CN112732780A - Figure network activity degree calculation method, system, processing terminal and computer equipment - Google Patents

Figure network activity degree calculation method, system, processing terminal and computer equipment Download PDF

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CN112732780A
CN112732780A CN202011628432.9A CN202011628432A CN112732780A CN 112732780 A CN112732780 A CN 112732780A CN 202011628432 A CN202011628432 A CN 202011628432A CN 112732780 A CN112732780 A CN 112732780A
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platform
character
activity
network
index
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CN112732780B (en
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曾曦
陈天莹
张朝清
吴大冬
周伟中
李霄
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Shenzhen Wanglian Anrui Network Technology Co ltd
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    • G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
    • G06F16/2462—Approximate or statistical queries
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
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    • G06Q50/01—Social networking

Abstract

The invention discloses a figure network activity degree calculation method, a figure network activity degree calculation system, a figure network activity degree processing terminal and computer equipment, and relates to the technical field of network space cognition. Extracting figure social platform data on each platform based on a system index of figure network liveness according to an evaluation object of figure liveness; the network activity calculation of the single-platform figure is realized; the time period of the overall data of the platform is kept consistent with the overall time period of the network activity of the character, and weight calculation is carried out on each social platform used by the character based on the account number and post number of each platform; and calculating the activity of the personnel integrated network. According to the invention, the character posting related data is used as a main index, the behavior of others on the user such as praise, comment and forwarding is used as a secondary index, the activity degree calculation dimension is added, and after the activity degree calculation dimension is expanded, the activity frequency degree of the character on the current social platform is more accurately evaluated.

