Information processing device, information processing method, and information processing program

Abstract

Provided is technology that makes it possible to select an SNS posting user suitable for transmitting information relating to a target commodity and / or a target service using a social networking service (SNS).　An information processing device according to the present disclosure comprises: a first acquiring unit that acquires a target category to which a target product or the like belongs; a second acquiring unit that acquires posting data posted by posting users who post user-generated content to an SNS; an estimating unit that estimates an information transmission field for each of a plurality of posting users, and that classifies the posting data posted by each posting user into certain characteristic groups for each of the plurality of posting users and estimates the information transmission field of each posting user on the basis of the characteristic groups; and an extracting unit that extracts, from among the plurality of posting users, a posting user suitable for transmitting information related to the target product or the like by means of user-generated content, on the basis of a degree of matching calculated by comparing the target category for the target product or the like and the information transmission field for each of the plurality of posting users.

Description

Information Processing Apparatus, Information Processing Method, and Information Processing Program

[0001] The present invention relates to an information processing apparatus, an information processing method, and an information processing program for assisting in the transmission of information regarding a target product and / or a target service.

[0002] In recent years, with the remarkable increase in the utilization rate of social networking services (SNS), the content existing on SNS has begun to be utilized for marketing purposes. And in such SNS marketing, from the enterprise side that wants to conduct marketing of a target product or a target service, there is a tendency to request an influencer with influence in SNS transmission to promote the product or service using SNS.

[0003] On the other hand, a technology is also known in which SNS users themselves propose the promotion of products they wish to introduce to enterprises.

[0004] For example, Patent Document 1 discloses a co-proposal device that proposes the promotion of a product of a predetermined enterprise by a user terminal used by an SNS user, and determines whether to accept this proposal by an enterprise terminal used by the enterprise. In this co-proposal device, when an SNS user conducts promotion on SNS, it is determined based on predetermined conditions whether a product to be introduced that the SNS user wishes to introduce can be posted on SNS, and when it is determined that the conditions for posting are met, a posting proposal of the product to be introduced from the SNS user is transmitted to the enterprise terminal.

[0005] Japanese Patent Application Laid-Open No. 2022-133148

[0006] Conventionally, in SNS marketing where the enterprise side asks SNS users to cooperate in promoting a target product or a target service, there has been a problem in selecting SNS users suitable for promoting the target product or service. For example, even an influencer with influence in SNS transmission for a specific category may not necessarily have influence in transmitting for products or services belonging to another category.

[0007] On the other hand, the technology described in Patent Document 1 proposes to companies that SNS users promote products they wish to introduce using SNS, so it seems that discrepancies in product categories where the influence of SNS dissemination can be effectively exerted are less likely to occur. However, with the technology described in Patent Document 1, the products that SNS users wish to introduce must be registered in advance. Therefore, companies are forced to select users who are suitable for disseminating information using SNS from among the SNS users who have proposed posting products they wish to introduce. This significantly reduces the degree of freedom that companies have in selecting SNS users who will cooperate in promoting their target products or services. Thus, there is still room for improvement in the technology for selecting users who are suitable for disseminating information about target products and / or target services using SNS from among SNS posting users who post user-generated content to SNS.

[0008] The purpose of this disclosure is to provide a technology that enables the selection of SNS posting users suitable for disseminating information about the target product or / or service using SNS.

[0009] The information processing device disclosed herein is an information processing device that supports the dissemination of information regarding the target product and / or target service. The information processing device comprises: a first acquisition unit that acquires a target category, which is the category to which the target product and / or target service belongs; a second acquisition unit that acquires posted data posted to a social networking service by posting users who post user-generated content to the social networking service; an estimation unit that estimates the information dissemination field for each of the multiple posting users, by classifying the posted data posted by the posting user into predetermined characteristic groups for each of the multiple posting users, and estimating the information dissemination field based on the characteristic groups; and an extraction unit that extracts posting users suitable for disseminating information regarding the target product and / or target service by user-generated content from among the multiple posting users, based on the degree of agreement calculated by comparing the target category for the target product and / or target service with the information dissemination field for each of the multiple posting users. The information processing device may further comprise a provisioning unit that provides information regarding the posting users extracted by the extraction unit to business users who conduct business related to the target product and / or target service.

[0010] Furthermore, in the above-described information processing device, the estimation unit classifies the posted data into a group of characteristics that includes a content classification to which a predetermined product category and / or service category is assigned to the content contained in the posted data, estimates the information field related to the product and / or service category as the information dissemination field, and the extraction unit may extract from among a plurality of posting users the posting user whose information dissemination field includes the target category. By extracting posting users suitable for disseminating information about the target product or service in this way, it is possible to suppress situations in which discrepancies occur in product and service categories in which the influence of dissemination by social networking services (SNS) can be effectively exerted, and it is possible to extract users from among many posting users who can effectively exert the influence of dissemination by SNS. Furthermore, when selecting posting users to cooperate in promoting the target product or service, a suitable degree of freedom can be maintained.

[0011] Furthermore, the information processing device of this disclosure may further include a third acquisition unit that acquires predetermined attributes of business users who conduct business related to the target products and / or target services. The estimation unit classifies the posted data into a group of characteristics that includes a user characteristic classification to which a predetermined user characteristic category is assigned to the content contained in the posted data, estimates the impression field based on the user characteristic classification as the information dissemination field, and the extraction unit may extract from among a plurality of posting users the posting users whose information dissemination field includes the attributes of the business user. This allows posting users who match the attributes of the business user to act as spokespeople for the business user and disseminate information about the target products and services widely and effectively without damaging the image of the business user.

[0012] Furthermore, the information processing device of this disclosure may further include a fourth acquisition unit that acquires information about target users, who are users that a business operator conducting business related to the target product and / or target service expects to appeal to the target product and / or target service through information dissemination.The estimation unit then classifies the posted data into a group of characteristics that includes a user classification relating to the users who view the posted data, estimates the area of ​​influence based on the user classification as the information dissemination area, and the extraction unit may extract from among a plurality of posting users the posting users whose information dissemination area includes the target users.This makes it possible to effectively reach target users.

[0013] In this case, the fourth acquisition unit can acquire information about the target user based on a predetermined analysis of user-generated content posted by the business user to the social networking service. The second acquisition unit may also acquire the number of times the posted data has been displayed to the viewing user, and the extraction unit may prioritize extracting posting users from among the multiple posting users, with those posting more frequently displayed to the viewing user.

[0014] Furthermore, in the information processing apparatus of this disclosure, the estimation unit classifies the posted data into a group of characteristics that includes a response classification relating to the progression of predetermined responses from users viewing the posted data, estimates the field relating to the freshness of information based on the response classification as the information dissemination field, and the extraction unit may preferentially extract from among a plurality of posting users the most recent posting users have in terms of the response classification. This makes it possible to preferentially extract posting users who have high information freshness and can get responses from many users viewing the data in information dissemination via SNS, thereby making the influence of dissemination via SNS more effective.

[0015] Furthermore, the information processing device described above may further include a calculation unit that calculates the overall contribution to information dissemination based on the individual contributions set for each of the multiple factors that contribute to the dissemination of information about the target product and / or target service by posting users. Here, the multiple factors include content classification, to which a predetermined product category and / or service category is assigned to the content included in the posting data; user characteristic classification, to which a predetermined user characteristic category is assigned to the content included in the posting data; viewing user classification, to which viewing users view the posting data; and reaction classification, to which a predetermined reaction from the viewing users progresses. The extraction unit can preferentially extract posting users with higher overall contributions from among the multiple posting users. This allows for the extraction of posting users by considering multiple factors that contribute to information dissemination via SNS and integrating these factors, thereby enabling the selection of posting users more suitable for information dissemination using SNS.

