CN112732780B - Character network liveness calculation method, system, processing terminal and computer equipment - Google Patents
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Abstract
The invention discloses a figure network liveness calculation method, a figure network liveness calculation system, a processing terminal and computer equipment, and relates to the technical field of network space cognition. Extracting the figure social platform data on each platform based on the system index of the figure network liveness according to the evaluation object of the figure liveness; the network liveness calculation of the single-platform characters is realized; the time period of the platform overall data is kept consistent with the overall time period of the character network liveness, and weight calculation is carried out on each social platform used by the character based on the account numbers and the post numbers of each platform; and (5) calculating the comprehensive network activity of the personnel. According to the method, the related data of the posting of the person is used as a main index, actions of praise, comment, forwarding and the like of other people on the user are used as secondary indexes, the actions are added into the liveness calculation dimension, and after the liveness calculation dimension is expanded, the activity frequency of the person on the current social platform is evaluated more accurately.
Description
Technical Field
The invention relates to the technical field of network space cognition, in particular to a figure network liveness calculation method and system based on open source data.
Background
The liveness has different meanings depending on the scenario. In the securities industry, liveness is commonly used to evaluate the frequency of trading a stock. In a social network, user liveness refers to the frequency of activities performed by a user on the social network, and is mainly calculated by the occurrence frequency of actions such as posting, forwarding, commenting and the like of the user.
There is currently little quantitative research on the user activity of social networks. The prior user liveness calculation is mainly based on the time, the number of the letters, the number of replies, the number of praise, the number of collection and the like of the user on a single social platform, and then the weighted calculation is carried out by combining the time and other factors. Social media such as microblogs and WeChat support the formulation of influence strategies by classifying users into active and inactive users based on the user's posting, comments, use frequency and the like of the own platform and activate the inactive users.
The existing liveness calculation method has several disadvantages:
(1) In the present internet era, every person uses a plurality of social platforms, and meanwhile, posts, replies, praise and other actions can be carried out on different social platforms, and the actions can be combined to completely reflect the network liveness of a person. The existing character liveness calculation method is only aimed at a single platform, and the calculation result cannot measure the liveness of a character network on the whole level.
(2) Most individual users cannot continuously post on the social platform, more are in browsing, commenting or praying states, and the invisible behaviors are also the manifestation of the liveness of the users. The existing method for calculating the network liveness of the characters takes the number and frequency of postings of one person and the number of comments of the person as main calculation factors, ignores the key factor of frequent interaction, and causes that the result is not consistent with the actual situation.
(3) The existing social platform generally calculates the activity of the person 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 liveness of the person cannot be estimated in an environment based on open source data.
In order to overcome the problems in the related art and based on the problems in the calculation of the character liveness, the scheme provides a character network liveness analysis method based on open source data, expands the dimension of user liveness assessment and improves the accuracy of target users in the calculation of the social network liveness; the user liveness evaluation method of the multi-social platform is introduced, the problem that the overall network liveness of the character based on open source data cannot be calculated is solved, and the blank of comprehensive evaluation of the network liveness of the character is made up. The technical scheme is as follows:
the character network liveness calculation method based on the open source data comprises the following steps:
step one, extracting figure social platform data on each platform based on a system index of the figure network liveness according to an evaluation object of the figure liveness;
step two, realizing network liveness calculation of single-platform characters;
step three, the time period of taking the overall data of the platforms is consistent with the overall time period of the activity of the figure network, and weight calculation is carried out on each social platform used by the figure based on the account number and the post number of each platform;
and step four, comprehensive network activity calculation of personnel.
In one embodiment, implementing network liveness calculations for a single platform persona includes:
the main index starts from the dimensions of the posting number, the praise number, the forwarding number and the comment number of the characters and can directly reflect the data of the activity frequency of key people on the platform;
secondary indexes, namely, data reflecting the frequent degree of the activities of the characters from the dimension side surfaces of the number of the signed praise, the number of the signed comment and the number of the forwarded post;
the weight calculation of the primary index and the secondary index is carried out, and the weight calculation is carried out on each social platform used by the person based on the account numbers and the post numbers of each platform;
the character network liveness of a single platform is calculated based on the main index, the secondary index and the weight of the main index and the secondary index.
In one embodiment, the main index is calculated as follows:
pri_index＝num_post+num_trans+num_likes+num_posts
where pri_index is the main index, num_post is the number of posts posted by people, num_trans is the number of posts forwarded by people, num_keys is the number of comments by people, and num_post is the number of comments by people.
In one embodiment, the secondary index is calculated as follows:
sec_index＝likes*w_like+posts+transs*w_trans
where sec_index is a secondary index, keys is the mean of the number of signed comments, posts is the mean of the number of signed comments, trans is the mean of the number of forwarded posts, w_like is the adjustment factor for the number of signed comments, and w_trans is the adjustment factor for the number of forwarded comments.
