Analysis apparatus, analysis program, and analysis method

Abstract

To analyze the scale of information diffusion.SOLUTION: An analysis apparatus includes: a post information acquisition unit which acquires post information including information on normal posts including a predetermined keyword and diffusing posts that diffuses the normal posts, and stores the information as a post group in association with the diffusing posts related to the normal posts, the post group including a small-scale diffusing post group in which the number of diffusing posts is smaller than a predetermined quantity, and a large-scale diffusing post group in which the number of diffusing posts is larger than the predetermined quantity; and an analysis unit which analyzes diffusing trends of the normal posts on the basis of information on the number of diffusing posts of the normal posts included in each of the small-scale diffusing post group and the large-scale diffusing post group.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an analysis apparatus, an analysis program, and an analysis method.

Background Art

[0002] The phenomenon of information diffusion on the Internet is widely recognized as something that improves the recognition of products and commercial materials, and is also applied to practical effect measurement in digital marketing such as advertising distribution.

[0003] However, the application of the information diffusion phenomenon is only recognized as a result, and it is difficult to predict the phenomenon in advance. As long as there is such unpredictability, there is mainly a large cost inefficiency in the field of advertising production, which has hindered the development of the industry.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] For example, Patent Document 1 discloses a technique for predicting the influence of each poster and the number of future posts on a specific topic on a website such as an SNS.

[0006] However, the invention described in Patent Document 1 predicts the scale of primary information diffusion on the SNS for a specific topic, and it is difficult to predict the number of re-diffusions such as diffusing by quoting a certain post.

[0007] The present invention has been made in view of such a background, and an object thereof is to analyze the scale of information diffusion.

Means for Solving the Problems

[0008] To solve the above problems, an analyzer according to the present disclosure acquires post information including information on normal posts including a predetermined keyword and diffusion posts that diffuse each of the normal posts, and stores the diffusion posts related to the normal posts as a post group in a post information acquisition unit. The post group includes a small-scale diffusion post group in which the number of diffusion posts is less than a predetermined number and a large-scale diffusion post group in which the number of diffusion posts is more than the predetermined number. An analysis unit analyzes the diffusion tendency of the normal posts based on information on the number of diffusion posts of the normal posts included in each of the small-scale diffusion post group and the large-scale diffusion post group.

[0009] Regarding other problems disclosed in the present application and solutions therefor, they will be clarified by the embodiments of the invention and the drawings.

Effect of the Invention

[0010] According to the present invention, the scale of information diffusion can be analyzed.

Brief Description of the Drawings

[0011]

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Mode for Carrying Out the Invention