[0016] Furthermore, the information processing device of this disclosure may further include an evaluation unit that evaluates the degree of work performance of the posting user in disseminating information about the target product and / or target service, which is predicted in advance based on predetermined information relating to the posting user's past information dissemination work. The extraction unit may then extract the posting user taking into account the degree of work performance. Furthermore, the calculation unit described above may calculate the overall contribution by taking into account the degree of work performance in addition to the multiple factors. According to this, when extracting posting users suitable for disseminating information using SNS by comprehensively considering multiple factors that contribute to disseminating information about the target product or target service, users with a higher predicted degree of work performance can be extracted preferentially. As a result, information dissemination via SNS will be carried out with greater certainty.

[0017] Furthermore, the information processing device of this disclosure may further include: an effect estimation unit that estimates the effect of disseminating information about the target product and / or target service based on a predetermined analysis of the posted data posted by the extracted posting users; an incentive calculation unit that calculates an incentive that can be given to the extracted posting users based on the estimated effect, wherein the incentive increases as the estimated effect increases; and a notification unit that notifies the extracted posting users of the calculated incentive. According to this, in SNS marketing, it is possible to widely recruit SNS posting users who have influence over disseminating information about the target product or target service via SNS, and to notify the SNS posting users in advance of an incentive for SNS marketing that is appropriate for them, thereby encouraging them to cooperate in disseminating information about the target product or target service in a way that provides them with appropriate incentives.

[0018] Furthermore, in the above-described information processing device, the effect estimation unit may estimate that the greater the number of times the extracted posting user is displayed to the viewing user, the greater the effect of the information dissemination.

[0019] Alternatively, the effect estimation unit may estimate that the greater the number of responses from viewing users for the extracted posting users, the greater the effect of the information dissemination.

[0020] Alternatively, the effect estimation unit may estimate that the greater the degree of agreement calculated by comparing the information dissemination field and the target category for the extracted posting users, the greater the effect on information dissemination.

[0021] Alternatively, the effect estimation unit may estimate that the greater the degree of agreement calculated by comparing the extracted posting user classification with the target category, the greater the effect on information dissemination.

[0022] Alternatively, the effect estimation unit may estimate that for each of the extracted posting users, the higher the overall contribution, the greater the effect on information dissemination.

[0023] Furthermore, this disclosure can be viewed from the perspective of a computer-based information processing method. Specifically, the information processing method of this disclosure is an information processing method that supports the dissemination of information about a target product and / or target service, wherein the computer performs the following steps: a first acquisition step of acquiring a target category, which is the category to which the target product and / or target service belongs; a second acquisition step of acquiring posted data posted to a social networking service by posting users who post user-generated content to the social networking service; an estimation step of estimating the information dissemination field for each of the multiple posting users, which involves classifying the posted data posted by the posting user into predetermined characteristic groups, and estimating the information dissemination field based on the characteristic groups; and an extraction step of extracting posting users from among the multiple posting users who are suitable for disseminating information about the target product and / or target service using user-generated content, based on the degree of agreement calculated by comparing the target category for the target product and / or target service with the information dissemination field for each of the multiple posting users.

[0024] Furthermore, this disclosure can be viewed from the perspective of an information processing program. Specifically, the information processing program of this disclosure is an information processing program that supports the dissemination of information about a target product and / or target service, and causes a computer to execute: a first acquisition step of acquiring a target category, which is the category to which the target product and / or target service belongs; a second acquisition step of acquiring posted data posted to a social networking service by posting users who post user-generated content to the social networking service; an estimation step of estimating the information dissemination field for each of the multiple posting users, which involves classifying the posted data posted by the posting user into predetermined characteristic groups for each of the multiple posting users, and estimating the information dissemination field based on the characteristic groups; and an extraction step of extracting posting users from among the multiple posting users who are suitable for disseminating information about the target product and / or target service using user-generated content, based on the degree of agreement calculated by comparing the target category for the target product and / or target service with the information dissemination field for each of the multiple posting users.

[0025] According to this disclosure, it is possible to select SNS posting users who are suitable for disseminating information about the target product or / or target service using SNS.

[0026] This figure shows the schematic configuration of the information processing system in the first embodiment. This figure shows the components of the server included in the information processing system in the first embodiment in more detail, as well as the components of the user terminal that communicates with the server. This is the first figure illustrating the operation flow of the information processing system in the first embodiment. This is the first figure for explaining the information dissemination field based on characteristic groups in the first embodiment. This is the first figure for explaining how to extract users suitable for disseminating information about a target product using user-generated content posted on SNS in the first embodiment. This figure shows an example where, in the user extraction embodiment in Figure 5, a response classification related to the progression of engagement is further considered. This is the second figure illustrating the operation flow of the information processing system in the first embodiment. This is the second figure for explaining the information dissemination field based on characteristic groups in the first embodiment. This is the second figure for explaining how to extract users suitable for disseminating information about a target product using user-generated content posted on SNS in the first embodiment. This is the third figure illustrating the operation flow of the information processing system in the first embodiment. This is the third figure for explaining the information dissemination field based on characteristic groups in the first embodiment. The third figure illustrates a method for extracting users suitable for disseminating information about a target product using user-generated content posted on social networking services (SNS) in the first embodiment. The second figure illustrates the flow of operation of the information processing system in the second embodiment.

[0027] Embodiments of this disclosure will be described below with reference to the drawings. The configurations of the following embodiments are illustrative, and this disclosure is not limited to the configurations of these embodiments.

[0028] <First Embodiment> The outline of the information processing system in the first embodiment will be described with reference to Figure 1. Figure 1 is a diagram showing the schematic configuration of the information processing system in this embodiment. The information processing system 100 according to this embodiment is composed of a network 200, a server 300, and a user terminal 400. The information processing system disclosed herein is a system that supports the dissemination of information about target products and target services, and this support for information dissemination is performed by the server 300. In the following description, among the users who use the information processing system 100, users who post user-generated content to social networking services (SNS) will be referred to as SNS posting users, and users who conduct business related to the above-mentioned target products and target services will be referred to as business operators. Both SNS posting users and business operators may possess a user terminal 400.

[0029] Network 200 is, for example, an IP network. Network 200 can be wireless, wired, or a combination of both, as long as it is an IP network. For example, in the case of wireless communication, the user terminal 400 may access a wireless LAN access point (not shown) and communicate with the server 300 via a LAN or WAN. Furthermore, network 200 is not limited to these examples and may also be, for example, a public switched telephone network, an optical fiber line, an ADSL line, or a satellite communication network.

[0030] Server 300 is connected to user terminals 400 via network 200. Note that in Figure 1, for the sake of simplicity, one server 300 and four user terminals 400 are shown, but it goes without saying that the configuration is not limited to these.

[0031] The server 300 can be any electronic computer device with processing capabilities for arithmetic and processing operations such as data acquisition, generation, and updating. For example, it may be a personal computer, server, mainframe, or other electronic device. In other words, the server 300 can be configured as a computer having a processor such as a CPU or GPU, main memory such as RAM or ROM, and auxiliary storage such as EPROM, hard disk drive, and removable media. The removable media may be, for example, a USB memory stick or a disk recording medium such as a CD or DVD. The auxiliary storage device stores the operating system (OS), various programs, various tables, etc.

