A point process indexed by time is called a counting process when it counts the number of instances reshares, in our case. Exploiting uncertainty in popularity prediction of. Self exciting spatiotemporal point processes 3 self exciting models can be estimated using standard maximum likelihood approaches, discussed in section3. He, anand rajaraman 2, jure leskovec department of statistics1 and computer science2, stanford university kdd15, aug 12, 2015. The selfexciting point process sepp model is a recently developed crime prediction method whose inputs are the locations and times of historic crimes. A self exciting point process model for predicting tweet popularity article pdf available june 2015 with 169 reads how we measure reads. Selfexciting point process modeling of crime ucla statistics. The fda and point process approaches are linked together by using functional data analysis technique to describe the intensity of. We build on the theory of selfexciting point processes to develop a statistical model that allows us to make accurate. A self exciting point process model for predicting tweet popularity qingyuan zhao 1, murat a. We build on the theory of selfexciting point processes to develop a statistical model that allows us to make accurate predictions. In a self exciting point process, each reshare not only increases the cumulative count by one, it also exposes new followers who may further reshare the post.
In section 4, we compare the predictive accuracy of. A point process is also called a counting process when it is. Pdf selfexciting point process modeling of crime researchgate. Previous works in the literature proposed different so.
Seismic models the information cascade as a self exciting point process. Quyu kong, marianandrei rizoiu, lexing xie submitted on 29 jan 2020. We build on the theory of selfexciting point processes to develop a statistical model that allows us to predict the final number of reshares of a given post that is. It shows promising predictive performance and forms the basis of a popular commercial software package, however few detailed case studies describing the application of the sepp to crime data exist in the scientific literature. Once a self exciting model is estimated, we are able to answer a range of scienti cally interesting questions about the dynamics of their generating processes. Pdf in this paper, we present a procedure for modeling earthquake based on spatialtemporal point process.
These models naturally capture triggering and clustering behavior. A selfexciting point process model for predicting tweet. Our methodol ogy combines the idea of stochastic declustering with kernel. The self exciting point process sepp is a model of the spread of crime in space and time, incorporating background and triggering processes.
Our model requires no training or expensive feature engineering. Our methodology combines the idea of stochastic declustering with kernel density estima tion in a novel way. We model each social cascade with a marked hawkes self exciting point process, and estimate the content virality, memory decay, and user in uence. Crime prediction is an imperative step in predictive modeling. The self exciting point process sepp model is a recentlydeveloped crime prediction method whose inputs are the locations and times of historic crimes. Improving the robustness and accuracy of crime prediction.
In section 5, we compare the predictive accuracy of. Pdf selfexciting point process in modeling earthquake occurrences. To our surprise, hawkes process with a predictive overlay outperform recent featuredriven and gen. Bspline, forecast, hawkes process, integral equation, nonstationary selfexciting point process, popularity prediction, simulation. Longitudinal modeling of social media with hawkes process. This property is ideal to model the rich get richer phenomenon in information spreading. We then learn a predictive layer for popularity prediction using a collection of cascade history. We develop a statistical model based on the theory of selfexciting point processes.
A selfexciting point process model for predicting tweet popularity. A selfexciting point process model for predicting tweet popularity qingyuan zhao 1, murat a. Selfexciting spatiotemporal point process models predict the rate of events as a function of space, time, and the previous history of events. Exploiting uncertainty in popularity prediction of information diffusion cascades using selfexciting point processes. We develop a statistical model based on the theory of self exciting point processes. In this paper, a novel model for the bid arrival process using a self. Feature driven and point process approaches for popularity. Self exciting point processes are frequently used to model rich get richer phenomenon 21, 22, 32, 35, and are ideal for modeling information cascades in networks, because every new reshare.
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