In one embodiment, the adjustment factor w_like of the praise number is set to 0.01 in the weight calculation of the primary and secondary indexes, and the adjustment factor w_trans of the forwarding number is set to 0.1.
In one embodiment, the personal network activity of the single platform is calculated as follows:
act_plat＝pri_index*0.8+sec _ index*0.2
the main index is given a weight of 0.8, and the secondary index is given a weight of 0.2, so that the influence degree of the main index and the secondary index on the activity of the character network is reflected.
In one embodiment, in step three, the weights of the platform include account weights and post weights, wherein,
the account weights are defined as follows:
wherein w_zh Platform n Is the weight of the platform n account number, zh f platform n Is the number of n accounts of the platform;
the post weights are defined as follows:
wherein w-tz Platform 1 Is the weight of platform n post, tz Platformn Is the platform n post count;
finally, platform weight for platform 1:
wherein w is Platform 1 Is the platform weight of platform 1.
In one embodiment, in step four, the comprehensive network activity calculation is obtained by a weighted sum calculation of N platform activities, and the calculation formula is as follows:
where act is the personality network liveness.
Another object of the present invention is to provide a system for implementing the method for calculating the liveness of a character network based on open source data, where the system for calculating the liveness of a character network based on open source data includes:
the person social platform data extraction unit is used for extracting the person social platform data on each platform based on the system index of the person network liveness according to the evaluation object of the person liveness;
the network liveness calculation unit is used for realizing network liveness calculation of the single-platform characters;
the weight calculation unit is used for taking the time period of the overall data of the platform to be consistent with the overall time period of the activity of the figure network, and carrying out weight calculation on each social platform used by the figure based on the account number and the post number of each platform;
and the activity calculating unit is used for realizing the comprehensive network activity calculation of personnel.
Another object of the present invention is to provide an information data processing terminal, which mounts the character network activity calculating system based on open source data and implements the character network activity calculating method based on open source data.
It is a further object of the present 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 the figure network liveness according to an evaluation object of the figure liveness;
step two, realizing network liveness calculation of single-platform characters;
step three, the time period of taking the overall data of the platforms is consistent with the overall time period of the activity of the figure network, and weight calculation is carried out on each social platform used by the figure based on the account number and the post number of each platform;
and step four, comprehensive network activity calculation of personnel.
The technical scheme provided by the embodiment of the invention can comprise the following beneficial effects:
according to the method, the related data of the posting of the person is used as a main index, actions of praise, comment, forwarding and the like of other people on the user are used as secondary indexes, the actions are added into the liveness calculation dimension, and after the liveness calculation dimension is expanded, the activity frequency of the person on the current social platform is evaluated more accurately.
Based on the total posting quantity and account numbers of the multiple social platforms, the influence weight of each platform on the activity of the person is calculated, the activity of the person on different social platforms is subjected to normalization processing and comprehensive calculation, and finally the overall network activity of the person is obtained. The problem that the overall network liveness of the person 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 disclosure and together with the description, serve to explain the principles of the disclosure.
Fig. 1 is a diagram of a network activity index architecture provided by the present invention.
Fig. 2 is a flowchart of figure network activity calculation provided by the present invention.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to the appended drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. The invention may be embodied in many other forms than described herein and similarly modified by those skilled in the art without departing from the spirit or scope of the invention, which is therefore not limited to the specific embodiments disclosed below.
It will be understood that when an element is referred to as being "fixed 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. The terms "vertical", "horizontal", "left", "right" and the like are used herein for illustrative purposes only and are not meant to be 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. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
(1) According to the method, the related data of the posting of the person is used as a main index, actions of praise, comment, forwarding and the like of other people on the user are used as secondary indexes, the actions are added into the liveness calculation dimension, and after the liveness calculation dimension is expanded, the activity frequency of the person on the current social platform is evaluated more accurately.
(2) Based on the total posting quantity and account numbers of the multiple social platforms, the influence weight of each platform on the activity of the person is calculated, the activity of the person on different social platforms is subjected to normalization processing and comprehensive calculation, and finally the overall network activity of the person is obtained. The problem that the overall network liveness of the person cannot be calculated is solved.
As shown in fig. 1, the character network liveness index system is mainly constructed from three dimensions of a main index, a secondary index and a platform weight. The main indexes comprise the posting number, the transfer number, the praise number and the comment number of the characters; the secondary index comprises the number of endorsements, the number of commentary on the endorsements and the number of forwarding the endorsements of the figures; the platform weight is mainly calculated on the basis of the account numbers and the post numbers of all the platforms for each social platform used by the person.
The character network liveness is dynamically changed, and the method is calculated based on the data of characters in various social platforms in a period of time.
