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Method for predicting post forwarding amount in social network based on Kalman filter

A technology of social network and forecasting method, which is applied in the field of forecasting post forwarding volume, and can solve the problem of low forecasting accuracy

Active Publication Date: 2018-01-19
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

In literature [4], the self-excited point process model is constructed based on the Hawkes process, and all historical behaviors of the forwarding process are considered, but it is precisely because the influence of all forwarding historical processes is considered that the prediction accuracy is not high

Method used

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  • Method for predicting post forwarding amount in social network based on Kalman filter
  • Method for predicting post forwarding amount in social network based on Kalman filter
  • Method for predicting post forwarding amount in social network based on Kalman filter

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Embodiment Construction

[0049] The technical solution of the present invention will be described in further detail below in conjunction with accompanying drawings and examples of implementation:

[0050] In the first step, we assume that the influence of the post at time t-1 is 0.8, and the corresponding estimation error P′ t-1 =3, then the system state at time t and the predicted value of the error are unchanged, and assuming that the uncertainty q=4 in the forecasting process, then the total deviation in the forecasting process is 5.

[0051] In the second step, from the cascaded forwarded data, it can be obtained that the forwarding time at time t and time t-1 are 84988s and 84993s respectively, and the number of followers of the forwarding user is 77. Therefore, the forwarding strength at time t is obtained Also assume that the deviation of this value is r=4.

[0052] In the third step, there are two values ​​for estimating the system state at time t, which are 0.8 and 0.977, and the actual ...

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Abstract

The invention relates to a method for predicting the post forwarding amount in a social network in the field of Internet, and particularly designs a method for estimating the event popularity by predicting the post forwarding amount in the social network based on a Kalman filter. The method is aimed at solving the problem of predicting the total post forwarding amount when the time after a post isissued tends to be infinite. A post influence predicting model is established for different states in the post cascaded forwarding process, the theory that the post state at the current moment is only influenced by the post state at the last moment is put forward, and branch factors are introduced; by establishing an information cascaded tree, the predicting model of the total future post forwarding amount is converted into geometric progression summation, and therefore the final post forwarding amount is predicted. The method is widely applied to application scenes of public sentiment control, new product popularization and the like.

Description

technical field [0001] The invention relates to a method for predicting post forwarding volume in a social network in the Internet field. Specifically, the design is a method based on Kalman filter in social network to estimate the popularity of events by predicting the amount of post forwarding. Background technique [0002] With the development of online social networks, more and more people choose to share resources and transmit information through online networks. Foreign websites, such as Facebook and Twitter, and domestic websites, such as Sina Weibo, are all platforms for people to spread information. The information published on the website can be seen by more users after being reposted by users' fans. After such multiple forwarding, a large information cascading network is formed, which spreads the information to a wider range. It can be seen that the information disseminated in the online network will have a profound impact on the entire social network. For exa...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/00G06N7/00
Inventor 郑吉平张丝曼张智明
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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