[0012] <Summary of the Invention> [Item 1] A posting information acquisition unit that acquires posting information including information on normal postings including a predetermined keyword and diffusion postings that diffuse each of the normal postings, and stores the diffusion postings related to the normal postings as a posting group in association with each other; The posting group includes a small-scale diffusion posting group in which the number of the diffusion postings is less than a predetermined number and a large-scale diffusion posting group in which the number of the diffusion postings is more than the predetermined number, and based on the information on the number of the diffusion postings of the normal postings included in each of the small-scale diffusion posting group and the large-scale diffusion posting group, an analysis unit that analyzes the diffusion tendency of the normal postings; An analysis device comprising: [Item 2] The analysis unit extracts so that the number of the diffusion postings in each of the small-scale diffusion posting group and the large-scale diffusion posting group becomes the same scale, and analyzes the diffusion tendency by comparing the diffusion postings that increase or decrease when the small-scale diffusion posting group and the large-scale diffusion posting group are increased or decreased by the same multiple. The analysis device according to Item 1. [Item 3] The posting information acquisition unit acquires information on the accounts that made the normal postings and the diffusion postings, An evaluation unit that evaluates the contribution degree of information diffusion of a specific one of the accounts in the posting group including the account that made the normal posting and the account that made the diffusion posting related to the normal posting; The analysis device according to Item 1 or 2, comprising: [Item 4] A similarity determination unit that determines the similarity between a first keyword included in the normal post for which the diffusion tendency has been analyzed based on the information on the number of the diffusion posts, and a second keyword for which the diffusion tendency is to be analyzed; comprising; The analysis unit predicts the diffusion tendency of the second keyword based on the similarity. The analysis apparatus according to item 1 or 2. [Item 5] A prediction unit that predicts the number of diffusions when the normal post is posted by the user of the account from the information on the diffusion tendency and the contribution degree information; The analysis apparatus according to item 3, comprising. [Item 6] On a computer, A post information acquisition step of acquiring post information including information on a normal post including a predetermined keyword and a diffusion post that diffuses each of the normal posts, and associating and storing the diffusion posts related to the normal posts as a post group; The post group includes a small-scale diffusion post group in which the number of the diffusion posts is less than a predetermined number and a large-scale diffusion post group in which the number of the diffusion posts is more than the predetermined number, and based on the information on the number of the diffusion posts of the normal posts included in each of the small-scale diffusion post group and the large-scale diffusion post group, an analysis step of analyzing the diffusion tendency of the normal posts; An analysis program for causing the above to be executed. [Item 7] When a computer A post information acquisition step of acquiring post information including information on a normal post including a predetermined keyword and a diffusion post that diffuses each of the normal posts, and associating and storing the diffusion posts related to the normal posts as a post group; The post group includes a small-scale diffusion post group in which the number of the diffusion posts is less than a predetermined number and a large-scale diffusion post group in which the number of the diffusion posts is more than the predetermined number, and based on the information on the number of the diffusion posts of the normal posts included in each of the small-scale diffusion post group and the large-scale diffusion post group, an analysis step of analyzing the diffusion tendency of the normal posts; An analysis method for executing the above.

[0013] FIG. 1 is a diagram showing an example of the overall configuration of an evaluation system according to an embodiment of the present invention. The evaluation system of the present embodiment includes a server device 1. The server device 1 is communicably connected to a user terminal 3 via a communication network 2. The communication network 2 is, for example, the Internet, and is constructed by a public telephone network, a mobile phone network, a wireless communication path, Ethernet (registered trademark), or the like.

[0014] ==Server device 1== The server device 1 may be a general-purpose computer such as a workstation or a personal computer, or may be logically realized by cloud computing. In the present embodiment, one unit is illustrated for convenience of explanation, but the present invention is not limited thereto, and a plurality of units may be used.

[0015] ==User terminal 3== The user terminal 3 is a computer used by a user who performs analysis. For example, a smartphone, a tablet computer, a personal computer, a wearable computer, or the like. The user can access the server device 1 by, for example, an application executed on the user terminal 3 or a web browser.

[0016] FIG. 2 is a diagram showing an example of the hardware configuration of the server device 1. Note that the illustrated configuration is an example, and it may have other configurations. The server device 1 includes a processor 101, a memory 102, a storage device 103, a communication interface 104, an input device 105, and an output device 106. The storage device 103 stores various data and programs, such as a hard disk drive, a solid state drive, or a flash memory. The communication interface 104 is an interface for connecting to the communication network 2, such as an adapter for connecting to Ethernet (registered trademark), a modem for connecting to a public telephone network, a wireless communication device for performing wireless communication, a USB (Universal Serial Bus) connector or an RS232C connector for serial communication, etc. The input device 105 inputs data, such as a keyboard, a mouse, a touch panel, a button, a microphone, etc. The output device 106 outputs data, such as a display, a printer, a speaker, etc. Note that each functional unit of the server device 1 described later is realized by the processor 101 reading out a program stored in the storage device 103 into the memory 102 and executing it, and each storage unit of the server device 1 is realized as a part of the storage area provided by the memory 102 and the storage device 103.

[0017] FIG. 3 shows the functional configuration of the server device 1. As shown in FIG. 3, the server device 1 includes each storage unit such as a contribution information storage unit 131 and a similarity information storage unit 132, and each processing unit such as a contribution information acquisition unit 111, an analysis unit 112, a contribution degree evaluation unit 113, a similarity determination unit 114, a prediction unit 115, and a presentation unit 116.

[0018] Descriptions of each storage unit of the contribution information storage unit 131, the similarity information storage unit 132, and the performance information storage unit 133 are described.

