CN110020375B - Evaluation method for influence of social network users - Google Patents
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Abstract
The invention relates to the technical field of social network research, and particularly discloses a method for evaluating influence of social network users. The invention decomposes the user influence into the user relation influence and the user resource influence, wherein the user relation influence comprises a user 1-to-1 relation influence and a user 1-to-multiple relation influence. The user resource impact mainly includes an intangible user resource impact and a tangible user resource impact. The user relationship influence is weighted with the user resource influence and is the influence of the user in the current social network. The influence evaluation method provided by the invention focuses on the user relationship and the user resource, and can evaluate the influence of the user more accurately aiming at the two factors in the social network.
Description
Technical Field
The invention relates to the field of social network research, in particular to a method for evaluating influence of social network users.
Background
In recent years, as the demands of people for obtaining information and transmitting information are increasing, the social network is a good medium for meeting the demands of people, so that the social network is rapidly developed, and the social network is a complex virtual society composed of each relatively independent user, is the hottest network application of the internet, and deeply changes the rule and mode of internet information transmission, such as the current representative social network: microblog, personal area, facebook, twitter, etc. The participation of interaction communication of social networks at any time and any place becomes a requirement. The users in different regions and different environments are interconnected, so that the users can be helped to identify the most influential users in the current social network, the users can conveniently identify high-power users in some social networks, meanwhile, merchants are helped to screen out proper product popularization people to select, on the other hand, the users with high influential effects on public opinion guidance aiming at hot events to a certain extent, the people are reasonably guided, and further, the public opinion direction in the social network is controlled to have higher application value. The online social network gets rid of the limitation of regions, is more dependent on the content displayed by the users on the social network, and lacks trust relationship established by face-to-face communication and communication among different users in the traditional social network. Therefore, the reasonable selection of a plurality of characteristics and the establishment of a more scientific social network user influence evaluation method have important significance.
According to the current mainstream social network user influence evaluation method, on one hand, the PageRank is used for evaluating the link weight, and the influence of a certain user is judged by combining the influence transmission mechanism. There are also scholars that use the number of followers in the network or the propagation of messages to determine the magnitude of the user's impact. However, these methods require repeated iterations, and have high time complexity and weak general applicability. And none of them relates to a main aspect of influence, namely that the core relationship of influence is the influence of people on other people. A user establishes influence because content information output by the user or interpersonal resources mastered by the user have a certain influence on other people, and thus, personal influence is established.
In summary, the existing influence evaluation method for the user individuals in the social network faces the scene of the large-scale social network, and is difficult to meet the requirements on analysis effect and efficiency.
Disclosure of Invention
The method for evaluating the influence of the social network user can accurately and effectively evaluate the influence of the social network user.
The method for evaluating the influence of the social network user provided by the invention comprises the following steps: (1) Calculating the 1-to-1 relationship influence of users on the vermicelli and the 1-to-many relationship influence of users in the participated communities, distributing different weights, and taking and calculating the relationship influence of the users; the influence of the 1-to-1 relationship generated by the user on the fan-shape is the proportion of the interaction time of the user and the single fan-shape to the total interaction time of the user in the social activity, and the total interaction time of the user in the social activity is the sum of the interaction time of the user and all fan-shape; the 1-pair multi-relation influence refers to the proportion of friends of the user in the community to the total number of users in the community, and the number of users in each community is taken as a corresponding weight to summarize and analyze the influence of the user in the community participated in; (2) Calculating influence forces generated by the intangible resources of the user and the tangible resources of the user respectively, and distributing different weights to obtain and acquire the influence forces of the user resources; the user intangible resource influence refers to influence constructed by the information of graduation schools, professional fields, work units, expert identities, fan numbers of the users, and the large V relations with the high fan numbers, and the user intangible resource influence refers to influence generated by content output of the users in a social network, and the weight parameters of the user intangible resources and the user intangible resource influence depend on the ratio of the fan numbers of the users to the number of the users influenced by user activities; (3) And respectively distributing different weights by combining the user relationship influence and the user resource influence, and taking and calculating the final influence of the user.
Wherein the weight ratio of the 1-to-1 relationship influence and the 1-to-multiple relationship influence is 4:1.
The content output of the user in the social network comprises resources pushed by the user, including articles, pictures, audio and video media resources forwarded by the user and activities initiated by the user.
The intangible resource influence is calculated by using Gaussian kernel density estimation on the basis of the relevant statistical parameters of the number of social relations in the social network.
