Collective Human Behavior in Cascading System

Discovery, Modeling and Applications

Yunfei Lu, Linyun Yu, Tianyang Zhang, Chengxi Zang, Peng Cui, Chaoming Song, Wenwu Zhu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

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 languageEnglish (US)
Title of host publication2018 IEEE International Conference on Data Mining, ICDM 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages297-306
Number of pages10
ISBN (Electronic)9781538691588
DOIs
StatePublished - Dec 27 2018
Event18th IEEE International Conference on Data Mining, ICDM 2018 - Singapore, Singapore
Duration: Nov 17 2018Nov 20 2018

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume2018-November
ISSN (Print)1550-4786

Conference

Conference18th IEEE International Conference on Data Mining, ICDM 2018
CountrySingapore
CitySingapore
Period11/17/1811/20/18

Fingerprint

Information systems

Keywords

  • Collective human behavior
  • Generative framework
  • Information cascades

ASJC Scopus subject areas

  • Engineering(all)

Cite this

Lu, Y., Yu, L., Zhang, T., Zang, C., Cui, P., Song, C., & Zhu, W. (2018). Collective Human Behavior in Cascading System: Discovery, Modeling and Applications. In 2018 IEEE International Conference on Data Mining, ICDM 2018 (pp. 297-306). [8594854] (Proceedings - IEEE International Conference on Data Mining, ICDM; Vol. 2018-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICDM.2018.00045

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 proceedingConference contribution

Lu, Y, Yu, L, Zhang, T, Zang, C, Cui, P, Song, C & Zhu, W 2018, Collective Human Behavior in Cascading System: Discovery, Modeling and Applications. in 2018 IEEE International Conference on Data Mining, ICDM 2018., 8594854, Proceedings - IEEE International Conference on Data Mining, ICDM, vol. 2018-November, Institute of Electrical and Electronics Engineers Inc., pp. 297-306, 18th IEEE International Conference on Data Mining, ICDM 2018, Singapore, Singapore, 11/17/18. https://doi.org/10.1109/ICDM.2018.00045
Lu Y, Yu L, Zhang T, Zang C, Cui P, Song C et al. Collective Human Behavior in Cascading System: Discovery, Modeling and Applications. In 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). https://doi.org/10.1109/ICDM.2018.00045
Lu, Yunfei ; Yu, Linyun ; Zhang, Tianyang ; Zang, Chengxi ; Cui, Peng ; Song, Chaoming ; Zhu, Wenwu. / Collective Human Behavior in Cascading System : Discovery, Modeling and Applications. 2018 IEEE International Conference on Data Mining, ICDM 2018. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 297-306 (Proceedings - IEEE International Conference on Data Mining, ICDM).
@inproceedings{8d2b6f2f66e944ed9843f1df352959fa,
title = "Collective Human Behavior in Cascading System: Discovery, Modeling and Applications",
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.",
keywords = "Collective human behavior, Generative framework, Information cascades",
author = "Yunfei Lu and Linyun Yu and Tianyang Zhang and Chengxi Zang and Peng Cui and Chaoming Song and Wenwu Zhu",
year = "2018",
month = "12",
day = "27",
doi = "10.1109/ICDM.2018.00045",
language = "English (US)",
series = "Proceedings - IEEE International Conference on Data Mining, ICDM",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "297--306",
booktitle = "2018 IEEE International Conference on Data Mining, ICDM 2018",

}

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 -