[0019] The contribution information storage unit 131 stores information related to contributions on a website on the Internet, an example of which is shown in FIG. 4. The contribution information includes, as an example, contributions, re - contributions of contributions, cited contributions (diffusion contributions) that cite contributions, the accounts that made the contributions and the diffusion contributions, information on the followers and follow - ers of the accounts, the types or numbers of evaluations of the contributions, etc., but is not limited thereto. Further, the contribution information includes large - scale diffusion contribution groups and small - scale diffusion contribution groups.

[0020] The similarity information storage unit 132 stores similarity information between keywords or accounts determined by the similarity determination unit 114, an example of which is shown in FIG. 5. The similarity information storage unit 132 may store information on the degree of similarity in addition to the similarity information between keywords and between accounts.

[0021] The performance information storage unit 133 stores similarity information between keywords or accounts determined by the similarity determination unit 114, an example of which is shown in FIG. 6.

[0022] The processing of the contribution information acquisition unit 111, the analysis unit 112, the contribution degree evaluation unit 113, the similarity determination unit 114, the prediction unit 115, and the presentation unit 116 is described below.

[0023] As an example, the contribution information acquisition unit 111 acquires contribution information from a website on the Internet via the communication network 2 and stores it in the contribution information storage unit 131.

[0024] As an example, the website from which the contribution information acquisition unit 111 acquires contribution information may be a website where a user can post information from an account they manage (hereinafter referred to as normal posting), and it is sufficient if the account managed by another user has functions such as being able to repost normal postings, quote and post (collectively referred to as diffusion posting for reposting and quote posting), etc. It may be social media including influencer platforms such as blogs, X (formerly Twitter (registered trademark)), Instagram (registered trademark), TikTok (registered trademark), YouTube (registered trademark), 17Live (registered trademark), etc., but is not limited thereto. Note that the diffusion posting may include cases where the posting is displayed on the wall of the other user when the account managed by the other user expresses an evaluation of the normal posting.

[0025] As an example, the contribution information acquisition unit 111 acquires contribution information including a predetermined keyword. The keyword may include product names, services, talents (including but not limited to entertainers, youtubers, v-tubers, streamers, etc.), the official name, abbreviation, affectionate name, etc. of a character name, generalized product names (e.g., web services, snacks, beverages, foods, cosmetics, real estate, etc.), activity, event, industry names (sports, fashion, dining out, etc.), etc., but is not limited thereto.

[0026] Note that the contribution information acquired by the contribution information acquisition unit 111 includes UGC (user generated content). UGC is a term indicating a reference to a product, etc. by a certain consumer. Starting from the UGC sent from a certain account, there are known cases where the awareness of the product, etc. is improved when followers, etc. perform diffusion posting.

[0027] The contribution information acquisition unit 111 may acquire the above-mentioned contribution information by specifying the language in which the posting was made and the period for extraction.

[0028] The contribution information acquisition unit 111 may acquire the accounts that made normal postings and diffusion postings, and the follow and follower information of those accounts.

[0029] The contribution information acquisition unit 111 extracts a normal contribution and a diffusion contribution made based on the normal contribution, and generates a contribution group.

[0030] The contribution information acquisition unit 111 classifies a contribution group with the number of diffusion contributions less than a predetermined number as a small-scale diffusion contribution group, and a contribution group with the number of diffusion contributions greater than a predetermined number as a large-scale diffusion contribution group, and stores them in the contribution information storage unit 131.

[0031] Based on the follow-follower relationship between the account that made the normal contribution and the account that diffused the normal contribution, the contribution information acquisition unit 111 extracts the network (contribution group) between the accounts regarding the diffusion of the normal contribution. In this case, the contribution information acquisition unit 111 may extract the contribution group using a known method such as the Leiden method, for example.

[0032] The contribution information acquisition unit 111 classifies the contribution group according to the size of the contribution group, that is, the number of accounts included in the contribution group. The contribution group size is classified, for example, into categories such as the number of accounts included in the contribution group being 1 (micro), 2 to 10 (small), 11 to 99 (medium), 100 or more (large). The contribution information acquisition unit 111 may further classify a contribution group with the number of accounts included, for example, 1, 2 to 10 as a small-scale diffusion contribution group, and a contribution group with 11 to 99, 100 or more as a large-scale diffusion contribution group, but is not limited to these thresholds. Assuming that one account has made a diffusion contribution once, the number of accounts mentioned here can be regarded as the number of contributions, and the number of accounts that have made diffusion contributions can be regarded as the number of diffusion contributions.