[0032] Furthermore, the server 300 may use SaaS (Software as a Service), PaaS (Platform as a Service), or IaaS (Infrastructure as a Service) via a cloud server as appropriate, without providing dedicated software, hardware, or OS for the information processing system 100 according to this embodiment.

[0033] The user terminal 400 can be any electronic device such as a mobile terminal owned by a user of the information processing system 100 (this includes SNS posting users and business users), and may be other terminal devices such as mobile terminals, tablet terminals, smartphones, wearable devices, personal computers, etc.

[0034] Next, a detailed explanation of the components of the server 300 will be given based on Figure 2. Figure 2 shows in more detail the components of the server 300 included in the information processing system 100 in the first embodiment, as well as the components of the user terminal 400 that communicates with the server 300.

[0035] The server 300 has a communication unit 301, a storage unit 302, and a control unit 303 as functional units. It loads a program stored in an auxiliary storage device into the working area of ​​the main memory and executes it. Through the execution of the program, each functional unit is controlled, thereby enabling the realization of each function that matches the predetermined purpose of each functional unit. However, some or all of the functions may be implemented by hardware circuits such as ASICs or FPGAs.

[0036] Here, the communication unit 301 is a communication interface for connecting the server 300 to the network 200. The communication unit 301 is comprised of, for example, a network interface board and a wireless communication circuit for wireless communication. The server 300 is connected to a user terminal 400 and other external devices via the communication unit 301 so as to be able to communicate with them.

[0037] The storage unit 302 comprises a main memory and an auxiliary storage device. The main memory is a memory where programs executed by the control unit 303 and data used by said control programs are stored. The auxiliary storage device is a device where programs executed by the control unit 303 and data used by said control programs are stored. The storage unit 302 stores data transmitted from user terminals 400 and the like. The storage unit 302 stores information about target categories and target users, which are the categories to which the business user wishes to disseminate information about using SNS, as well as attributes of the business user and a set of characteristics for each SNS posting user, which will be described later. The server 300 can acquire data transmitted from user terminals 400 and other external devices via the communication unit 301.

[0038] The control unit 303 is a functional unit that manages the control performed by the server 300. The control unit 303 can be implemented by a processing unit such as a CPU. The control unit 303 is further composed of six functional units: a first acquisition unit 3031, a second acquisition unit 3032, a third acquisition unit 3033, a fourth acquisition unit 3034, an estimation unit 3035, and an extraction unit 3036. Each functional unit may be implemented by executing a stored program using the CPU.

[0039] The first acquisition unit 3031 acquires the target category, which is the category to which the target product or service that the business user wishes to disseminate information about using SNS belongs. Here, the first acquisition unit 3031 acquires the target category by acquiring information transmitted from the business user's user terminal 400 and stores it in the storage unit 302.

[0040] In this embodiment, the user terminal 400 has a communication unit 401, an input / output unit 402, and a storage unit 403 as functional units. The communication unit 401 is a communication interface for connecting the user terminal 400 to the network 200, and is configured to include, for example, a network interface board and a wireless communication circuit for wireless communication. The input / output unit 402 is a functional unit for displaying information transmitted from the outside via the communication unit 401, and for inputting information when transmitting information to the outside via the communication unit 401. The storage unit 403 is configured to include a main memory and an auxiliary memory, similar to the storage unit 302 of the server 300.

[0041] The input / output unit 402 further includes a display unit 4021, an operation input unit 4022, and an image / audio input / output unit 4023. The display unit 4021 has the function of displaying various information and is implemented by, for example, an LCD (Liquid Crystal Display) display, an LED (Light Emitting Diode) display, an OLED (Organic Light Emitting Diode) display, etc. The operation input unit 4022 has the function of receiving operation input from the user and is specifically implemented by soft keys such as a touch panel or hard keys. The image / audio input / output unit 4023 has the function of receiving image input such as still images and videos and is specifically implemented by a camera using an image sensor such as Charged-Coupled Devices (CCD), Metal-oxide-semiconductor (MOS), or Complementary Metal-Oxide-Semiconductor (CMOS). The image / audio input / output unit 4023 also has the function of receiving audio input and output and is specifically implemented by a microphone or speaker.

[0042] In this configuration, the business user can use the user terminal 400 to transmit information about the target products and services to the server 300. The server 300 may also provide the business user's user terminal 400 with an interface for inputting information about the target products and services. In this case, the business user can transmit information about the target products and services to the server 300 by inputting information into the interface via the user terminal 400.

[0043] The second acquisition unit 3032 acquires posted data that has been posted to a social networking service (SNS) by an SNS posting user who posts user-generated content to the SNS.

[0044] Here, the above social networking service (SNS) is, for example, X (registered trademark), Instagram (registered trademark), TikTok (registered trademark), Facebook (registered trademark), etc. In recent years, since the number of users using such SNS has been increasing rapidly, business user may hope to promote target products or target services by using SNS marketing that utilizes the content existing on the SNS for marketing purposes.

[0045] Here, the second acquisition unit 3032 can acquire the above post data by using, for example, a well-known API that acquires various data of X (registered trademark).

[0046] The third acquisition unit 3033 acquires predetermined attributes about business users who hope to conduct SNS marketing for target products or target services. Here, the above attributes are, for example, the industry type of the business conducted by the business user, the history of the business user (established, emerging, etc.), the area where the business conducted by the business user expands, and the corporate image of the business user (solid, free, logical, passionate, etc.). The third acquisition unit 3033 can acquire the above attributes by acquiring the information transmitted from the user terminal 400 of the business user and store it in the storage unit 302.

[0047] The fourth acquisition unit 3034 acquires information about target users who are users expected to appeal by information dissemination for target products or target services by the business user. The fourth acquisition unit 3034 may acquire the information about the above target users by acquiring the information transmitted from the user terminal 400 of the business user, or may acquire the information about the target users based on a predetermined analysis described later.

[0048] The estimation unit 3035 estimates the information dissemination field for each SNS posting user. Here, the estimation unit 3035 classifies the post data posted by each of the plurality of SNS posting users into a predetermined characteristic group, and estimates the information dissemination field based on the characteristic group. The details of the process executed by the estimation unit 3035 will be described based on FIG. 3 described later.

[0049] The extraction unit 3036 extracts users from among multiple SNS posting users who are suitable for disseminating information about the target product or / or service through user-generated content posted on SNS. Here, the extraction unit 3036 extracts the above based on the degree of match calculated by comparing the target category for the target product or service with the information dissemination field for each of the multiple SNS posting users. Details of the processing performed by the extraction unit 3036 will be explained later with reference to Figure 3.

[0050] Here, the operation flow of the information processing system 100 in this embodiment will be described. Figure 3 is a first diagram illustrating the operation flow of the information processing system 100 in this embodiment. Figure 3 illustrates the operation flow between the server 300, the user terminal 400, and the SNS server 500 in the information processing system 100 in this embodiment, and the processing performed by the server 300, the user terminal 400, and the SNS server 500. The flow illustrated in Figure 3 illustrates the operation flow between the server 300, the user terminals 400 of SNS posting users and business users, and the SNS server 500, and the processing performed by the server 300, the user terminals 400 of SNS posting users and business users, and the SNS server 500. The SNS server 500 described above is a server that provides the social networking service according to this embodiment.

[0051] In this embodiment, first, information regarding the target product for which the business user wishes to conduct SNS marketing is input to the business user's user terminal 400 (S101). Here, the above information includes, for example, the product name, description, specifications, and price of the target product. This information is then transmitted from the business user's user terminal 400 to the server 300.

