The purpose of this paper is to present a new human dynamics model to explain the process of verified users' (VUs) posting on Sina micro-blog.
A common human dynamics research method with three steps is applied. Firstly, a large-scale behavioral dataset is collected involving 495 VUs and five topics on Sina micro-blog. Second, five important indicators that reveal the characteristics of posting behavior are analyzed. Then, a quantitative model is constructed to describe the process of posting behavior, and its validity is verified by simulations.
Three important characteristics of VUs' micro-blog posting behavior are observed: fat-tailed distribution, fluctuation and periodicity. These characteristics do not fit the assumption of interest-driven models proposed by previous literature. An optimized task-driven model is introduced to describe this complex phenomenon mathematically. The model is verified on empirical data, confirming that task-driven models can be optimized to explore information behavior on social media.
Being different from previous studies that mainly describe common users' posting behavior on social media by applying interest-driven models, this paper customizes an optimized task-driven model for VUs, who mainly treat social media as a platform for work and play a crucial role in information creation on social media.
