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

3 Scopus citations

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.

    Fingerprint

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