The main flow of figure network liveness calculation is as follows:
extracting the figure social platform data on each platform based on the system index of the figure network liveness according to the evaluation object of the figure liveness;
the network liveness calculation of the single platform character mainly comprises the following aspects:
main index calculation
The main index starts from the dimensions of the posting number, the praise number, the forwarding number, the comment number and the like of the characters, and can directly reflect the data of the activity frequency of key people on the platform. The calculation mode is as follows:
pri_index＝num - post+num_trans+num_likes+num_posts
where pri_index is the main index, num_post is the number of posts posted by people, num_trans is the number of posts forwarded by people, num_keys is the number of comments by people, and num_post is the number of comments by people.
Secondary index calculation
The secondary index is counted from the posting of the person, the posting commented number and the posting forwarded number, and the data cannot directly reflect the activity frequency of the key person, but reflects the activity frequency of the key person to a certain extent from the side. The calculation mode is as follows:
sec_index＝likes*w_like+posts+transs*w_trans
where sec_index is a secondary index, keys is the mean of the number of signed points, posts is the mean of the number of signed points commented on, and trans is the mean of the number of signed points forwarded. w_like is the adjustment factor for the praise number, and w_trans is the adjustment factor for the forwarding number.
Weight calculation of primary and secondary indexes
Through statistics Facebook, twitter and other data of a plurality of social platforms, the orders of the praise, the share and the comment of a post are sequentially decreased, and the difference is about one order, so that after expert study and judgment, w_like is set to be 0.01 (the adjustment factor of the praise), and w_trans is set to be 0.1 (the adjustment factor of the forwarding number). By doing so, on one hand, the magnitude among the three can be balanced, and on the other hand, the magnitude of the secondary index can be reduced.
Character network liveness of single platform
The calculation mode of the single-platform character network liveness based on the main index, the secondary index and the weight calculation mode of the main index and the secondary index is as follows:
act_plat＝pri_index*0.8+sec_index*0.2
wherein, the main index expert gives a weight of 0.8, and the secondary index expert gives a weight of 0.2, thereby reflecting the influence degree of the main index and the secondary index on the activity of the character network.
Weights of each platform
The user base of each social platform is different, the forms of the user release content are different, and the frequency and the dependence degree of the use of the characters in different areas are also different, so that the regional characteristics are required to be combined when evaluating the network activity of the characters, and the weight occupied by each platform is comprehensively calculated by adopting the total posting amount and the posting account number of the users of the platform related to the specific area. In order to more accurately embody the importance degree of the platform, the time period of taking the whole data of the platform is consistent with the whole time period of the activity degree of the character network. The weight of a specific platform is defined as follows:
A. account weight
Wherein w_zh Platform n Is the weight of the platform n account number, zh f platform n The number of n accounts is the number of n accounts on the platform, and the other accounts are the same.
B. Post weight
Wherein w-tz Platform 1 Is the weight of platform n post, tz Platform 1 Is the platform n post count, and the other is the same.
Finally, platform weight for platform 1:
wherein w is Platform 1 Is the platform weight of platform 1.
Personnel integrated network liveness calculation
The character network liveness is calculated by the weighted sum of N platform liveness. The calculation formula is as follows:
where act is the personality network liveness.
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 adaptations, 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 is to be understood that the present disclosure is not limited to the precise arrangements and instrumentalities shown in the drawings, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the present disclosure should be limited by the attached claims.
Claims (6)
1. The character network liveness calculating method based on the open source data is characterized by comprising the following steps of:
step one, extracting figure social platform data on each platform based on a system index of the figure network liveness according to an evaluation object of the figure liveness;
step two, realizing network liveness calculation of single-platform characters; comprising the following steps:
the main index starts from the dimensions of the posting number, the praise number, the forwarding number and the comment number of the characters and can directly reflect the data of the activity frequency of key people on the platform;
secondary indexes, namely, data reflecting the frequent degree of the activities of the characters from the dimension side surfaces of the number of the signed praise, the number of the signed comment and the number of the forwarded post;
the character network liveness of the single platform is calculated based on the main index, the secondary index and the weight of the main index and the secondary index;
setting the adjustment factor w_like of the praise number to be 0.01 in the weight calculation of the primary and secondary indexes, and setting the adjustment factor w_trans of the forwarding number to be 0.1;
the figure network liveness of the single platform is calculated as follows:
act_plat＝pri_index*0.8+sec_index*0.2
wherein, the main index gives a weight of 0.8, and the secondary index gives a weight of 0.2, thereby reflecting the influence degree of the main index and the secondary index on the activity of the character network; pri_index is a primary index, and sec_index is a secondary index;
step three, the time period of taking the overall data of the platforms is consistent with the overall time period of the activity of the figure network, and weight calculation is carried out on each social platform used by the figure based on the account number and the post number of each platform; the weights of the platform include account weights and post weights, wherein,
the account weights are defined as follows:
wherein w_zh Platform 1 Weighting platform 1 account number, zh Platform 1 For the number of platform 1 account, zh Platform 2 For the number of platform 2 accounts, zh Platform 3 For the number of 3 accounts of the platform, zh Platform n Is the number of n accounts of the platform;
the post weights are defined as follows:
wherein w_tz Platform 1 Is the weight of the platform 1 post, tz Platform 1 Is the platform 1 post count, tz Platform 2 Is the platform 2 post count, tz Platform 3 Is the platform 3 post count, tz Platform n Is the platform n post count;
finally, platform weight for platform 1:
wherein w is fb Is the platform weight of platform 1;
step four, calculating the comprehensive network activity of the personnel, wherein the comprehensive network activity of the personnel is calculated by the weighted sum of the activities of N platforms, and the calculation formula is as follows:
where act is the personnel integrated network activity.