Description

Figure network activity degree calculation method, system, processing terminal and computer equipment
Technical Field
The invention relates to the technical field of network space cognition, in particular to a figure network activity degree calculation method and system based on open source data.
Background
Liveness has different meanings depending on different scenarios. In the securities industry, liveness is generally used to evaluate how often a stock is traded. In the social network, the user activity refers to the frequency of activities performed by the user on the social network, and is calculated mainly through the occurrence frequency of behaviors of the user such as posting, forwarding and commenting.
There is currently less quantitative research on the user liveness of social networks. The existing calculation of user liveness mainly comprises the steps of carrying out weighted calculation according to the time, the number of issued documents, replies, likes and favorites, and the like of a user on a single social platform and combining the factors of time and the like. Social media such as micro blogs and micro messages divide users into active and inactive users based on user text sending, comment, use frequency and the like of the own platform, so that the influence strategy can be formulated, and the inactive users can be activated.
The existing activity degree calculation method has the defects of several aspects:
(1) in the current internet era, everyone uses a plurality of social platforms, and acts such as posting, replying, praise and the like can be performed on different social platforms at the same time, and the acts can be integrated to completely reflect the activity of a personal network. The existing person activity degree calculation method only aims at a single platform, and the calculation result cannot measure the activity degree of a person network on the whole level.
(2) Most individual users do not continuously post on the social platform, and are more in browsing, commenting or praise states, and the invisible behaviors are also reflected by the activity degree of the users. The existing person network activity calculation method takes the number and frequency of postings of one person and the comment number of the posts of the person as main calculation factors, ignores the key factor of frequent interaction, and causes the result not to accord with the actual situation.
(3) The existing social platform generally calculates the character liveness through self background user behavior data, and improves the user viscosity of a calculation result platform. However, since the data of each platform is private data, the overall network activity of the person cannot be evaluated in an environment based on open source data.
In order to overcome the problems in the related technology and based on the problems of character activity calculation, the scheme provides a character network activity analysis method based on open source data, so that the dimension of user activity evaluation is expanded, and the accuracy of activity calculation of a target user in a social network is improved; the user activity evaluation method with the introduction of the multiple social platforms solves the problem that the overall network activity of the character based on open source data cannot be calculated, and makes up the blank of comprehensive evaluation of the character network activity. The technical scheme is as follows:
the figure network activity degree calculation method based on open source data comprises the following steps:
step one, extracting figure social platform data on each platform based on a system index of figure network liveness according to an evaluation object of figure liveness;
step two, realizing the network activity calculation of the single-platform figure;
step three, keeping the time period of the whole platform data consistent with the whole time period of the character network activity, and performing weight calculation on each social platform used by the character based on the account number and post number of each platform;
and step four, calculating the activity of the personnel integrated network.
In one embodiment, implementing network liveness computation for a single platform personality comprises:
the main index starts from the dimensionality of the posting number, the praise number, the forwarding number and the comment number of the character and can directly reflect the data of the activity frequency degree of key personnel on the platform;
the secondary index reflects the data of the frequency degree of the activity of the character from the dimension side of the number of endorsed posts, the number of commented posts and the number of forwarded posts of the character;
calculating the weight of the primary index and the secondary index, and calculating the weight of each social platform used by the character based on the account number and the post number of each platform;
the character network activity of the single platform is calculated based on the main indexes, the secondary indexes and the weights of the main indexes and the secondary indexes.
In one embodiment, the primary indicator is calculated as follows:
pri_index＝num_post+num_trans+num_likes+num_posts
where pri _ index is a main index, num _ post is a number of posts of a character, num _ trans is a number of posts of a character, num _ likes is a number of like points, and num _ post is a number of comments of a character.
In one embodiment, the secondary indicator is calculated as follows:
sec_index＝likes*w_like+posts+transs*w_trans
the sec _ index is a secondary index, liks is a mean value of the number of posted votes, posts is a mean value of the number of posted votes, transitions is a mean value of the number of posted votes, w _ like is an adjustment factor of the number of posted votes, and w _ trans is an adjustment factor of the number of forwarded votes.
In one embodiment, in the weight calculation of the primary and secondary indexes, the adjustment factor w _ like of the praise number is set to be 0.01, and the adjustment factor w _ trans of the forwarding number is set to be 0.1.
In one embodiment, the single-platform human network activity is calculated as follows:
act_plat＝pri_index*0.8+sec_index*0.2
the main index is given with a weight of 0.8, and the secondary index is given with a weight of 0.2, so that the influence degree of the main index and the secondary index on the activity of the human network is reflected.
In one embodiment, in step three, the platform's weights include an account weight and a post weight, wherein,
the account weight is defined as follows:
Figure BDA0002875538940000031
wherein, w _ zhPlatform nIs the weight of the platform n account number, zhf platform nIs the platform n account number;
the post weight is defined as follows:
Figure BDA0002875538940000041
wherein, w-tzPlatform 1Is the weight of the platform n post, tzPlatform nIs the number of n posts on the platform;
finally, platform weight for platform 1:
Figure BDA0002875538940000042
wherein, wPlatform 1Is the platform weight for platform 1.
In one embodiment, in step four, the calculation of the comprehensive network activity of the personnel is obtained by the weighted sum calculation of the activities of the N platforms, and the calculation formula is as follows:
Figure BDA0002875538940000043
wherein act is the network activity of the character.
Another object of the present invention is to provide a system for implementing the method for computing activity of a character network based on open source data, the system for computing activity of a character network based on open source data comprising:
the figure social platform data extraction unit is used for extracting figure social platform data on each platform based on a system index of figure network activity according to an evaluation object of figure activity;
the network activity calculation unit is used for realizing the network activity calculation of the single-platform character;
the weight calculation unit is used for keeping the time period of the whole platform data consistent with the whole time period of the character network activity and carrying out weight calculation on each social platform used by the character based on the account number and the post number of each platform;
and the activity calculation unit is used for realizing the activity calculation of the comprehensive network of the personnel.
Another object of the present invention is to provide an information data processing terminal equipped with the system for calculating the activity of a character network based on open source data, and implementing the method for calculating the activity of a character network based on open source data.
