Poisson-process-model-based method for predicting event popularity in social network

A social network and process model technology, applied in the field of event popularity prediction, can solve the problems of predicting public patents without event popularity

Active Publication Date: 2014-12-03
上海深杳智能科技有限公司 +1
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Problems solved by technology

[0004] After searching, there are currently no published patents related to the prediction of event popularity

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  • Poisson-process-model-based method for predicting event popularity in social network
  • Poisson-process-model-based method for predicting event popularity in social network
  • Poisson-process-model-based method for predicting event popularity in social network

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Embodiment Construction

[0072] The present invention will be described in detail below in conjunction with specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0073] This embodiment provides a method for predicting event popularity in microblogs, which is divided into four parts:

[0074] The first step is to select a sample event and obtain the Weibo propagation chain of the sample event;

[0075] The second step is to calculate the influence of users, and select those with high influence as key users, and mark the remaining users as non-key users;

[0076] The third step is to model the process of forwarding microblogs brought by key users as a Poiss...

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Abstract

The invention provides a Poisson-process-model-based method for predicting the event popularity in a social network. The method comprises the steps as follows: Step 1, selecting a sample event, and obtaining a micro-blog propagation chain of the sample event; Step 2, performing user influence calculation, screening users with the strong influence as critical users, and marking the rest users as non-critical users; Step 3, modeling micro-blog forwarding processes brought by the critical users as a Poisson process model, and simply processing micro-blog forwarding processes brought by the non-critical users; Step 4, learning and estimating parameters of the Poisson process model of the critical users and parameters of the non-critical users by the aid of information of the sample event; Step 5, giving a new event with evolution information in certain duration, and predicting the popularity of the new event at a certain moment in the future according to the model in Step 3. By the aid of the method, the better popularity prediction can be provided at the initial stage of a micro-blog event, and very effective help can be provided for reasonable guidance of development of the event.

Description

technical field [0001] The present invention relates to a method for predicting event popularity in a social network in the technical field of the Internet, in particular to a method for predicting event popularity in a social network based on a Poisson process model. Background technique [0002] In the era of Web 2.0, social networks are in full swing and have gradually become an important part of netizens' daily lives. Weibo, as a new type of broadcasting social network platform that shares brief information through the attention mechanism, has the characteristics of simple operation and strong interaction. By the end of 2012, the number of registered users of Sina Weibo had exceeded 400 million. As a new media, Weibo has played an increasingly important role in disseminating information and forming hot topics and hot events. Weibo is becoming the main source of hot spots in public opinion, fully participating in and influencing the real world. [0003] Weibo plays a ve...

Claims

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Application Information

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IPC IPC(8): G06F17/30G06Q10/04G06Q50/00
CPCG06F16/951G06Q10/04G06Q50/01
Inventor 陈凯周异何建华周曲杨蒙蒙
Owner 上海深杳智能科技有限公司
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