[0052] Then, the server 300 acquires the information transmitted from the business user's user terminal 400 (S102). The server 300 then stores the target category, which is the category to which the above-mentioned target product belongs, in the storage unit 302 (S103). At this time, the server 300 can acquire the target category to which the target product belongs by comparing, for example, information including the product name and description of the target product with a database in which product categories are defined, and store this in the storage unit 302. In addition, if the business user has entered information regarding the category to which the target product belongs in the process of S101 above, the server 300 may store the target category in the storage unit 302 based on that information.

[0053] Furthermore, user information, including the SNS account name and contact information, is entered into the user terminal 400 of the SNS posting user (S104). Then, the user information is transmitted from the user terminal 400 of the SNS posting user to the server 300.

[0054] Then, the server 300 performs user registration for the SNS posting user based on the information transmitted from the SNS posting user's user terminal 400 (S105). This user registration is a process in the information processing system 100 according to this embodiment to register that the user will conduct SNS marketing for the target product as an SNS posting user. Note that the user registration process may be performed in advance, not according to this flow.

[0055] Then, when a user-generated content is posted to X (registered trademark) on the user terminal 400 of an SNS posting user (S106), it is stored as posted data in the SNS server 500 (S107).

[0056] Server 300 accesses the SNS server 500 and obtains information about posted data submitted by registered SNS posting users from among the user-generated content stored in the SNS server 500 and distributed via the network 200 (S108). Here, Server 300 can obtain posted data by SNS posting users using, for example, a well-known API for obtaining various data of X (registered trademark).

[0057] Then, the server 300 performs an estimation process to estimate the information dissemination field based on user-generated content posted to SNS for each of the multiple SNS posting users registered in the information processing system 100 (S109). This will be explained with reference to Figure 4.

[0058] Figure 4 is the first diagram illustrating the information dissemination field based on the characteristic group in this embodiment. In this flow, for each of the multiple SNS posting users, the server 300 classifies the posting data by the SNS posting user into a characteristic group that includes a content classification to which a predetermined product category and / or service category is assigned to the content contained in the posting data. Then, the server estimates the information field related to the product and / or service category as the information dissemination field of the SNS posting user having a characteristic group that includes such a content classification.

[0059] In the example shown in Figure 4, the content classification of the posted data by SNS user A is such that 75% of the content is assigned the product category "beauty products" and 20% is assigned the product category "food products". Based on this set of characteristics of SNS user A, including this content classification, server 300 estimates the information field of SNS user A as the beauty field, which is the product category with the largest proportion in the content classification, namely "beauty products". Server 300 can classify the posted data, obtained using a well-known API for acquiring various data from X (registered trademark), into the above set of characteristics by applying well-known natural language processing and image recognition processing.

[0060] On the other hand, the content classification of the data posted by SNS user B is as follows: 40% of the content is assigned the product category "instant noodles," 30% is assigned the product category "wine," and 20% is assigned the service category "movies." Based on this content classification and the characteristics of SNS user B, server 300 estimates the information field of SNS user B as the food and beverage field, which is related to the product categories "instant noodles" and "wine" in proportion to the content classification.

[0061] Thus, by estimating the information dissemination fields for each of multiple SNS posting users, users can register to engage in SNS marketing as SNS posting users without restrictions on target products or services. This allows for the broad recruitment of collaborators for SNS marketing, and as a result, enables SNS marketing that targets a wider range of products and services.

[0062] Returning to Figure 3, the server 300 then performs an extraction process (S110) to extract users from among multiple SNS posting users who are suitable for disseminating information about the target product or / or service using user-generated content posted on SNS. This will be explained with reference to Figures 5 and 6.

[0063] Figure 5 is a first diagram illustrating the method for extracting users suitable for disseminating information about the target product through user-generated content posted on SNS in this embodiment. In this flow, the server 300 compares the target category for the target product with the information dissemination fields of each of the multiple SNS posting users, and executes a process to extract users from among the multiple SNS posting users whose information dissemination fields include the target category.

[0064] In the example shown in Figure 5, it is estimated that User A's information dissemination field is beauty, User B's is food and beverage, and User C's is entertainment. On the other hand, if the information obtained in the process S102 in Figure 3 is, for example, information about foundation, the target category may be stored as cosmetics. Then, the server 300 compares the cosmetics category for foundation with the information dissemination fields of each of the multiple SNS posting users, and extracts User A from among SNS posting users A, B, and C, whose information dissemination field is beauty and whose target category is cosmetics, as a user suitable for disseminating information about the target product.

[0065] Furthermore, by extracting users suitable for disseminating information about the target product in this way, it is possible to suppress situations where there is a mismatch between the product and service categories in which the influence of SNS dissemination can be effectively exerted, and it is also possible to extract users who can effectively exert influence through SNS from among the many SNS posting users registered in the information processing system 100. From the perspective of business users, a suitable degree of freedom can be maintained when selecting SNS users to cooperate in promoting the target product or service.

[0066] In this case, the server 300 may prioritize extracting SNS posting users who have a high number of recent reactions in a predetermined reaction classification from among multiple SNS posting users. Here, the above reaction classification is a classification of predetermined reactions from viewing users to posted data posted on SNS by SNS posting users. The above reactions are, for example, so-called engagements such as "likes" and "comments" on X (registered trademark). In other words, the server 300 classifies the posted data into a group of characteristics that includes a reaction classification related to the trend of engagement from viewing users, and estimates the field related to information freshness based on the reaction classification as the information dissemination field. Then, from among multiple SNS posting users, it prioritizes extracting SNS posting users who have a high number of recent reactions in the above reaction classification. This will be explained with reference to Figure 6.

[0067] Figure 6 shows an example in which, in addition to the user extraction method in Figure 5 above, a classification of responses related to the progression of engagement is also considered.

[0068] In the example shown in Figure 6, the response classification regarding the trend of engagement is divided into four groups: Group 1 to Group 4. For example, Group 1 represents the group with high engagement in the most recent period (e.g., within one day of posting), Group 2 represents the group with high engagement in the most recent period (e.g., within two or three days of posting), Group 3 represents the group with high engagement in the relatively recent period (e.g., within one week of posting), and Group 4 represents the group with high engagement in the past period (later than the most recent period). Furthermore, the level of engagement can be determined by the absolute number of responses from viewing users, or by relative comparisons among SNS posting users.

[0069] Then, the server 300 first extracts users A, B, and D from among the SNS posting users A, B, and D, whose information dissemination field is the beauty field and whose target category is the cosmetics category. Next, based on the above response classification, it extracts user D, who is classified into the first group of characteristics, as the first priority, and user A, who is classified into the fourth group of characteristics, as the second priority.

[0070] According to this, it is possible to prioritize the extraction of SNS users who post information that is fresh and receives responses from many viewers, thereby allowing the influence of SNS to be exerted more effectively.

[0071] Returning to Figure 3, the server 300 provides the business user with information about SNS posting users by transmitting the information about SNS posting users extracted by the processing in S110 to the business user's user terminal 400. The business user's user terminal 400 then obtains the information transmitted from the server 300 and acquires information about SNS posting users who will cooperate in promoting the target product using SNS (S111).

[0072] Consequently, while traditional SNS marketing, where businesses ask SNS users to cooperate in promoting their products or services, has faced challenges in selecting users suitable for promoting those products or services, this new approach makes it possible to effectively match users with the right SNS influence to promote those products or services.