It is a further object of the invention to provide a computer device comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to perform the steps of:
step one, extracting figure social platform data on each platform based on a system index of figure network liveness according to an evaluation object of figure liveness;
step two, realizing the network activity calculation of the single-platform figure;
step three, keeping the time period of the whole platform data consistent with the whole time period of the character network activity, and performing weight calculation on each social platform used by the character based on the account number and post number of each platform;
and step four, calculating the activity of the personnel integrated network.
The technical scheme provided by the embodiment of the invention has the following beneficial effects:
according to the invention, the character posting related data is used as a main index, the behavior of others on the user such as praise, comment and forwarding is used as a secondary index, the activity degree calculation dimension is added, and after the activity degree calculation dimension is expanded, the activity frequency degree of the character on the current social platform is more accurately evaluated.
Based on the total posting amount and the account number of the multiple social contact platforms, calculating the influence weight of each platform on the character liveness, carrying out normalization processing and comprehensive calculation on the liveness of the characters on different social contact platforms, and finally obtaining the overall network liveness of the characters. The problem that the activity of the whole network of the character cannot be calculated is solved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the disclosure.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a network activity indicator architecture diagram provided by the present invention.
FIG. 2 is a flowchart of computing activity of a character network according to the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein, but rather should be construed as broadly as the present invention is capable of modification in various respects, all without departing from the spirit and scope of the present invention.
It will be understood that when an element is referred to as being "secured to" another element, it can be directly on the other element or intervening elements may also be present. When an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may also be present. As used herein, the terms "vertical," "horizontal," "left," "right," and the like are for purposes of illustration only and are not intended to represent the only embodiments.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
(1) According to the method, the character posting related data is used as a main index, the behavior of others on the user, such as praise, comment and forwarding, is used as a secondary index, the activity degree calculation dimension is added, and after the activity degree calculation dimension is expanded, the activity frequency degree of the character on the current social platform is evaluated more accurately.
(2) Based on the total posting amount and the account number of the multiple social contact platforms, calculating the influence weight of each platform on the character liveness, carrying out normalization processing and comprehensive calculation on the liveness of the characters on different social contact platforms, and finally obtaining the overall network liveness of the characters. The problem that the activity of the whole network of the character cannot be calculated is solved.
As shown in fig. 1, a human network activity index system is mainly constructed from three dimensions, namely a primary index, a secondary index and a platform weight. The main indexes comprise postings number, transfer number, praise number and comment number of the character; the secondary indexes comprise the number of votes on which the character posts, the number of comments on which the posts are commented and the number of forwarded posts; the platform weight is mainly used for carrying out weight calculation on each social platform used by the person on the basis of the account number and the post number of each platform.
The character network activity is dynamically changed, and the method is calculated based on data of characters on each social platform in a period of time.
The main process of the character network activity calculation is as follows:
extracting figure social platform data on each platform based on a system index of figure network liveness according to an evaluation object of figure liveness;
the network activity calculation of the single-platform character is mainly divided into the following aspects:
calculation of principal index
The main index starts from dimensions such as postings, praise, forwarding and comments of people and can directly reflect data of frequent activity of key personnel on the platform. The calculation method is as follows:
pri_index＝num-post+num_trans+num_likes+num_posts
where pri _ index is a main index, num _ post is a number of posts of a character, num _ trans is a number of posts of a character, num _ likes is a number of like points, and num _ post is a number of comments of a character.
Sub index calculation
The secondary indexes are the number of endorsed posts, the number of commented posts and the number of forwarded posts of the character, and the data cannot directly reflect the activity frequency of key personnel, but reflect the activity frequency of the key personnel to a certain extent from the side. The calculation method is as follows:
sec_index＝likes*w_like+posts+transs*w_trans
where sec _ index is a sub-index, likes is the mean of the number of posted votes, posts is the mean of the number of posted votes, and transitions is the mean of the number of posted votes. w _ like is an adjustment factor for the number of praise, and w _ trans is an adjustment factor for the number of hops.
Weight calculation of primary and secondary indicators
Through data discovery of a plurality of social platforms such as Facebook and Twitter, magnitude levels of praise number, share number and comment number of a post are sequentially decreased, and the gap is about one magnitude level, so that after expert research and judgment, w _ like is set to be 0.01 (adjustment factor of praise number), and w _ trans is set to be 0.1 (adjustment factor of forwarding number). By doing so, on one hand, the magnitude between the three can be balanced, and on the other hand, the magnitude of the secondary index can be reduced.
Single platform character network liveness
Based on the weight calculation mode of the primary index, the secondary index and the primary and secondary indexes, the single-platform character network activity degree calculation mode is as follows:
act_plat＝pri_index*0.8+sec_index*0.2
the main index experts give a weight of 0.8, and the sub index experts give a weight of 0.2, so that the influence degree of the main index and the sub index on the activity of the human network is reflected.
Individual platform weight
The social contact platforms have different user bases, different user release content forms and different use frequency and dependence degrees of people in different areas, so that when the network activity of people is evaluated, the total posting amount and the posting account number of the users related to a specific area of the platform are adopted to comprehensively calculate the weight occupied by each platform in combination with the regional characteristics. In order to reflect the importance degree of the platform more accurately, the time period of the whole platform data is consistent with the whole time period of the human network activity degree. The weight of a specific platform is defined as follows:
A. account weight
Figure BDA0002875538940000081
Wherein, w _ zhPlatform nIs the weight of the platform n account number, zhf platform nThe number of the n accounts of the platform and the like.
B. Post weight
Figure BDA0002875538940000091
Wherein, w-tzPlatform 1Is the weight of the platform n post, tzPlatform 1The number of n posts on the platform, and the same other principles.
Finally, platform weight for platform 1:
Figure BDA0002875538940000092
wherein, wPlatform 1Is the platform weight for platform 1.
Personnel integrated network liveness calculation
The character network activity is obtained through the weighted sum calculation of the activity of the N platforms. The calculation formula is as follows:
Figure BDA0002875538940000093
wherein act is the network activity of the character.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure should be limited only by the attached claims.