[0033] As an example, the analysis unit 112 analyzes the diffusion tendency of the normal contribution based on the information on the total number of contributions including the diffusion contributions of the normal contributions included in each of the small-scale diffusion contribution group and the large-scale diffusion contribution group.

[0034] The analysis unit 112, as an example, calculates the average value or median value of the diffusion posts included in each of the small-scale diffusion post group and the large-scale diffusion post group for a normal post containing a certain keyword (referred to as keyword A), based on the information on the number of diffusion posts included in each, and stores it in the performance information storage unit 133.

[0035] For example, as shown in FIG. 7, the analysis unit 112 extracts the small-scale diffusion post group and the large-scale diffusion post group that include normal posts containing keyword A so that the total number of posts in each is of the same scale. The analysis unit 112 samples and extracts a part of the normal posts in each of these small-scale diffusion post group and large-scale diffusion post group. When the normal posts sampled and extracted from the small-scale diffusion post group and the large-scale diffusion post group are increased or decreased by the same multiple, the analysis unit 112 reads out the value of the average value or median value of the diffusion posts of the normal post containing the above-mentioned keyword A from the performance information storage unit 133, multiplies it by the number of normal posts, and compares the number of the total posts to be increased or decreased, thereby analyzing the diffusion tendency of the normal post containing keyword A.

[0036] For example, for a keyword (referred to as keyword B) for which the analysis unit 112 has not obtained data on the average value or median value of the diffusion posts, the analysis unit 112 extracts the small-scale diffusion post group and the large-scale diffusion post group so that the total number of posts in each is of the same scale. The analysis unit 112 samples and extracts a part of the normal posts in each of these small-scale diffusion post group and large-scale diffusion post group. When the normal posts sampled and extracted from the small-scale diffusion post group and the large-scale diffusion post group are increased or decreased by the same multiple, the analysis unit 112 reads out the value of the average value or median value of the diffusion posts of the normal post containing keyword A (assuming that the similarity determination unit 114 described later determines that keyword B is similar to keyword A) that the similarity determination unit 114 determines to be similar to keyword B from the performance information storage unit 133, multiplies it by the number of normal posts, and compares the number of the total posts to be increased or decreased, thereby analyzing the diffusion tendency of the normal post containing keyword B.

[0037] A specific example of the analysis by the analysis unit 112 will be described. The analysis unit 112 extracts a small-scale diffusion post group (total number of posts: 1000, number of post groups: 100, average number of diffusion posts per normal post: 9) and a large-scale diffusion post group (total number of posts: 900, number of post groups: 20, average number of diffusion posts per normal post: 44) regarding normal posts including keyword A (the total number of posts in both the small-scale diffusion post group and the large-scale diffusion post group is 1900). The analysis unit 112, for example, extracts 25 for the small-scale diffusion post group (total number of posts: 250) and 5 for the large-scale diffusion post group (total number of posts: 225) so that the number of posts in each of the small-scale diffusion post group and the large-scale diffusion post group is about the same. The analysis unit 112 randomly extracts 20% (5 normal posts in the small-scale diffusion post group and 1 normal post in the large-scale diffusion post group) from each of the normal posts in the extracted small-scale diffusion post group and large-scale diffusion post group. The analysis unit 112, for example, doubles the extracted normal posts (resulting in 30 normal posts in the small-scale diffusion post group and 6 normal posts in the large-scale diffusion post group), and also increases the number of diffusion posts associated with the doubled normal posts at the same ratio. In this case, for the 5 increased normal posts in the small-scale diffusion post group, the number of diffusion posts increases by 45, and the total number of posts increases by 50. Also, the total number of posts for 30 normal posts becomes 300. Also, for the 1 increased normal post in the large-scale diffusion post group, the number of diffusion posts increases by 44, and the total number of posts increases by 45. Also, the total number of posts for 6 normal posts becomes 270. Therefore, as the diffusion tendency of normal posts including keyword A, increasing the normal posts in the small-scale diffusion post group makes it easier to increase the total number of posts. By performing the above-described processing, the analysis unit 112 analyzes which of the small-scale diffusion post group or the large-scale diffusion post group formed by the diffusion posts based on normal posts including a specific keyword results in an increase in the total number of posts.