2. The method for calculating the activity of the character network based on the open source data according to claim 1, wherein the main index is calculated as follows:
pri_index＝num_post+num_trans+num_likes+num_posts
where pri_index is the main index, num_post is the number of posts posted by people, num_trans is the number of posts forwarded by people, num_keys is the number of comments by people, and num_post is the number of comments by people.
3. The method for calculating the activity of the character network based on the open source data according to claim 1, wherein the secondary index is calculated as follows:
sec_index＝likes*w_like+posts+transs*w_trans
where sec_index is a secondary index, keys is the mean of the number of signed comments, posts is the mean of the number of signed comments, trans is the mean of the number of forwarded posts, w_like is the adjustment factor for the number of signed comments, and w_trans is the adjustment factor for the number of forwarded comments.
4. A system for implementing the open source data-based character network liveness calculation method as claimed in any one of claims 1 to 3, wherein the open source data-based character network liveness calculation method comprises:
the person social platform data extraction unit is used for extracting the person social platform data on each platform based on the system index of the person network liveness according to the evaluation object of the person liveness;
the network liveness calculation unit is used for realizing network liveness calculation of the single-platform characters;
the weight calculation unit is used for taking the time period of the overall data of the platform to be consistent with the overall time period of the activity of the figure network, and carrying out weight calculation on each social platform used by the figure based on the account number and the post number of each platform;
and the activity calculating unit is used for realizing the comprehensive network activity calculation of personnel.
5. An information data processing terminal, wherein the information data processing terminal is equipped with the system of the open source data-based character network activity calculating method according to claim 4, and implements the open source data-based character network activity calculating method according to any one of claims 1 to 3.
6. 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 the figure network liveness according to an evaluation object of the figure liveness;
step two, realizing network liveness calculation of single-platform characters; comprising the following steps:
the main index starts from the dimensions of the posting number, the praise number, the forwarding number and the comment number of the characters and can directly reflect the data of the activity frequency of key people on the platform;
secondary indexes, namely, data reflecting the frequent degree of the activities of the characters from the dimension side surfaces of the number of the signed praise, the number of the signed comment and the number of the forwarded post;
the character network liveness of the single platform is calculated based on the main index, the secondary index and the weight of the main index and the secondary index;
setting the adjustment factor w_like of the praise number to be 0.01 in the weight calculation of the primary and secondary indexes, and setting the adjustment factor w_trans of the forwarding number to be 0.1;
the figure network liveness of the single platform is calculated as follows:
act_plat＝pri_index*0.8+sec_index*0.2
wherein, the main index gives a weight of 0.8, and the secondary index gives a weight of 0.2, thereby reflecting the influence degree of the main index and the secondary index on the activity of the character network; pri_index is a primary index, and sec_index is a secondary index;
step three, the time period of taking the overall data of the platforms is consistent with the overall time period of the activity of the figure network, and weight calculation is carried out on each social platform used by the figure based on the account number and the post number of each platform; the weights of the platform include account weights and post weights, wherein,
the account weights are defined as follows:
wherein w_zh Platform 1 Weighting platform 1 account number, zh Platform 1 For the number of platform 1 account, zh Platform 2 For the number of platform 2 accounts, zh Platform 3 For the number of 3 accounts of the platform, zh Platform n Is the number of n accounts of the platform;
the post weights are defined as follows:
wherein w_tz Platform 1 Is the weight of the platform 1 post, tz Platform 1 Is the platform 1 post count, tz Platform 2 Is the platform 2 post count, tz Platform 3 Is the platform 3 post count, tz Platform n Is the platform n post count;
finally, platform weight for platform 1:
wherein w is fb Is the platform weight of platform 1;
step four, calculating the comprehensive network activity of the personnel, wherein the comprehensive network activity of the personnel is calculated by the weighted sum of the activities of N platforms, and the calculation formula is as follows:
where act is the personnel integrated network activity.
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