Claims (10)

1. A character network activity degree calculation method based on open source data is characterized by comprising the following steps:
step one, extracting figure social platform data on each platform based on a system index of figure network liveness according to an evaluation object of figure liveness;
step two, realizing the network activity calculation of the single-platform figure;
step three, keeping the time period of the whole platform data consistent with the whole time period of the character network activity, and performing weight calculation on each social platform used by the character based on the account number and post number of each platform;
and step four, calculating the activity of the personnel integrated network.
2. The character network activity degree calculation method based on open source data as claimed in claim 1, wherein the realization of the network activity degree calculation of the single platform character comprises:
the main index starts from the dimensionality of the posting number, the praise number, the forwarding number and the comment number of the character and can directly reflect the data of the activity frequency degree of key personnel on the platform;
the secondary index reflects the data of the frequency degree of the activity of the character from the dimension side of the number of endorsed posts, the number of commented posts and the number of forwarded posts of the character;
calculating the weight of the primary index and the secondary index, and calculating the weight of each social platform used by the character based on the account number and the post number of each platform;
the character network activity of the single platform is calculated based on the main indexes, the secondary indexes and the weights of the main indexes and the secondary indexes.
3. The method for computing human network activity based on open source data as claimed in claim 2, wherein the main index is computed as follows:
pri_index＝num_post+num_trans+num_likes+num_posts
where pri _ index is a main index, num _ post is a number of posts of a character, num _ trans is a number of posts of a character, num _ likes is a number of like points, and num _ post is a number of comments of a character.
4. The method for computing human network activity based on open source data as claimed in claim 2, wherein the secondary index is computed as follows:
sec_index＝likes*w_like+posts+transs*w_trans
the sec _ index is a secondary index, liks is a mean value of the number of posted votes, posts is a mean value of the number of posted votes, transitions is a mean value of the number of posted votes, w _ like is an adjustment factor of the number of posted votes, and w _ trans is an adjustment factor of the number of forwarded votes.
5. The method for computing human network activity based on open source data according to claim 2, wherein an adjustment factor w _ like of the number of praise is set to 0.01 and an adjustment factor w _ trans of the number of forwarding is set to 0.1 in the weight computation of the primary and secondary indexes;
the calculation mode of the character network activity of the single platform is as follows:
act_plat＝pri_index*0.8+sec_index*0.2
the main index is given with a weight of 0.8, and the secondary index is given with a weight of 0.2, so that the influence degree of the main index and the secondary index on the activity of the human network is reflected.
6. The method of claim 1, wherein in step three, the weight of the platform comprises an account weight and a post weight, wherein,
the account weight is defined as follows:
Figure FDA0002875538930000021
wherein, w _ zhPlatform nIs the weight of the platform n account number, zhf platform nIs the platform n account number;
the post weight is defined as follows:
Figure FDA0002875538930000031
wherein, w _ tzPlatform 1Is the weight of the platform n post, tzPlatform 1Is the number of n posts on the platform;
finally, platform weight for platform 1:
Figure FDA0002875538930000032
wherein, wPlatform 1Is the platform weight for platform 1.
7. The method for computing human network activity based on open source data as claimed in claim 1, wherein in step four, the human integrated network activity is computed by weighting and computing the activities of N platforms, and the computing formula is as follows:
Figure FDA0002875538930000033
wherein act is the network activity of the character.
8. A system for implementing the method for computing the activity of the character network based on the open source data according to any one of claims 1 to 7, wherein the system for computing the activity of the character network based on the open source data comprises:
the figure social platform data extraction unit is used for extracting figure social platform data on each platform based on a system index of figure network activity according to an evaluation object of figure activity;
the network activity calculation unit is used for realizing the network activity calculation of the single-platform character;
the weight calculation unit is used for keeping the time period of the whole platform data consistent with the whole time period of the character network activity and carrying out weight calculation on each social platform used by the character based on the account number and the post number of each platform;
and the activity calculation unit is used for realizing the activity calculation of the comprehensive network of the personnel.
9. An information data processing terminal, characterized in that the information data processing terminal is equipped with the figure network activity degree calculation system based on the open source data of claim 8 and implements the figure network activity degree calculation method based on the open source data of any one of claims 1 to 7.
10. A computer device, characterized in that the computer device comprises a memory and a processor, the memory storing a computer program which, when executed by the processor, causes the processor to carry out the steps of:
step one, extracting figure social platform data on each platform based on a system index of figure network liveness according to an evaluation object of figure liveness;
step two, realizing the network activity calculation of the single-platform figure;
step three, keeping the time period of the whole platform data consistent with the whole time period of the character network activity, and performing weight calculation on each social platform used by the character based on the account number and post number of each platform;
and step four, calculating the activity of the personnel integrated network.
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