[0073] Figure 7 is a second diagram illustrating the flow of operation of the information processing system 100 in this embodiment. Figure 7 illustrates the flow of operation between the server 300, the user terminal 400, and the SNS server 500 in the information processing system 100 in this embodiment, and the processes executed by the server 300, the user terminal 400, and the SNS server 500. The flow illustrated in Figure 7 illustrates the flow of operation between the server 300, the user terminals 400 of SNS posting users and business users, and the SNS server 500, and the processes executed by the server 300, the user terminals 400 of SNS posting users and business users, and the SNS server 500. Furthermore, in each process shown in Figure 7, processes that are substantially the same as those shown in Figure 3 are denoted by the same reference numerals, and their detailed explanation is omitted.

[0074] In the flow shown in Figure 7, after the processing in S103, information about the business user is input to the business user's user terminal 400 (S201). Here, the information about the business user includes the business user's company information and profile information. This information is then transmitted from the business user's user terminal 400 to the server 300.

[0075] Then, the server 300 acquires the information transmitted from the business user's user terminal 400 (S202). The server 300 then stores the attributes of the business user in the storage unit 302 (S203). At this time, the server 300 can, for example, compare the business user's company information and profile information with a database in which an attribute list is defined, to acquire attributes of the business user, such as the type of business the business user conducts, the business user's history (established, new, etc.), the region in which the business user conducts business, and the business user's corporate image (solid, free, logical, passionate, etc.), and store this in the storage unit 302. If, in the process of S201 above, the business user has entered information regarding the user's attributes, the server 300 may store the business user's attributes in the storage unit 302 based on that information.

[0076] Furthermore, in this flow, during processing S109, the server 300 classifies the posted data of each of the multiple SNS posting users into a group of characteristics that includes a user characteristic classification, to which a predetermined user characteristic category is assigned to the content contained in the posted data. Then, it estimates the impression field based on this user characteristic classification as the information dissemination field of the SNS posting user that has the group of characteristics that includes the user characteristic classification. This will be explained with reference to Figure 8.

[0077] Figure 8 is a second diagram illustrating the information dissemination field based on the characteristic group in this embodiment. In the example shown in Figure 8, the user characteristics classification of the posted data by SNS posting user A is such that 75% of the content is assigned the user characteristic category "cheerful" and 20% is assigned the user characteristic category "concise". Based on the characteristic group of SNS posting user A that includes this user characteristic classification, the server 300 estimates the impression field that can be assumed from "cheerful," which is the user characteristic category with the largest proportion, and "concise," which is the user characteristic category with the next largest proportion, as the information dissemination field of SNS posting user A, namely "a message that gives a passionate impression". The server 300 can classify the posted data, obtained using a well-known API for obtaining various data of X (registered trademark), into the above characteristic group by applying well-known natural language processing, image recognition processing, and well-known impression analysis processing.

[0078] On the other hand, the user characteristics classification of the posted data by SNS posting user B is as follows: 40% of the content is assigned the user characteristic category "calm," 40% is assigned the user characteristic category "complex," and 10% is assigned the user characteristic category "gloomy." Based on this user characteristic classification of SNS posting user B and the well-known impression analysis process, server 300 estimates "communication that gives a logical impression" as the information dissemination field of SNS posting user B.

[0079] According to this, it becomes possible to objectively determine the impression a social media user gives based on the posting data they have submitted to social media.

[0080] Returning to Figure 7, the server 300 then performs an extraction process (S110) to select users from among multiple SNS posting users who are suitable for disseminating information about the target product or / or service through user-generated content posted on SNS. This will be explained with reference to Figure 9.

[0081] Figure 9 is a second diagram illustrating the method for extracting users suitable for disseminating information about the target product through user-generated content posted on SNS in this embodiment. In this flow, the server 300 compares the attributes of the business user with the information dissemination fields of each of the multiple SNS posting users, and executes a process to extract users from among the multiple SNS posting users whose information dissemination fields include the attributes of the business user.

[0082] In the example shown in Figure 9, it is estimated that for each of the multiple SNS posting users, User A's information dissemination field is "passionate impression," User B's is "logical impression," and User C's is "negative impression." On the other hand, if the information obtained in the processing of S202 in Figure 7 above is, for example, information about the corporate culture or corporate image of a business user, the attributes of the business user may be stored as "free and active corporate culture." In this case, Server 300 compares "free and active corporate culture" regarding the corporate image with the above-mentioned information dissemination fields for each of the multiple SNS posting users, and extracts User A from among SNS posting users A, B, and C, whose information dissemination field is "passionate impression" and whose attributes as a business user are encompassed by "free and active corporate culture," as a user suitable for disseminating information about the target product. In this case, it is assumed that the information dissemination field of SNS posting user A regarding the product category includes the target category of the target product.

[0083] Furthermore, by extracting users suitable for disseminating information about the target products in this way, it becomes possible to suppress situations where there is a mismatch between the product and service categories in which the influence of SNS dissemination can be effectively exerted. In addition, SNS posting users that match the attributes of the business user can act as spokespeople for the business user, and widely and effectively disseminate information about the target products and services without damaging the business user's image.

[0084] Figure 10 is a third diagram illustrating the flow of operation of the information processing system 100 in this embodiment. Figure 10 illustrates the flow of operation between the server 300, the user terminal 400, and the SNS server 500 in the information processing system 100 in this embodiment, and the processes executed by the server 300, the user terminal 400, and the SNS server 500. The flow illustrated in Figure 10 illustrates the flow of operation between the server 300, the user terminals 400 of SNS posting users and business users, and the SNS server 500, and the processes executed by the server 300, the user terminals 400 of SNS posting users and business users, and the SNS server 500. Furthermore, in each process shown in Figure 10, processes that are substantially the same as those shown in Figure 3 are denoted by the same reference numerals, and their detailed explanation is omitted.

[0085] In the flow shown in Figure 10, after the processing in S103, information about the target user is input to the business user's user terminal 400 (S301). Here, the information about the target user refers to information such as the profile of the user that the business user expects to promote the target product through social media. This information is then transmitted from the business user's user terminal 400 to the server 300.

[0086] Then, the server 300 acquires the information transmitted from the business user's user terminal 400 (S302). The server 300 then stores the target users in the storage unit 302 (S303). At this time, the server 300 can acquire, for example, the age group, gender, nationality, and preferred genre of individuals that the business user expects to appeal to through SNS information dissemination regarding the target product, and store this information in the storage unit 302. The server 300 may also acquire information about target users based on a predetermined analysis of user-generated content posted by the business user to the SNS. In this case, the server 300 can acquire, for example, a user group that is expected to support the business user on the SNS, based on the number of followers of the business user's account on X (registered trademark) or the engagement with user-generated content by the business user, as target users.

[0087] Furthermore, in this flow, during processing S109, the server 300 classifies the posted data of each of the multiple SNS posting users into a characteristic group that includes a viewing user classification related to the viewing users who view it. Then, it estimates the influence field based on this viewing user classification as the information dissemination field of the SNS posting user that has the characteristic group that includes the viewing user classification. This will be explained with reference to Figure 11.

[0088] Figure 11 is a third diagram illustrating the information dissemination field based on the characteristic group in this embodiment. In the example shown in Figure 11, the user classification of the users viewing the posted data by SNS posting user A is 75% in their 20s and 20% in their teens. Based on this, the server 300 estimates "dissemination to young people" as the information dissemination field of SNS posting user A, including this user classification. The server 300 can classify the posted data obtained using a well-known API for obtaining various data of X (registered trademark) into the above characteristic group by applying well-known natural language processing and image recognition processing.

[0089] On the other hand, the user classification of the data posted by SNS user B is as follows: 40% are in their 40s, 30% are in their 50s, and 20% are in their 30s. Based on this classification of users, server 300 estimates "communication targeting middle-aged people" as the information dissemination field of SNS user B.