[0038] In the above-described example, a case of a normal post including keyword A whose average number of diffused posts is stored in the performance information storage unit 133 was introduced. However, regarding keyword B for which the average number of diffused posts is unknown, the analysis unit 112 may calculate the increase in the number of diffused posts based on the normal post when the normal post is increased, using the information on the average number of diffused posts of keyword A determined by the similarity determination unit 114 to be similar to keyword B.

[0039] FIG. 8 is a diagram showing an example of the diffusion tendency analyzed by the analysis unit 112. The merchandise in FIG. 8 includes specific product names or general merchandise names, and it is possible to analyze whether the total number of posts increases more in the small-scale diffused post group or the large-scale diffused post group for normal posts including these as keywords. For example, it shows that for snacks_1 and snacks_2, diffusion by the small-scale post group results in an increase in the total number of posts, while for snacks_3 and snacks_4, diffusion by the large-scale post group results in an increase in the total number of posts.

[0040] As an example, the analysis unit 112 may predict the diffusion tendency of a normal post including a certain keyword based on the information on the similarity of the keywords included in the normal posts determined by the similarity determination unit 114 described later. The analysis unit 112 may analyze that keywords with high similarity have the same diffusion tendency.

[0041] As an example, the analysis unit 112 may predict the diffusion tendency of a normal post by a certain account based on the information on the similarity of the accounts determined by the similarity determination unit 114 described later. The analysis unit 112 may analyze that normal posts by accounts with high similarity have the same diffusion tendency.

[0042] As an example, the contribution degree evaluation unit 113 evaluates the contribution degree of information diffusion of a specific account in a post group including the account that made the normal post and the account that made the diffused post related to the normal post.

[0043] The contribution evaluation unit 113 analyzes the posting group and estimates an information diffusion network representing how each normal posting is diffusely posted, as shown in FIG. 9, for example. Specifically, the contribution evaluation unit 113 extracts all the accounts that diffusely posted a certain normal posting and sets them as the node set V of the network. Further, when the contribution evaluation unit 113 sets the node set of the accounts that made normal postings or diffusely posted as Vfrom and the node set of the accounts that received normal postings or diffusely posted as Vto, it creates an edge between Vfrom and Vto and estimates the information diffusion network. Further, the contribution evaluation unit 113 analyzes how the number of diffusely posted posts changes as a result of excluding the account (evaluation account) for which the contribution to diffusion is to be evaluated from the posting group. Specifically, the contribution evaluation unit 113 randomly extracts an evaluation account from the posting group and excludes the node corresponding to the evaluation account from the diffusion path network. Next, the contribution evaluation unit 113 also excludes the nodes of the accounts to which information has diffused from the evaluation account and repeats this. Finally, the contribution evaluation unit 113 sets the number of excluded nodes as the contribution of the evaluation account to information diffusion.

[0044] As an example, the similarity determination unit 114 determines whether or not a first keyword included in a normal posting analyzed for diffusion tendency is similar to a second keyword for which the diffusion tendency is to be analyzed.

[0045] The similarity determination unit 114 determines the similarity between keywords based on, for example, the category of products, services, etc. in which the keywords are included, and information on the characteristics of the products, services, etc. indicated by the keywords. For example, when the first keyword is the product name of cosmetics and the second keyword is also the product name of cosmetics, the similarity determination unit 114 determines that the first keyword and the second keyword are similar. Also, for example, when the first keyword is the product name of confectionery, the second keyword is also the product name of confectionery, and they have common characteristics such as each product having a sweet feature, containing chocolate, or having the same selling price, the similarity determination unit 114 may determine that the first keyword and the second keyword are similar.

[0046] The similarity determination unit 114 may, for example, determine the similarity of accounts.

