Abstract
The subject of collective attention is in the center of this era of information explosion. It is thus of great interest to understand the fundamental mechanism underlying attention in large populations within a complex evolving system. Moreover, an ability to predict the dynamic process of collective attention for individual items has important implications in an array of areas. In this report, we propose a generative probabilistic model using a self-excited Hawkes process with survival theory to model and predict the process through which individual items gain their attentions. This model explicitly captures three key ingredients: the intrinsic attractiveness of an item, characterizing its inherent competitiveness against other items; a reinforcement mechanism based on sum of each previous attention triggers; and a power-law temporal relaxation function, corresponding to the aging in the ability to attract new attentions. Experiments on two population-scale datasets demonstrate that this model consistently outperforms the state-of-the-art methods.
Highlights
In recent year, there has been heightened research interest regarding the predictive modeling of the dynamics of collective attention for online content[27, 28]
We find that P(Δt|Nc) roughly follows a power-law distribution with an exponent 2.11 for Nc = 10 and an exponent 2.03 for Nc = 20 respectively, indicating that collective attention is allocated in a rather asymmetric way, with a burst of rapidly arriving attentions followed by long periods of no attention
We propose a general framework to model and predict the dynamic process of collective attention
Summary
There has been heightened research interest regarding the predictive modeling of the dynamics of collective attention for online content[27, 28]. More sophisticated models have been proposed to simulate the dynamics of attentions for individual items, treating the diffusion process as a reinforced Poisson process[34, 35] or a double stochastic process[36] These models usually assume an aggregate stochastic process without distinguishing the triggering effects of different attentions in the diffusion-and-reaction process. We propose a generative probabilistic model using a self-excited Hawkes process with survival theory to model and predict the dynamic process through which individual items gain their attentions. Experimental results demonstrate that our proposed model consistently outperforms the state-of-the-art methods on two datasets
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
