A method for extracting high-influence information from social networks

Abstract

The invention provides an information extraction method based on transmission and distribution statistics and validity evaluation on the basis of intrinsic characteristics of a social network. The method includes the steps of carrying out initial qualitative evaluation on information distribution according to information source tools and establishing valid quadratic characteristics based on user information in the process of validity processing to carry out quantitative evaluation. In the process of calculation, a linear complexity algorithm is used for carrying out analysis, the demand for computing resources is low, and time cost and space cost are low. In the process of association, comparison processing is carried out, and MicroBlog information which is really high in exposure rate is output at last. The information extraction method has higher practical value in reality, effectively achieves analysis on influence and exposure rates of public opinion information of the social network and can filter out low-quality data and reserve true and valid information for use in follow-up manual analysis.

Description

technical field [0001] The invention relates to a method for extracting high-influence information of a social network, belonging to the technical application field of social network data mining. Background technique [0002] As an important and efficient information transmission platform, social network has more and more people participating in it. The government, institutions, and business units exchange information with everyone on this platform, absorb opinions from all sides in a timely manner, evaluate the development and effect of various policies, and then revise and re-plan the original plan. [0003] At present, information evaluation by enterprises is generally based on manual analysis, and due to the massive nature of network information, information with low forwarding and comment volume is directly ignored in manual processing, and it is mainly based on a large number of forwarding or comment information on social networks. When enterprises conduct informa...

AI Technical Summary

Problems solved by technology

[0004] However, a large number of reposts and comments do not directly represent exposure and influence. Due to the inherent difficulties in the information analysis of social networks, first of all, social networks are full of false information and low-quality information, and a unified analysis of these information Observation and understanding will waste a lot of manpower and material resources, and cannot reflect real problems; secondly, due to the large number of participants, the information is unbounded, and the information can be infinitely expanded in the network, and even finally affect the reality. Such a large amount of original information is difficult Fully understand and effectively extract, ultimately used to assist decision-making

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