[0047] For example, the similarity determination unit 114 may analyze a graph structure representing the relationship between accounts, and determine that accounts with similar graph structures are similar. The graph structure may be a graph structure representing the relationship between accounts in social media (including, for example, relationships such as following, follower relationships, relationships between accounts with evaluations (likes, etc.) or diffusion to posts of a certain account, relationships associated with replies, etc.), represented by nodes (accounts) and edges (relationships connecting accounts). In addition, when determining the similarity of graph structures, factors such as the overlap of edges in important node / edge relationships, the similarity of central node (account) attributes, and the centrality values of nodes calculated by typical algorithms such as PageRank may be considered, but are not limited to these.

[0048] The similarity determination unit 114 may obtain information on the entities (which may include individuals, companies, etc.) operating the accounts, and determine the similarity between accounts based on information such as business content, industry, business type, products handled, target industries, business scale, etc. In addition, the similarity determination unit 114 may compare the designs of sites (keywords / phrases used, texture, color, font type, size, etc.) based on information such as service sites or homepages operated by the entity, and determine the similarity between accounts based on the similarity of the designs.

[0049] The prediction unit 115, for example, predicts a method for increasing diffusion posts when a normal post is made from a certain account based on information on diffusion tendency and contribution degree, or based on information on diffusion tendency, contribution degree, and further similarity degree.

[0050] The prediction unit 115 predicts, for example, a method for increasing the number of diffusion posts when a normal post is made from a certain account based on information on the diffusion tendency and information on the contribution degree. The prediction unit 115 obtains, from the analysis result of the analysis unit 112, which of the small-scale diffusion post group and the large-scale diffusion post group is more likely to obtain the scale of diffusion posts for a normal post including a certain keyword. Further, the prediction unit 115 may select a post group including accounts with a high contribution degree for the normal post.

[0051] The prediction unit 115 predicts, for example, a method for increasing the number of diffusion posts when a normal post is made from a certain account based on information on the diffusion tendency, information on the contribution degree, and information on the similarity degree. The prediction unit 115 obtains information on a keyword for which it wants to predict a method for increasing the number of diffusion posts by receiving a user input operation. The prediction unit 115 obtains information on keywords with a high similarity degree to the keyword determined by the similarity determination unit 114. The prediction unit 115 obtains, from the analysis result of the analysis unit 112, which of the small-scale diffusion post group and the large-scale diffusion post group is more likely to obtain the scale of diffusion posts for a normal post including the keyword with a high similarity degree. Further, the prediction unit 115 may select a post group including accounts with a high contribution degree for the normal post and the diffusion post.

[0052] The presentation unit 116 presents to the user terminal 3 the method for increasing the number of diffusion posts when a normal post is made from a certain account predicted by the prediction unit 115.

[0053] FIG. 10 is a diagram for explaining an example of the processing of the evaluation device of the present embodiment.

[0054] The server device 1 acquires post information (1001). The server device 1 analyzes the post information (1002). The server device 1 evaluates the contribution degree of the account to the diffusion post (1003). The server device 1 determines the similarity degree of the keyword or the account (1004). The server device 1 predicts a method for obtaining more diffusion posts from the normal post (1005). The server device 1 presents the predicted method to the user terminal 3 (1006).

[0055] As described above, the preferred embodiments of the present disclosure 1 have been described in detail with reference to the accompanying drawings. However, the technical scope of the present disclosure is not limited to such examples. It is obvious that those having ordinary knowledge in the technical field of the present disclosure can conceive of various modification examples or correction examples within the scope of the technical idea described in the claims, and these are naturally understood to belong to the technical scope of the present disclosure.

[0056] The apparatus described in this specification may be realized as a single apparatus, or may be realized by a plurality of apparatuses (for example, a cloud server) partially or entirely connected by a communication network. For example, the CPU and the storage device of the server device 1 may be realized by different servers connected to each other by a communication network.

[0057] A series of processes by the apparatus described in this specification may be realized using any of software, hardware, and a combination of software and hardware. It is possible to create a computer program for realizing each function of the server device 1 according to this embodiment and install it on a PC or the like. In addition, a computer-readable recording medium storing such a computer program can also be provided. The recording medium is, for example, a magnetic disk, an optical disk, a magneto-optical disk, a flash memory, or the like. Further, the above computer program may be distributed via a communication network, for example, without using a recording medium.