[0090] Returning to Figure 10, the server 300 then performs an extraction process (S110) to extract users from among multiple SNS posting users who are suitable for disseminating information about the target product or / or service using user-generated content posted on SNS. This will be explained with reference to Figure 12.

[0091] Figure 12 is a third diagram illustrating the method for extracting users suitable for disseminating information about the target product through user-generated content posted on SNS in this embodiment. In this flow, the server 300 compares the target user described above with the information dissemination fields of each of the multiple SNS posting users, and executes a process to extract users from among the multiple SNS posting users in whose information dissemination fields the target user is included.

[0092] In the example shown in Figure 12, it is estimated that the information dissemination fields of each of the multiple SNS posting users are as follows: User A is "dissemination to young people," User B is "dissemination to middle-aged people," and User C is "dissemination to young people." Then, if the target user stored in the processing of S303 in Figure 10 above is, for example, in their 20s, the server 300 extracts User A from among SNS posting users A, B, and C as a user suitable for disseminating information about the target product, because the target user's age group (in this case, their 20s) is included in the information dissemination field of "dissemination to young people." In this case, it is assumed that the target category of the target product is included in the information dissemination field of SNS posting user A regarding the product category.

[0093] Furthermore, the server 300 may obtain the number of times each SNS posting user's posts have been displayed to viewing users, and may prioritize extracting users from among the multiple SNS posting users who have had a higher number of displays to viewing users. This would allow for the extraction of users with higher engagement from among SNS posting users who have a significant influence on viewing users who have reached the target users, thereby making the influence of SNS more effective.

[0094] Furthermore, by extracting users suitable for disseminating information about the target product in this way, it becomes possible to effectively reach target users while suppressing situations where there is a mismatch between the product and service categories in which the influence of social media dissemination can be effectively exerted.

[0095] According to the information processing system 100 described above, it is possible to select SNS posting users who are suitable for disseminating information about the target product and / or target service using SNS.

[0096] <Second Embodiment> The second embodiment will be described with reference to Figure 13. In this embodiment, the server 300 calculates the overall contribution to information dissemination based on the individual contribution values ​​set for each of the multiple factors that contribute to information dissemination about the target product and / or target service by SNS posting users. The server 300 then prioritizes extracting posting users from among the multiple SNS posting users, with the highest overall contribution values ​​being selected.

[0097] Here, the above-mentioned multiple factors include content classification, to which a predetermined product category and / or service category is assigned to the content included in the posted data; user characteristic classification, to which a predetermined user characteristic category is assigned to the content included in the posted data; user classification, to which users view the posted data; and response classification, to which predetermined responses from such users progress. These classifications are as described in the first embodiment above as a group of characteristics of SNS posting users.

[0098] Figure 13 is a diagram illustrating the flow of operation of the information processing system 100 in this embodiment. Figure 13 explains the flow of operation between the server 300, the user terminal 400, and the SNS server 500 in the information processing system 100 in this embodiment, and the processes executed by the server 300, the user terminal 400, and the SNS server 500. The flow illustrated in Figure 13 explains the flow of operation between the server 300, the user terminals 400 of SNS posting users and business users, and the SNS server 500, and the processes executed by the server 300, the user terminals 400 of SNS posting users and business users, and the SNS server 500. Furthermore, in each process shown in Figure 13, processes that are substantially the same as those shown in Figure 3 are denoted by the same reference numerals, and their detailed explanation is omitted.

[0099] In the flow shown in Figure 13, after the processing in S103, factor information is input to the business user's user terminal 400 (S401). Here, the factor information is information for comparison with the above-mentioned multiple factors which can be defined as a group of characteristics of SNS posting users in the first embodiment, and includes information about the business user and information about the target user. The target category to which the target product belongs is stored in the storage unit 302 of the server 300 during the processing in S103. This information is then transmitted from the business user's user terminal 400 to the server 300.

[0100] Then, the server 300 acquires the information transmitted from the business user's user terminal 400 (S402). The server 300 then stores the attributes of the business user and the target user as factor information in the storage unit 302 (S403).

[0101] Furthermore, in the flow shown in Figure 13, after the processing in S108, in the processing in S409, the server 300 calculates the overall contribution to information dissemination for each of the multiple SNS posting users based on the individual contributions set for each of the multiple factors that contribute to the information dissemination by the SNS posting user about the target product and / or target service, and estimates the information dissemination field as a group of characteristics of the SNS posting user.

[0102] Here, the server 300 sets the individual contribution of the content classification described above. In this case, for each SNS posting user, the server 300 sets the individual contribution of the content classification as the proportion of posting data that matches the target product, etc., to all posting data by that SNS posting user. Note that category matching or mismatch can be determined, for example, by using the cosine similarity between the embedding vector of the posting data and the category.

[0103] Furthermore, the server 300 sets the individual contributions of the above-mentioned user characteristic classifications. In this case, for each SNS posting user, the server 300 applies well-known natural language processing and image recognition processing to the posting data by the SNS posting user to classify them into user characteristic categories, such as positive, negative, cheerful, gloomy, concise, complex, logical, emotional, passionate, and calm. The server then determines the degree of agreement between the user characteristic classification of the SNS posting user, which includes these user characteristic categories, and the attributes of the business user, such as the business user's history (established, new, etc.) and the business user's corporate image (solid, free, logical, passionate, etc.), and sets the individual contributions based on this. The degree of agreement can be determined, for example, based on the proportion of posting data that matches the attributes of the business user out of all posting data by the SNS posting user.

[0104] Furthermore, server 300 sets the individual contribution of the above-mentioned browsing user classification. In this case, server 300 classifies each SNS posting user by the age group, gender, nationality, and genre of interests to which the browsing users belong. Then, it determines the degree of agreement between this browsing user classification of SNS posting users and the attributes of the target user, such as the age group, gender, nationality, and genre of interests of the target user, and sets the individual contribution based on this. Note that the above degree of agreement can be determined, for example, based on the proportion of posting data that matches the attributes of the target user out of all posting data by SNS posting users.

[0105] Furthermore, the server 300 sets the individual contributions for the above-mentioned response classifications. In this case, for each SNS posting user, the server 300 classifies the trends in responses from viewing users into one of four groups, for example, recent, recent, and past (as described in the first embodiment above). Based on these groups, the server sets the individual contributions so that the contributions increase in the order of recent, recent, recent, and past groups. Alternatively, the server 300 may calculate the moving average of the trends in responses from viewing users and set the individual contributions regardless of these groups.

[0106] Then, based on the individual contributions described above, the server 300 calculates the overall contribution of SNS posting users to the dissemination of information about the target product and / or target service.

[0107] For example, server 300 can calculate the overall contribution based on the following formula: [Formula 1] SCOt = a × SCO1 + b × SCO2 + c × SCO3 + d × SCO4 SCOt: Overall contribution SCO1: Individual contribution of content classification SCO2: Individual contribution of user characteristic classification SCO3: Individual contribution of browsing user classification SCO4: Individual contribution of response classification a: Weight coefficient for content classification b: Weight coefficient for user characteristic classification c: Weight coefficient for browsing user classification d: Weight coefficient for response classification

[0108] In this case, the server 300 first scales each individual contribution SCO1-SCO4 in the above equation 1 to a value between 0 and 1. Furthermore, for each weight coefficient a-d in the above equation 1, the server sets the values ​​of these weight coefficients so that the coefficients increase in the order a, c, d, b, that is, so that the influence on the overall contribution increases in the order of individual contribution of content classification, individual contribution of browsing user classification, individual contribution of reaction classification, and individual contribution of user characteristic classification.