The method for calculating the influence of the physical resources is that the number of users which can be influenced by social activities carried out by users in a certain period is the proportion of the number of current active users in the social network, and the number of active users is 20% of the number of users in the whole social network.
And the user analyzes the influence of the 1-to-1 relationship generated by the fan by using 20% of user data before the ranking of the interaction frequency.
The method comprises the steps of using the related statistical parameters of the number of social relations in the social network as a basis to enable the number of user fans to be used as parameters.
When the user is an expert in a certain field, the influence mean value of the first 20% of users with strong influence in the number of the user fan is used as an adjustment parameter when the influence of the intangible resources is evaluated.
Wherein, the weight ratio of the user resource influence and the user relation influence is 4:1.
Wherein the weight parameters of the user intangible resource and the user tangible resource influence are set to be the ratio of 1:4.
The influence evaluation method provided by the invention focuses on the user relationship and the user resource, and can evaluate the influence of the user more accurately aiming at the two factors in the social network:
(1) And a plurality of factors related to the user are fully considered, including friends with which the user communicates and influence of the user on other users in the participating communities.
(2) The influence of a plurality of special purposes including the number of the user fans as the user in the whole social network is considered, so that the influence of the user with a plurality of fans in the social network is improved.
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FIG. 1 is a flowchart of a method for evaluating influence of social network users according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to fig. 1 and the specific embodiment. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not to be construed as limiting the invention.
Referring to fig. 1, a user influence is decomposed into a user relationship influence and a user resource influence, wherein the user relationship influence includes a user 1-to-1 relationship influence and a user 1-to-multiple relationship influence. And the influence of the relation of 1 to 1 mainly refers to the proportion of the time of the user to the total time of the user to the social activity, wherein the time of the user to the total time of the user to the social activity. The total interaction time of the users in the social activities refers to the sum of interaction time of the users and all the fan-shaped fans, and the influence of 1 on the multiple relations mainly refers to the influence generated by the users participating in a plurality of communities and then in the communities. The influence depends on the proportion of the users with friend relations in the community of the users to the total number of the users in the community.
The user resource impact mainly includes an intangible user resource impact and a tangible user resource impact. The influence of tangible resources mainly refers to the influence of media resources such as articles, messages, pictures and the like pushed or forwarded by users, and the influence generated by organizing activities, developing projects and the like, and the influence of intangible resources mainly emphasizes the influence generated by users with certain attributes, wherein the attributes comprise a plurality of characteristics of the users, such as graduation schools, professional fields, work units, expert identities, vermicelli numbers and the like, and also comprise some connection capability, such as a certain large V relationship with a high vermicelli number and the like.
The sum of the weights of the user relationship influence and the user resource influence is the influence of the user in the current social network. The specific evaluation method comprises the following steps:
step 1: calculating 1-to-1 influence of users on vermicelli and 1-to-many influence of users in a participated community by adopting defined user relationship influence, and distributing different weights to obtain and calculate the user relationship influence;
step 2: calculating influence forces generated by the intangible resources of the user and the tangible resources of the user respectively by adopting defined user resource influence forces, and distributing different weights to obtain and calculate the user resource influence forces;
step 3: and respectively distributing different weights by combining the user relationship influence and the user resource influence, and taking and calculating the final influence of the user.
The relationship influence of the user includes a 1-to-1 relationship influence and a 1-to-multiple relationship influence. The 1-to-1 relationship influence mainly refers to the proportion of the time that a user communicates with a single fan of the user to the total time that the user communicates in social activities. Analysis is performed here using the top 20% user data of the interaction frequency rank. The influence of the user on the community is analyzed mainly by 1, the proportion of friends in the community to the total number of users in the community is analyzed, and the number of users in each community is taken as the corresponding weight to gather and analyze the influence of the user on the community in which the user participates. The weight ratio of the 1-to-1 relationship influence and the 1-to-multiple relationship influence is 4:1.
The intangible resource influence mainly refers to influence constructed by the information of qualification, identity and the like of a user. Because the influence cannot be reasonably and quantitatively evaluated, the method uses the number of the user fan as a parameter and combines a Gaussian kernel density estimation method to judge the intangible resources of the user. In this case, the number of fans of the user may be small considering that the user may be an expert in a certain field, but the user has a strong influence in the field. Therefore, the method and the device use the influence mean value of the first 20% of users with strong influence in the number of the user fans as the adjustment parameter so as to promote the users with small number of the fan fans.
