Multi-platform-based issue network attention information processing method, system and application

Abstract

The invention belongs to the technical field of network space cognitive domains, and discloses a multi-platform-based topic network attention information processing method and system and application. Extracting social platform data, news media data and local newspaper and magazine data of the subject; calculating the network attention of the issue social platform from two dimensions of issue related posting and posting interaction data; calculating the attention of the news media based on the influence of the news media and the report information of the news media, and calculating the attention of the newspapers and magazines based on the influence of the newspapers and magazines and the report information of the newspapers and magazines; and calculating the final network attention of the topic as the sum of the network attention of the social media, the network attention of the news media and the attention of the newspapers and magazines. The invention innovatively provides a comprehensive calculation method for adding a scientific and reasonable weight ratio to network interaction indexes and combining local netizen data, and provides a more scientific and reasonable theoretical support for network attention calculation of an issue.

Description

technical field [0001] The present invention belongs to the technical field of cyberspace cognitive domain, and in particular relates to a method, system and application for processing issue network attention degree information based on multiple platforms. Background technique [0002] At present, the degree of online attention of an issue reflects the degree of attention of an issue on the Internet within a period of time, and is one of the important indicators for evaluating the influence of an issue on the Internet. [0003] The calculation of the online attention of an issue is generally divided into two types. One is to directly crawl the network data and count the number of relevant posts on the issue to represent; the other is to crawl the interactive data of the issue-related posts, including likes and comments. , forwarding, reading, etc., and simply sum them to refer to the online attention of the topic. [0004] Through the above analysis, the problems and defect...

AI Technical Summary

Problems solved by technology

[0004] Through the above analysis, the existing problems and defects of the existing technology are as follows: (1) The existing technology cannot scientifically and completely express the attention of netizens to the topic by only using posting data

(2) Existing technologies cannot fully cover the attention of the entire network by only using social platform data

(3) The existing technology lacks an effective weight ratio for the interactive data of online posting of topics, but in fact, posting data and interactive data are different for the presentation of attention

(4) In the existing technology, for network issues in different regions, the data collection does not have obvious regional characteristics, so that the obtained data cannot accurately reflect the attention of the issues in the target area and target groups, and the accuracy of the judgment results is poor

(5) The attention degree information in the existing technology has problems of poor timeliness and inaccurate data volume, which leads to inaccurate prediction of changes in the degree of attention when there is a sudden change in the degree of attention caused by an emergency

[0007] Based on the problems existing in the calculation of topic network attention, this program proposes a multi-platform-based calculation method for topic network attention, which uses data from multiple sources such as social platforms, news media, local newspapers and magazines, and solves the problem. There are problems of timeliness and data volume in the traditional calculation of attention. Using the data obtained on the network, the real-time attention of various issues can be calculated in real time, and the change of attention can be reflected in a timely manner when there is a sudden change in the attention rate caused by emergencies, making up for it. Gaps in the calculation method of topic network attention

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