[0109] According to this, the overall contribution can be calculated by considering the priority of each individual contribution obtained through the Discloser's diligent examination of multiple factors that contribute to the dissemination of information about the target product and / or service by SNS posting users. Then, when extracting SNS posting users suitable for disseminating information about the target product based on this overall contribution, it becomes possible to extract users who can disseminate information more effectively.

[0110] Returning to Figure 13, the server 300 then performs an extraction process (S110) to extract users from among multiple SNS posting users who are suitable for disseminating information about the target product or / or service through user-generated content posted on SNS. At this time, the server 300 prioritizes extracting users with a higher overall contribution from among multiple SNS posting users. In this case, the server 300 may first extract SNS posting users whose overall contribution is above a predetermined threshold, and then prioritize extracting users with a higher overall contribution from among them.

[0111] By extracting users suitable for disseminating information about the target product through this flow, it is possible to extract users by considering multiple factors that contribute to such information dissemination and integrating these factors, thereby more effectively selecting SNS posting users suitable for information dissemination using SNS.

[0112] Furthermore, the information processing system 100 described above can also be used to select SNS posting users who are suitable for disseminating information about the target product and / or target service using SNS.

[0113] <Modification of the Second Embodiment> A modification of the second embodiment will now be described. In this modification, the server 300 further evaluates the predicted level of work performance of SNS posting users. Here, the above level of work performance refers to the level of work performance of SNS posting users in disseminating information about the target product and / or target service, which is predicted in advance based on predetermined information regarding the past information dissemination work of the SNS posting users. The server 300 then takes this level of work performance into account to extract SNS posting users who are suitable for disseminating information using SNS.

[0114] Furthermore, the level of performance may be evaluated based on factors such as the engagement figures from past information dissemination activities, whether or not promotional posts for the target products or services were made within predetermined timeframes in past information dissemination activities, and whether or not the promotional wording and posting methods conformed to predetermined standards.

[0115] The server 300 then calculates the overall contribution score by adding the above-mentioned degree of business performance to the multiple factors described in the second embodiment.

[0116] In this case, the server 300 may calculate the overall contribution by multiplying the overall contribution SCOt calculated by number 1 as described in the second embodiment by the work performance scaled to a value between 0 and 1. If the work performance that can be evaluated as described above is high, the value will be close to 1, and if the work performance is low, the value will be close to 0.

[0117] According to this, when extracting SNS posting users suitable for disseminating information about the target product by comprehensively considering multiple factors that contribute to information dissemination, users with a high predicted level of work performance will be prioritized. As a result, information dissemination via SNS will be more reliable.

[0118] <Third Embodiment> A third embodiment will now be described. In this embodiment, the control unit 303 of the server 300 further includes: an effect estimation unit that estimates the effect on disseminating information about the target product and / or target service based on a predetermined analysis of the posting data posted by posting users extracted by the extraction unit 3036; a calculation unit that calculates an incentive that may be given to the posting users extracted by the extraction unit 3036 based on the effect estimated by the effect estimation unit; and a notification unit that notifies the posting users extracted by the extraction unit 3036 of the incentive calculated by the calculation unit.

[0119] The processing performed by the effect estimation unit, calculation unit, and notification unit described above may be executed following the flow shown in Figure 3, as described in the first embodiment.

[0120] In this embodiment, for SNS posting users extracted by the process in S110 of Figure 3 above, the effect on disseminating information about the target product and / or target service is estimated based on a predetermined analysis of the posting data posted by the SNS posting users.

[0121] In this process, the server 300 in this flow estimates that the greater the degree of agreement calculated by comparing the information dissemination field and the target category for SNS posting users extracted by the process in S110 of Figure 3 above, the greater the effect of disseminating information about the target product or service via SNS.

[0122] In this process, the server 300 may, instead of following the above, estimate the effectiveness of disseminating information about the target product or service via social networking services based on, for example, so-called reach such as the number of impressions on X (registered trademark), or so-called engagement such as "likes" and "comments."

[0123] In this case, the server 300 can estimate that the more frequently the posted data is displayed to viewing users for the SNS posting users extracted by the process in S110 of Figure 3 above, the greater the effect on the above-mentioned information dissemination.

[0124] Furthermore, the server 300 can also estimate that the greater the engagement from users viewing the posted data of the SNS posting users extracted by the processing in S110 of Figure 3 above, the greater the effect of the information dissemination described above.

[0125] Alternatively, in such processing, the server 300 may estimate that the greater the degree of match calculated by comparing the viewing user classification and the target category for SNS posting users extracted by the processing in S110 of Figure 3 above, the greater the effect of disseminating information about the target product or service via SNS.

[0126] In this case, the server 300 classifies the SNS posting users extracted by the processing in S110 of Figure 3 above into a group of characteristics that includes a user classification of the users who view the posted data. The user classification of the users is, for example, the hobbies and preferences of the users who view the data. The server 300 can obtain the user classification of the users based on engagements such as "likes" and "comments" that users have made on X (registered trademark).

[0127] Furthermore, the server 300 can estimate that, for example, among the SNS posting users extracted by the processing in S110 of Figure 3 above, users whose browsing user classification, which includes the hobbies and preferences of the browsing user, includes the target category, or users whose browsing user classification, which includes the hobbies and preferences of the browsing user, is similar to the target category, will have a greater effect on disseminating information about the target product or service via SNS than users whose browsing user classification, which includes the hobbies and preferences of the browsing user, is not similar to the target category.

[0128] Next, the server 300 performs the process of calculating the incentives that can be given to the SNS posting users extracted by the process in S110 of Figure 3 above, and the process of notifying the SNS posting users of the calculated incentives. Here, the server 300 can calculate the incentives so that the greater the effect of the estimated SNS in disseminating information about the target product or target service, the larger the incentive.

[0129] As a result, the user terminal 400 of the SNS posting user extracted by the process in S110 of Figure 3 above obtains information about the incentive (for example, the amount of monetary reward for SNS marketing) calculated by the server 300. Then, the SNS posting user, who has in advance learned about the incentive they can receive if they post information about the target product or service via SNS, can consider whether or not to post the information based on this incentive information and apply to post the information via the user terminal 400.

[0130] The server 300 then receives application information sent from the user terminal 400 of the SNS posting user, accepts applications for information dissemination regarding the target product or service via SNS, and transmits that information to the user terminal 400 of the business user.

[0131] Then, the user terminal 400 of the business operator receives the above information transmitted from the server 300. Having thus grasped the application from the SNS posting user extracted by the process in S110 of Figure 3 above, the business operator can consider whether or not to accept the application and can perform the acceptance process for the application from the SNS posting user via the user terminal 400.

[0132] The server 300 then obtains the consent information transmitted from the user terminal 400 of the business user and transmits that information to the user terminal 400 of the SNS posting user, thereby enabling the user terminal 400 to obtain the consent information.

[0133] According to the processing flow described above, in SNS marketing where a business user asks SNS posting users to cooperate in promoting a target product or service, it is possible to broadly recruit SNS posting users who have influence on disseminating information about the target product or service through SNS, and then appropriately match them with the SNS posting users who have been matched as candidates for SNS marketing by the business user. By notifying the SNS posting users in advance of incentives for SNS marketing tailored to their individual needs, it is possible to appropriately incentivize the SNS posting users and obtain their cooperation in disseminating information about the target product or service.