The tangible resource influence mainly refers to influence caused by content output of a user, wherein the content output comprises resources pushed by the user, and specifically comprises media resources such as articles, pictures, audios and videos forwarded by the user, activities initiated by the user and the like. The calculation method of the physical resource influence is that the number of users which can be influenced by social activities carried out by users in a certain period is the proportion of the number of currently active users in the social network, wherein the currently active users refer to users with the social activity times of the users in a certain period being greater than or equal to the 20 th quantile of the social activity times of all the users in the period from high to low. For simplicity, the number of active users may be replaced with 20% of the total social network users.
The user resource influence mainly comprises intangible resource influence based on the number of user fan shapes and user tangible resource influence based on social activities of the users. The number of fans of the users does not substantially change too much in the set time range, and if so, the average number of fans of the users in the time period is used as a calculation basis. Using the relevant statistical parameters of the number of social relations in the social network as a basis, the intangible resource influence of the user is calculated by using the Gaussian kernel density estimation. Meanwhile, the influence of the influence in the user fan on the final influence of the user is considered. And judging the physical resource influence of the user by using the proportion of the user influenced by the social activities of the user to the number of active users in the current social network. The active users refer to users with the social activity times greater than or equal to the social network user activity times ranked from high to low and 20 th quantiles in a certain period. Different weights are respectively given to the tangible resource influence and the intangible resource influence, wherein the weight ratio of the tangible resource to the intangible resource is 4:1, and then the resource influence of a user is calculated.
The user resource influence and the user relationship influence are used to calculate a final influence of the user within the social network. The weight ratio of the two is 4:1.
The above-described embodiments of the present invention do not limit the scope of the present invention. Any of various other corresponding changes and modifications made according to the technical idea of the present invention should be included in the scope of the claims of the present invention.
Claims (9)
1. The method for evaluating the influence of the social network user is characterized by comprising the following steps of: (1) Calculating the 1-to-1 relationship influence of users on the vermicelli and the 1-to-many relationship influence of users in the participated communities, distributing different weights, and taking and calculating the relationship influence of the users; the influence of the 1-to-1 relationship generated by the user on the fan-shape is the proportion of the time of the user to the single fan-shape to the total time of the user in the social activity, and the total time of the user in the social activity is the sum of the time of the user to all fan-shape; the 1-pair multi-relation influence refers to the proportion of friends of the user in the community to the total number of users in the community, and the number of users in each community is taken as a corresponding weight to summarize and analyze the influence of the user in the community participated in; (2) Calculating influence forces generated by the intangible resources of the user and the tangible resources of the user respectively, and distributing different weights to obtain and acquire the influence forces of the user resources; the user intangible resource influence refers to influence of information, namely influence generated by content output of the user in a social network, wherein the weight parameters of the user intangible resource and the user intangible resource influence depend on the ratio of the number of the fan of the user to the number of the user influenced by user activities;
(3) Combining the user relationship influence and the user resource influence, respectively distributing different weights, and taking and calculating the final influence of the user;
the intangible resource influence is calculated by using Gaussian kernel density estimation on the basis of the relevant statistical parameters of the number of social relations in the social network.
2. The method of evaluating social network user influence of claim 1, wherein a weight ratio of 1-to-1 relationship influence to 1-to-multiple relationship influence is 4:1.
3. The method of claim 1, wherein the content output of the user in the social network comprises a resource pushed by the user or an article, picture, audio-video media resource forwarded by the user, or an activity initiated by the user.
4. The method for evaluating influence of users in a social network according to claim 1, wherein the method for calculating influence of tangible resources is a proportion of the number of users that can be influenced by social activities performed by the users in a certain period to the number of currently active users in the social network, and the number of active users is 20% of the total number of users in the social network.
5. The method of claim 1, wherein the user's 1-to-1 relationship impact generated by the fan is analyzed using the top 20% of the user data ranked using the frequency of interaction.
6. The method for evaluating the influence of users on a social network according to claim 1, wherein the method uses the relevant statistical parameters of the number of social relations in the social network as a basis to make the number of fan-shaped users as parameters.
7. The method for evaluating influence of users in social network as set forth in claim 6, wherein when the user is an expert in a field, an influence mean of the first 20% of users with strong influence in the number of fan-shaped users is used as an adjustment parameter when evaluating influence of intangible resources.
8. The method of claim 1, wherein the weight ratio of the user resource influence to the user relationship influence is 4:1.
9. The method of evaluating the influence of a user on a social network according to claim 1, wherein the weight parameters of the user intangible resources and the user tangible resources influence are set to a ratio of 1:4.
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