems
SN - 0219-1377
IS - 12
ER -