[0134] <Other Modifications> The embodiments described above are merely examples, and this disclosure may be modified as appropriate without departing from its essence. For example, the processes and means described in this disclosure can be freely combined and implemented as long as no technical inconsistencies arise.

[0135] Furthermore, the processing described as being performed by one device may be divided and executed by multiple devices. For example, the first acquisition unit 3031, the second acquisition unit 3032, the third acquisition unit 3033, and the fourth acquisition unit 3034 may be formed in separate arithmetic processing units. In this case, these arithmetic processing units are preferably configured to cooperate. Also, the processing described as being performed by different devices may be executed by a single device. In a computer system, the hardware configuration (server configuration) by which each function is realized can be flexibly changed.

[0136] The present disclosure can also be realized by supplying a computer program implementing the functions described in the embodiments above to a computer, and having one or more processors in the computer read and execute the program. Such a computer program may be provided to the computer by a non-temporary computer-readable storage medium that can be connected to the computer's system bus, or it may be provided to the computer via a network. Non-temporary computer-readable storage mediums include, for example, any type of disk such as magnetic disks (floppy disks, hard disk drives (HDDs), etc.), optical disks (CD-ROMs, DVDs, Blu-ray discs, etc.), read-only memory (ROM), random access memory (RAM), EPROM, EEPROM, magnetic cards, flash memory, optical cards, and any type of medium suitable for storing electronic instructions.

[0137] 100... Information processing system 200... Network 300... Server 301... Communication unit 302... Storage unit 303... Control unit 400... User terminal

Claims

1. An information processing device that supports the dissemination of information about a target product and / or a target service, comprising: a first acquisition unit that acquires a target category which is the category to which the target product and / or a target service belongs; a second acquisition unit that acquires posted data posted to a social networking service by posting users who post user-generated content to the social networking service; an estimation unit that estimates the information dissemination field for each of the multiple posting users, by classifying the posted data posted by the posting user into predetermined characteristic groups, and estimating the information dissemination field based on the characteristic groups; and an extraction unit that extracts posting users suitable for disseminating information about the target product and / or a target service by user-generated content from among the multiple posting users, based on the degree of agreement calculated by comparing the target category for the target product and / or a target service with the information dissemination field for each of the multiple posting users.

2. The information processing apparatus according to claim 1, wherein the estimation unit classifies the posted data into a group of characteristics that includes a content classification to which a predetermined product category and / or service category is assigned to the content contained in the posted data, estimates the information field related to the product category and / or service category as the information dissemination field, and the extraction unit extracts from among a plurality of posting users the posting user whose information dissemination field includes the target category.

3. The information processing apparatus according to claim 1, further comprising a third acquisition unit for acquiring predetermined attributes of business users who conduct business related to the target product and / or target service, wherein the estimation unit classifies the posted data into a group of characteristics which includes a user characteristic classification to which a predetermined user characteristic category is assigned to the content contained in the posted data, estimates the impression field based on the user characteristic classification as the information dissemination field, and the extraction unit extracts from among a plurality of posting users the posting users whose information dissemination field includes the attributes of the business user.

4. The information processing apparatus according to claim 1, further comprising a fourth acquisition unit for acquiring information about target users, which are users that a business operator conducting business related to the target product and / or target service expects to promote the target product and / or target service through information dissemination, wherein the estimation unit classifies the posted data into characteristic groups that include a user classification relating to users who view the posted data, estimates the influence field based on the user classification as the information dissemination field, and extracts from a plurality of posting users the posting users whose information dissemination field includes the target users.

5. The information processing apparatus according to claim 4, wherein the fourth acquisition unit acquires information about the target user based on a predetermined analysis of user-generated content posted by the business user to the social networking service.

6. The information processing apparatus according to claim 4, wherein the second acquisition unit acquires the number of times the posted data has been displayed to the viewing user, and the extraction unit extracts from among a plurality of posting users that have been displayed to the viewing user more frequently.

7. The information processing apparatus according to claim 1, wherein the estimation unit classifies the posted data into a group of characteristics that includes a response classification relating to the progression of predetermined responses from users viewing the posted data, estimates a field related to the freshness of information based on the response classification as the information dissemination field, and the extraction unit extracts from among a plurality of posting users, prioritizing posting users who have the most recent responses in the response classification.

8. The information processing apparatus according to claim 1, further comprising a calculation unit that calculates an overall contribution to information dissemination based on individual contribution levels set for each of a plurality of factors that contribute to the dissemination of information about the target product and / or target service by the posting user, wherein the plurality of factors include a content classification in which a predetermined product category and / or service category is assigned to the content included in the posting data, a user characteristic classification in which a predetermined user characteristic category is assigned to the content included in the posting data, a viewing user classification relating to viewing users who view the posting data, and a response classification relating to the progression of predetermined responses from the viewing users, and the extraction unit preferentially extracts posting users from a plurality of posting users in order of increasing overall contribution level.

9. The information processing apparatus according to claim 1, further comprising an evaluation unit that evaluates the degree of performance of the posting user in disseminating information about the target product and / or target service, which is predicted in advance based on predetermined information relating to the posting user's past information dissemination work, wherein the extraction unit extracts the posting user taking into account the degree of performance.

10. The information processing apparatus according to claim 8, further comprising an evaluation unit that evaluates the degree of performance of the posting user's information dissemination activities regarding the target product and / or target service, which is predicted in advance based on predetermined information relating to the posting user's past information dissemination activities, wherein the calculation unit calculates the overall contribution by adding the degree of performance to the plurality of factors.

11. The information processing apparatus according to claim 1, further comprising a provisioning unit that provides information about the posting user extracted by the extraction unit to a business user that conducts business related to the target product and / or target service.

12. The information processing apparatus according to claim 1, further comprising: an effect estimation unit that estimates the effect of disseminating information about the target product and / or target service based on a predetermined analysis of the posted data posted by the extracted posting users; an incentive calculation unit that calculates an incentive that may be given to the extracted posting users based on the estimated effect, wherein the incentive is calculated such that the larger the estimated effect, the larger the incentive; and a notification unit that notifies the extracted posting users of the calculated incentive.

13. An information processing method for supporting the dissemination of information about a target product and / or a target service, wherein a computer performs the following steps: a first acquisition step of acquiring a target category which is the category to which the target product and / or a target service belongs; a second acquisition step of acquiring posted data posted to a social networking service by posting users who post user-generated content to the social networking service; an estimation step of estimating the information dissemination field for each of the multiple posting users, wherein for each of the multiple posting users, the posted data posted by the posting user is classified into predetermined characteristic groups, and the information dissemination field is estimated based on the characteristic groups; and an extraction step of extracting posting users from among the multiple posting users who are suitable for disseminating information about the target product and / or a target service using user-generated content, based on the degree of agreement calculated by comparing the target category for the target product and / or a target service with the information dissemination field for each of the multiple posting users.

14. An information processing program that supports the dissemination of information about a target product and / or target service, wherein the program causes a computer to perform: a first acquisition step of acquiring a target category which is the category to which the target product and / or target service belongs; a second acquisition step of acquiring posted data posted to a social networking service by posting users who post user-generated content to the social networking service; an estimation step of estimating the information dissemination field for each of the multiple posting users, wherein for each of the multiple posting users, the posted data posted by the posting user is classified into predetermined characteristic groups, and the information dissemination field is estimated based on the characteristic groups; and an extraction step of extracting posting users from among the multiple posting users who are suitable for disseminating information about the target product and / or target service using user-generated content, based on the degree of agreement calculated by comparing the target category for the target product and / or target service with the information dissemination field for each of the multiple posting users.

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