Modeling collective behavior of posting microblogs by stochastic differential equation with jump
| Autor: | Yong Hu, Yuan-Qi Li, Jun-Shan Pan, Hanping Hu |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Statistics and Probability
Collective behavior Social network business.industry Microblogging Computer science Statistical and Nonlinear Physics computer.software_genre 01 natural sciences 010305 fluids & plasmas Stochastic differential equation 0103 physical sciences Compound Poisson process Jump Feature (machine learning) Social media Data mining 010306 general physics business Focus (optics) computer |
| Zdroj: | Physica A: Statistical Mechanics and its Applications. 578:126117 |
| ISSN: | 0378-4371 |
| DOI: | 10.1016/j.physa.2021.126117 |
| Popis: | The characterization and understanding of online social network behavior is of importance from both the points of view of fundamental research and realistic application. In this manuscript, we propose a stochastic differential equation to describe the online microblogging behavior. Our analysis is based on the microblog data collected from Sina Weibo, which is one of the most popular microblogging platforms in China. Especially, we focus on the collective nature of the microblogging behavior, which embodies itself as the periodic patterns, the stochastic fluctuations around the baseline, and the extraordinary jumps in the analyzed data. Compared with existing works, we use time dependent parameters to facilitate the periodic feature of the microblogging behavior and incorporate a compound Poisson process to describe the extraordinary spikes in the Sina Weibo volume. These distinct merits lead to significant improvement in the prediction performance, thus justifying the validity of our model. This work may offer an alternative route towards the future detection of the anomalous behavior in online social network platforms. |
| Databáze: | OpenAIRE |
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