[0058] Also, the processes described in this specification do not necessarily have to be executed in the described order. Some process steps may be executed in parallel. In addition, additional process steps may be adopted, and some process steps may be omitted.

[0059] Also, the effects described in this specification are illustrative or exemplary only and not limiting. That is, the technology according to the present disclosure may exhibit other effects that are apparent to those skilled in the art from the description of this specification, together with or in place of the above effects.

Explanation of Reference Numerals

[0060] 1 Server device 2 Communication network 3 User terminal 101 CPU 102 Memory 103 Storage device 104 Communication interface 105 Input device 106 Output device 111 Post information acquisition unit 112 Analysis unit 113 Contribution degree evaluation unit 114 Similarity determination unit 115 Prediction unit 116 Presentation unit 131 Post information storage unit 132 Similarity information storage unit 133 Performance information storage unit

Claims

1. A posting information acquisition unit that acquires posting information including information on normal posts containing predetermined keywords and diffusion posts that diffuse each of the normal posts, and stores the diffusion posts related to the normal posts as a posting group by associating them; The posting group includes a small-scale diffusion posting group in which the number of the diffusion posts is less than a predetermined number and a large-scale diffusion posting group in which the number of the diffusion posts is more than the predetermined number, and based on information on the number of the diffusion posts of the normal posts included in each of the small-scale diffusion posting group and the large-scale diffusion posting group, an analysis unit that analyzes the diffusion tendency of the normal posts; An analysis device comprising the above.

2. The analysis unit extracts so that the number of the diffusion posts in each of the small-scale diffusion posting group and the large-scale diffusion posting group becomes the same scale, and analyzes the diffusion tendency by comparing the diffusion posts that increase or decrease when the small-scale diffusion posting group and the large-scale diffusion posting group are increased or decreased equally. The analysis device according to Claim 1.

3. The posting information acquisition unit acquires information on the accounts that made the normal posts and the diffusion posts, An evaluation unit that evaluates the contribution degree of information diffusion of a specific one of the accounts in the posting group including the account that made the normal post and the account that made the diffusion post related to the normal post; The analysis device according to Claim 1 or 2, comprising the above.

4. A similarity determination unit that determines the similarity between a first keyword included in the normal post for which the diffusion tendency has been analyzed based on the information on the number of the diffusion posts and a second keyword for which the diffusion tendency is to be analyzed; Comprising the above, The analysis unit predicts the diffusion tendency of the second keyword based on the similarity. The analysis device according to Claim 1 or 2.

5. A prediction unit that predicts the number of diffusions when the normal post is posted by the user of the account based on the information on the diffusion tendency and the information on the contribution degree; The analysis device according to Claim 3, comprising the above.

6. In a computer, A posting information acquisition step of acquiring posting information including information on normal posts containing predetermined keywords and diffusion posts that diffuse each of the normal posts, and storing the diffusion posts related to the normal posts as a posting group by associating them; The submission group includes a small-scale diffusion submission group in which the number of the diffusion submissions is less than a predetermined number, and a large-scale diffusion submission group in which the number of the diffusion submissions is more than the predetermined number, and an analysis step of analyzing the diffusion tendency of the normal submission based on the information on the number of the diffusion submissions of the normal submission included in each of the small-scale diffusion submission group and the large-scale diffusion submission group, An analysis program for causing the above to be executed.

7. A computer acquires submission information including information on a normal submission including a predetermined keyword and a diffusion submission that diffuses each of the normal submissions, and stores the diffusion submissions related to the normal submission in association with each other as a submission group in a submission information acquisition step; The submission group includes a small-scale diffusion submission group in which the number of the diffusion submissions is less than a predetermined number, and a large-scale diffusion submission group in which the number of the diffusion submissions is more than the predetermined number, and an analysis step of analyzing the diffusion tendency of the normal submission based on the information on the number of the diffusion submissions of the normal submission included in each of the small-scale diffusion submission group and the large-scale diffusion submission group, An analysis method for executing the above.

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