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 online social media at large scale are seldom unexplored. In this paper, we examine a real-world online social media with more than 1.7 million information spreading records, which explicitly document the detailed human behavior in this online information cascading system. We observe evident collective behavior in information cascading, and then propose metrics to quantify the collectivity. We find that previous information cascading models cannot capture the collective behavior in the real-world and thus never utilize it. Furthermore, we propose a generative framework with a latent user interest layer to capture the collective behavior in cascading system. Our framework achieves high accuracy in modeling the information cascades with respect to popularity, structure and collectivity. By leveraging the knowledge of collective behavior, our model shows the capability of making predictions without temporal features or early-stage information. Our framework can serve as a more generalized one in modeling cascading system, and, together with empirical discovery and applications, advance our understanding of human behavior.
| Original language | English (US) |
|---|---|
| Title of host publication | 2018 IEEE International Conference on Data Mining, ICDM 2018 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 297-306 |
| Number of pages | 10 |
| ISBN (Electronic) | 9781538691588 |
| DOIs | |
| State | Published - Dec 27 2018 |
| Event | 18th IEEE International Conference on Data Mining, ICDM 2018 - Singapore, Singapore Duration: Nov 17 2018 → Nov 20 2018 |
Publication series
| Name | Proceedings - IEEE International Conference on Data Mining, ICDM |
|---|---|
| Volume | 2018-November |
| ISSN (Print) | 1550-4786 |
Conference
| Conference | 18th IEEE International Conference on Data Mining, ICDM 2018 |
|---|---|
| Country | Singapore |
| City | Singapore |
| Period | 11/17/18 → 11/20/18 |
Fingerprint
Keywords
- Collective human behavior
- Generative framework
- Information cascades
ASJC Scopus subject areas
- Engineering(all)
Cite this
Collective Human Behavior in Cascading System : Discovery, Modeling and Applications. / Lu, Yunfei; Yu, Linyun; Zhang, Tianyang; Zang, Chengxi; Cui, Peng; Song, Chaoming; Zhu, Wenwu.
2018 IEEE International Conference on Data Mining, ICDM 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 297-306 8594854 (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2018-November).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Collective Human Behavior in Cascading System
T2 - Discovery, Modeling and Applications
AU - Lu, Yunfei
AU - Yu, Linyun
AU - Zhang, Tianyang
AU - Zang, Chengxi
AU - Cui, Peng
AU - Song, Chaoming
AU - Zhu, Wenwu
PY - 2018/12/27
Y1 - 2018/12/27
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 online social media at large scale are seldom unexplored. In this paper, we examine a real-world online social media with more than 1.7 million information spreading records, which explicitly document the detailed human behavior in this online information cascading system. We observe evident collective behavior in information cascading, and then propose metrics to quantify the collectivity. We find that previous information cascading models cannot capture the collective behavior in the real-world and thus never utilize it. Furthermore, we propose a generative framework with a latent user interest layer to capture the collective behavior in cascading system. Our framework achieves high accuracy in modeling the information cascades with respect to popularity, structure and collectivity. By leveraging the knowledge of collective behavior, our model shows the capability of making predictions without temporal features or early-stage information. Our framework can serve as a more generalized one in modeling cascading system, 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 online social media at large scale are seldom unexplored. In this paper, we examine a real-world online social media with more than 1.7 million information spreading records, which explicitly document the detailed human behavior in this online information cascading system. We observe evident collective behavior in information cascading, and then propose metrics to quantify the collectivity. We find that previous information cascading models cannot capture the collective behavior in the real-world and thus never utilize it. Furthermore, we propose a generative framework with a latent user interest layer to capture the collective behavior in cascading system. Our framework achieves high accuracy in modeling the information cascades with respect to popularity, structure and collectivity. By leveraging the knowledge of collective behavior, our model shows the capability of making predictions without temporal features or early-stage information. Our framework can serve as a more generalized one in modeling cascading system, and, together with empirical discovery and applications, advance our understanding of human behavior.
KW - Collective human behavior
KW - Generative framework
KW - Information cascades
UR - http://www.scopus.com/inward/record.url?scp=85061381873&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061381873&partnerID=8YFLogxK
U2 - 10.1109/ICDM.2018.00045
DO - 10.1109/ICDM.2018.00045
M3 - Conference contribution
T3 - Proceedings - IEEE International Conference on Data Mining, ICDM
SP - 297
EP - 306
BT - 2018 IEEE International Conference on Data Mining, ICDM 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -