KR20090001430A - Method and system for analyzing blog - Google Patents

Abstract

A blog analyzing method and system are provided to calculate user contents power based on contents power of each posting, thereby selecting a power user who is a center to establish various policies for the activation of a blog service. A blog analyzing method and system produces the posting contents power of postings on the basis of the action of blog users for at least one posting(S440). The blog analyzing method and system produces user contents power on the basis of the produced posting contents power(S450).

Description

Blog Analytics Method and System {METHOD AND SYSTEM FOR ANALYZING BLOG}

1 is a block diagram illustrating a blog analysis system according to an exemplary embodiment of the present invention.

2 is a diagram illustrating a blog network.

3 is a block diagram of the blog analysis server shown in FIG. 1.

4 is a flowchart illustrating a blog analysis method according to an embodiment of the present invention.

5 is a diagram illustrating an example of calculating a post content power according to an embodiment of the present invention.

FIG. 6 is a view illustrating exposure time and exposure time correction values of a post to explain an example of calculating user content power according to an embodiment of the present invention.

<Description of Drawing>

100: blog server, 130: blog analytics server,

132: ripple history generation unit, 134: article content power calculation unit,

136: user content power calculation unit, 160: database,

200: communication network, 300: user terminal

The present invention relates to a blog analysis method and system.

A social network is a network that expresses the relationship structure of a society as a whole by using relationship information between members in a society. It is called social network analysis to study the relationship structure and derive the unique characteristics of the society. In the past, it was difficult to obtain data indicating the relationship between members in social networks. Therefore, the focus of the existing research was to model the structure and characteristics of social networks without specific data on the relationship between members.

Due to the development of the Internet, these social networks have appeared online. The most distinctive feature of the online social network is that, unlike the existing social network, it stores specific information that can explain the relationship between members and information exchanged by the relationship in a database. Due to these characteristics, recent researches have been conducted to analyze online social networks.

A blog is a kind of personal website where users can store their posts online, and the blog world is a representative online social network of such blogs. Each blog user can have a relationship with someone else's blog, and these relationships form an online social network. Hereinafter, a social network consisting of a relationship between a blog and a blog is called a blog network.

The goal of an operator or company that runs a blogging world is to increase the profitability of the blogging world as a whole. Within the blog network, there are special blog users who play a role in encouraging other general blog users to actively use the blog service. If you can identify these users, you will be able to formulate various policies around blog service activation.

However, according to the previous researches, only the topological features of the social network have been considered to identify these users. However, if the criteria using only the topological features of the social network are used as described above, it is impossible to correctly select a user who has a great influence on the actual network. For example, a user with many neighbors in a blog network is not necessarily more influential to other users. Rather, a user with a few neighbors can exert a greater influence on other users. Therefore, in this case, it is difficult to identify blog users who have a substantial influence on other users.

Therefore, the technical problem to be achieved by the present invention is to provide a blog analysis method and system that can select a blog user that can exert a substantial influence on other users and is central to establishing various policies for activation of a blog service.

According to an embodiment of the present invention, a blog analysis method includes calculating a post content power of the post based on an action of a blog user on at least one post, and based on the post content power. Calculating the user content power.

The posting content power calculating step may include calculating direct content power that affects other users in the blog in which the post is written, and calculating indirect content power influencing other users in other blogs through which the post is propagated. It may include the step.

The article content power may be calculated by adding the value of the direct content power multiplied by the direct content power weight and the indirect content power multiplied by the indirect content power weight.

The direct content power may be calculated according to the following equation.

DRCP (D _{i, j} ) = wr × RC (D _{i, j} ) + wc × CC (D _{i, j} ) + ws × SC (D _{i, j} ) + wl × LC (D _{i, j} )

Here, D _{i, j} is the article, DRCP (D _{i, j} ) is the direct content power of the article D _{i, j} , RC (D _{i, j} ), CC (D _{i, j} ), SC (D _{i, j), LC (D i,} j) is each of the posts by D _i, the number of times a query _j, the posts D _i, the number of leaves a comment on _j, the posts D _i, the number of the scrap _j, the posts D _{i The} number of times the track has been written for _{, j} , and wr, wc, ws, and wl are the weights for viewing, commenting, scraping, and posting tracks, respectively.

The indirect content power may be calculated according to the following equation.

Where D _{i, j} is the post, IDCP (D _{i, j} ) is the indirect content power of the post D _{i, j} , D _{i ', j'} is the post directly reproduced from the post D _{i, j} , DRCP (D _{i ', j'} ) is the direct content power of the article D _{i ', j'} , and wd and wi are the direct content power weight and the indirect content power weight, respectively.

The article content power may be calculated according to the following equation.

DCP (D _{i, j} ) = wd × DRCP (D _{i, j} ) + wi × IDCP (D _{i, j} )

Here, D _{i, j} is the post, DCP (D _{i, j} ) is the post content power of the post D _{i, j} , DRCP (D _{i, j} ) is the direct content power of the post D _{i, j} , IDCP ( D _{i, j} ) is the indirect content power of the article D _{i, j} , and wd and wi are direct content power weights and indirect content power weights, respectively.

The calculating of the user content power may include correcting the posting content power according to the exposure time of the posting.

The at least one post includes a first post and a second post, and the user content power is a value obtained by multiplying a first exposure time correction value according to the exposure time of the first post by the post content power of the first post and the first post. The second exposure time correction value according to the exposure time of the post is multiplied by the value of the post content power of the second post, and calculated. The exposure time of the first post is longer than the exposure time of the second post. The exposure time correction value may be equal to or less than the second exposure time correction value.

The user content power may be calculated according to the following equation.

Here, UCP (U _i ) is the user content power of the user U _i , D _{i, j} is the at least one article, ET (D _{i, j} ) is the exposure time correction value of the article D _{i, j} , DCP (D _{i , j} ) is the post content power of the post D _{i, j} .

The method may further include generating a propagation history structurally representing a history of the postings based on the postings.

The method may further include selecting a power user of the blog connection network based on the user content power.

A computer readable medium according to another aspect of the present invention records a program for causing a computer to execute any of the above methods.

According to another aspect of the present invention, a blog analysis system includes a post content power calculation unit that calculates a post content power of the post based on a blog user's action on at least one post, and user content power based on the post content power. It includes a user content power calculation unit for calculating.

The post content power calculating unit may calculate direct content power that affects other users in the blog in which the post is written, and indirect content power that affects other users in another blog in which the post is propagated.

The post content power calculator may calculate the post content power by adding the value of the direct content power multiplied by the direct content power weight and the indirect content power multiplied by the indirect content power weight.

The user content power calculator may correct the post content power according to the exposure time of the post.

The apparatus may further include a propagation history generating unit configured to generate a propagation history structurally representing a history of the postings based on the posting.

The user content power calculator may select a power user of a blog connection network based on the user content power.

DETAILED DESCRIPTION Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings so that those skilled in the art may easily implement the present invention.

First, referring to FIGS. 1 to 3, a blog analysis system and a blog network to be analyzed in accordance with an embodiment of the present invention will be described in detail.

1 is a block diagram illustrating a blog analysis system according to an embodiment of the present invention, FIG. 2 is a diagram illustrating a blog connection network, and FIG. 3 is a block diagram of the blog analysis server shown in FIG. 1.

Referring to FIG. 1, a blog analysis system according to an embodiment of the present invention includes a blog server 100, a blog analysis server 130, and a database 160, and includes a plurality of user terminals through a communication network 200. It is connected with (300).

The blog server 100 provides a blog service to a user. A blog is a kind of personal web site that allows users to post freely according to their interests. The blog server 100 provides a blog to the user and allows the user to create a document (or post) including text, images, videos, music, and the like. Make sure that you write to your blog. In addition, the blog server 100 transmits and displays its own blog document and another user's blog document to the corresponding user terminal 300 through the communication network 200 at the request of the user.

The communication network 200 may include a telephone network as well as a data communication network including a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), the Internet, and the like. It can be used, and it does not matter wired and wireless, and it does not matter which communication method is used.

The user terminal 300 connects to and receives information from the blog server 100 and the blog analysis server 130 through the communication network 200, and displays the blog document from the blog server 100 in a browser (not shown). do. The user terminal 300 is provided with memory means such as a laptop computer, a workstation, a palmtop computer, a personal digital assistant (PDA), a web pad, a mobile communication terminal, as well as a desktop computer, and includes a microprocessor. It can be made of a terminal equipped with a computing power.

Each user may write a post in his blog through the user terminal 300. The other user can query, leave a comment, scrap, post a reply, etc. on the post through his user terminal 300. Here, inquiring is an action to read the post, leaving a comment is an action to leave their comments on the post, scraping is an action to copy the content of the post to register as a post in your blog, post a post This action creates a new post in your blog. Like normal posts, posts reproduced through scraping and posting can also trigger actions such as viewing, commenting, scraping, and posting a post by another user.

Referring to FIG. 2, the thick squares represent the blogs BA, BB, BC, and BD corresponding to the users U _A , U _B , U _C , and U _D , respectively, and the small squares inside the posts represent the posts existing in the blog. . The arrow shows the relationship between article 1 of user U _A and posts reproduced by the actions taken by users U _B , U _C , and U _D. For user U _A 's post 1, other users took various actions such as viewing, commenting, scraping, and posting tracks. In particular, user U _B scraped this post as his second post, and user U _C registered it as his third post by posting a reply to this post. In addition, user U _D scraped post 3 of user U _C as his second post.

The blog server 100 records the actions that the user has performed on his or her blog and other users' blogs. In other words, the blog server 100 is information about all the situations that occur in the blog, for example, the history of the user's posts on his blog, the history of the user viewed the blog posts of other users, the history of scraping, Blog data including a history of commenting on another user's blog post, a user's history of writing another user's blog post, and the like are recorded in the database 160.

The database 160 receives and stores blog posts and blog data from the blog server 100, and provides corresponding posts and data according to requests from the blog server 100 and the blog analysis server 130. Depending on the type of blog document, text, images, videos, etc. can be stored in a separate database, and blog data, etc. can also be stored in a separate database.

Referring to FIG. 3, the blog analysis server 130 includes a ripple history generator 132, a post content power calculator 134, and a user content power calculator 136. The spread history generator 132 generates a spread history based on blog data from the database 160. Ripple history is a set of data rearranged in a structure that allows you to effectively search for ripple relationships between blog posts. The post content power calculator 134 calculates the frequency of the user's action for each post with reference to the spread history, and calculates the content power of each post based on the frequency and weight of the action. The user content power calculator 136 calculates the user content power based on the content power of all posts owned by each user.

The blog analysis server 130 may be implemented by being included in the blog server 100. The blog analysis server 130 may be implemented as a separate system without being connected to the blog server 100 and the blog database 160 to receive blog data and perform a corresponding function. You may. Ripple history generation unit 132 may be implemented separately from the blog analysis server 130 as a separate module, the blog analysis server 130 receives the ripple history from the ripple history generation unit post content power and user content power Can be calculated.

Next, a blog analysis method for calculating user content power will be described in detail with reference to FIGS. 4 to 6.

4 is a flowchart illustrating a blog analysis method according to an embodiment of the present invention, FIG. 5 is a diagram illustrating an example of calculating a post content power according to an embodiment of the present invention, and FIG. 6 is an embodiment of the present invention. Accordingly, in order to explain an example of calculating user content power, it is a view showing exposure time and exposure time correction value of a post.

First, terms and symbols are defined for future description. U _i means a user whose identifier is i. D _i means a set of posts owned by U _i , and D _{i, j} means the j th post of U _i . The influence of the contents of the article D _{i, j} on other users is defined as the document contents power of the article, and is expressed as DCP (D _{i, j} ). In addition, the influence of the contents of the user U _i on other users is defined as the user contents power, and is expressed as UCP (U _i ). As mentioned earlier, there are five actions a user can take using the blog service: write, read, comment, scrap, and link. There are branches, and these actions are denoted by W, R, C, S, and L, respectively. When quantifying the influence of the content of a post, different weights can be assigned to give different actions to each action. The weights for actions W, R, C, S, and L are denoted by ww, wr, wc, ws, wl.

Referring to FIG. 4, the propagation history generator 132 generates a propagation history based on blog data from the database 160 (S410). The spread history is a structural representation of the history of the spread of this original post based on the original original post.

Table 1 shows an example of the ripple history generated for one original post. The first line of the propagation history shows information about the original post, and the lines below show information about the post created based on this original post. In this case, UID is a user identifier, D # represents an identifier of a corresponding post registered in each user blog, and TYPE indicates through which action the post is registered. PUID and PD # represent the user identifier of the last post and the identifier of the last post, respectively, which are the source of the post's ripple, and TIME means the time when the post was registered.

Referring to [Table 1], the user U _A has created and registered the original post D _{A, 1} at t1 (W), and the user U _C has posted the post (L) at t2 for the original post D _{A, 1} . Has registered the article D _{C, 3} . In addition, user U _B scraped the original post D _{A, 1} in t3 (S) _, and registered the post D _{B, 2} , and user U _D scraped the post D _{C, 3} registered by the user U _C to t4 (S) Post D _{D, 2} has been registered.

In this way, by creating a ripple history for each original post, it is easy to check the ripple relationship between the posts can effectively calculate the power of the post content.

The post content power calculating unit 134 calculates direct and indirect content power of each post based on the generated spill history (S420 and S430). If another user shows an action on a particular post, that means that the post was affected by the other user. Therefore, by multiplying the frequency of each action of the user for each post and the corresponding weight, the content power of the post can be quantified.

Each post can affect other users in the first blog you create, and it can affect other users in other blogs that have been propagated through scrapping or posting. The former is called the direct effect of the post and the latter is called the indirect effect of the post, and the quantified values are defined as the direct contents power and the indirect contents power of the post respectively. Posts D _i, referred to direct content-power and indirect contents power of _j DRCP (D _{i, j)} and (D _{i, j)} IDCP when each title, as an example, they Equation 1 and Equation 2 It can be calculated as Of course they can be calculated in different ways.

DRCP (D _{i, j} ) = wr × RC (D _{i, j} ) + wc × CC (D _{i, j} ) + ws × SC (D _{i, j} ) + wl × LC (D _{i, j} )

Here, RC (D _{i, j} ), CC (D _{i, j} ), SC (D _{i, j} ), LC (D _{i, j} ) refer to the number of times the article D _{i, j} has been viewed, and the article D _{i, j} comments left on the count means the number of trackbacks only for the posts D _i, a number of scrap _j, posts D _{i, j.}

Here, the post D _{i ', j'} represents a post reproduced directly from the post D _{i, j} , and wd and wi are direct content power weights and indirect content power weights, respectively.

The post content power calculator 134 calculates the content power of each post based on the calculated direct and indirect content power (S440). The post content power can be calculated as the sum of the direct content power and the indirect content power of the post as shown in [Equation 3]. I can regulate it.

DCP (D _{i, j} ) = wd × DRCP (D _{i, j} ) + wi × IDCP (D _{i, j} )

5, an example of calculating content power of a post using a spill history will be described. Figure 5 shows an example of calculating the content power of the posts using the ripple relationship between the posts shown in the ripple history of [Table 1]. Here, the weight wd of the direct content power and the weight wi of the indirect content power are equally set to 1, respectively. The value in each post in turn means (Direct Content Power, Indirect Content Power, Post Content Power). Posts D _{D, 2} have no post-repeat record, so the direct content power of 5 becomes the post content power. Therefore _, the indirect content power of D _{C, 3} is 5, and the post content power of D _{C, 3} is 8 by adding 3, which is a direct content power. In addition, D _{A, 1,} which is the original source of a post _, has 12 as an indirect content power by adding 8, which is a post content power of D _{C, 3, and 4, which} is a post content power of D _{B, 2} . Add 4 to D _{A, 1 to have} a post content power of 16.

The user content power calculation unit 136 calculates the content power of each post and calculates the user content power using the content (S450).

On the other hand, the content power of the post tends to increase in proportion to the time exposed after the post. Posts that have been registered a long time ago may be distorted as having a large content power because of a long exposed time even if the actual impact is small compared to a recently registered post.

Accordingly, the blog analysis method according to the embodiment of the present invention calculates the user content power by reflecting the influence of the exposure time on the content power of each post. In other words, the content power of each post is corrected according to the exposure time to calculate the user content power. To this end, first, a blog analysis period for calculating user content power is set (for example, 10 days), and the exposure time correction value of the corresponding article is calculated by referring to the time when each post is registered within the analysis period. The exposure time correction value is relatively small when the exposure time is long, and relatively large when the exposure time is short. As an example, the exposure time correction value may be calculated as a ratio obtained by subtracting the exposed time from the analysis time corresponding to the analysis period and dividing the result value by the analysis time. However, the exposure time correction value may be calculated in another way, for example, to be inversely proportional to the exposure time.

The user content power is calculated by multiplying the exposure time correction value calculated for each post by the corresponding post content power and adding the resulting values. Therefore, the content power [UCP (U _i )] of the user U _i is calculated as shown in [Equation 4]. In this case, the value obtained by multiplying the exposure time correction value and the post content power for each post may be referred to as the corrected content power of the post. By calculating the user content power as described above, the distortion of the post content power due to the difference in the exposure time of each post can be reduced, and the accuracy of the user content power can be improved.

Here, ET (D _{i, j} ) is the exposure time correction value of the article D _{i, j} .

Next, an example of calculating user content power will be described with reference to FIG. 6 and [Table 2]. User U _A registered the posts D _{A, 1} , D _{A, 2} , D _{A, 3} , D _{A, 4} in sequence during the analysis time from the start time of analysis to the end time of analysis, as indicated by the arrows. Power was calculated to be 50, 35, 35 and 10, respectively. The exposure time of each post is the time from the time when the post is registered to the analysis end time, and the exposure time correction values are 0.25, 0.4, 0.8, and 0.95 in order of the longest exposure time. Therefore, the user content power of the user U _A is UCP (U _A ) = 50 × 0.25 + 35 × 0.4 + 35 × 0.8 + 10 × 0.95 = 64.

The blog analysis server 130 calculates the content power of each user in this way, and then sorts the users in the order of the highest user content power, thereby affecting the actions of the users who have high influence due to the content in the blog network, that is, other users. A power user who is a large user can be selected (S460). In other words, the top n users with large user content power can be selected as power users.

By selecting the power users, the policy for activating the blog network can be performed. For example, you can focus on promoting your new product to power users, and influence others in your blog network to have a positive awareness of your new product. In addition, it can provide a variety of benefits to power users to encourage them to actively use the blog service to act as a catalyst for the activation of the entire blog network.

In the blog analysis method according to an embodiment of the present invention, the weights ww, wr, wc, ws, wl, wd, and wi may be determined differently depending on which action is important, for example, 7, 1, 3, Can be set as 7, 7, 1, 1 respectively. In addition, these weights may be determined in a policy for blog service activation.

Embodiments of the invention include a computer readable medium containing program instructions for performing various computer-implemented operations. This medium records a program or process for executing a method of analyzing a blog as described so far. The media may include, alone or in combination with the program instructions, data files, data structures, and the like. Examples of such media include magnetic media such as hard disks, floppy disks and magnetic tape, optical recording media such as CDs and DVDs, floppy disks and program commands such as magnetic-optical media, ROM, RAM, flash memory, and the like. Hardware devices configured to store and perform such operations. Alternatively, the medium may be a transmission medium such as an optical or metal wire, a waveguide, or the like including a carrier wave for transmitting a signal specifying a program command, a data structure, and the like. Examples of program instructions include not only machine code generated by a compiler, but also high-level language code that can be executed by a computer using an interpreter or the like.

Although the preferred embodiments of the present invention have been described in detail above, the scope of the present invention is not limited thereto, and various modifications and improvements of those skilled in the art using the basic concepts of the present invention defined in the following claims are also provided. It belongs to the scope of rights.

As described above, according to the present invention, the post content power is calculated based on a user's action frequency for each post in the blog network, and the user content power is calculated based on each post content power. You can select a power user who will be central to your policy.

Claims (19)

Calculating a post content power of the post based on a blog user's action on at least one post, and Calculating user content power based on the article content power Blog analysis method comprising a. In claim 1, The article content power calculation step, Calculating direct content power that affects other users in the blog in which the post is written, and Calculating indirect content power that affects other users in other blogs where the post is propagated Blog analysis method comprising a. In claim 2, The article content power is calculated by adding the value of the direct content power multiplied by the direct content power weight and the indirect content power multiplied by the indirect content power weight. In claim 2, The direct content power is calculated according to the following equation. DRCP (D _{i, j} ) = wr × RC (D _{i, j} ) + wc × CC (D _{i, j} ) + ws × SC (D _{i, j} ) + wl × LC (D _{i, j} ) Here, D _{i, j} is the article, DRCP (D _{i, j} ) is the direct content power of the article D _{i, j} , RC (D _{i, j} ), CC (D _{i, j} ), SC (D _{i, j), LC (D i,} j) is each of the posts by D _i, the number of times a query _j, the posts D _i, the number of leaves a comment on _j, the posts D _i, the number of the scrap _j, the posts D _{i The} number of times the track has been written for _{, j} , and wr, wc, ws, and wl are the weights for viewing, commenting, scraping, and posting tracks, respectively. In claim 2, The indirect content power is calculated according to the following equation.

Where D _{i, j} is the post, IDCP (D _{i, j} ) is the indirect content power of the post D _{i, j} , D _{i ', j'} is the post directly reproduced from the post D _{i, j} , DRCP (D _{i ', j'} ) is the direct content power of the article D _{i ', j'} , and wd and wi are the direct content power weight and the indirect content power weight, respectively. In claim 2, The article content power is calculated according to the following equation. DCP (D _{i, j} ) = wd × DRCP (D _{i, j} ) + wi × IDCP (D _{i, j} ) Here, D _{i, j} is the post, DCP (D _{i, j} ) is the post content power of the post D _{i, j} , DRCP (D _{i, j} ) is the direct content power of the post D _{i, j} , IDCP ( D _{i, j} ) is the indirect content power of the article D _{i, j} , and wd and wi are direct content power weights and indirect content power weights, respectively. In claim 1, The calculating of the user content power may include correcting the post content power according to the exposure time of the post. In claim 1, The at least one post includes a first post and a second post, The user content power is a value obtained by multiplying a first exposure time correction value according to the exposure time of the first post, the post content power of the first post, a second exposure time correction value according to the exposure time of the second post, and the second It is calculated by multiplying the post content power of the post. If the exposure time of the first post is longer than the exposure time of the second post, the first exposure time correction value is equal to or less than the second exposure time correction value. How to Analyze Your Blog. In claim 1, The user content power is calculated according to the following equation.

Here, UCP (U _i ) is the user content power of the user U _i , D _{i, j} is the at least one article, ET (D _{i, j} ) is the exposure time correction value of the article D _{i, j} , DCP (D _{i , j} ) is the post content power of the post D _{i, j} . In claim 1, And generating a propagation history structurally indicating a history of the posting being spread based on the posting. In claim 1, And selecting a power user of a blog connection network based on the user content power. A computer-readable medium having recorded thereon a program for executing the method of claim 1 on a computer. A post content power calculator configured to calculate a post content power of the post based on a blog user's action on at least one post, and A user content power calculator configured to calculate user content power based on the article content power Blog analysis system comprising a. In claim 13, The blog content power calculation unit is a blog analysis system that calculates the direct content power that affects other users in the blog in which the post was written, and the indirect content power affecting other users in the other blog in which the post was propagated. The method of claim 14, And the post content power calculating unit calculates the post content power by adding the value of the direct content power multiplied by the direct content power weight and the indirect content power multiplied by the indirect content power weight. In claim 13, The user content power calculator is a blog analysis system for correcting the post content power according to the exposure time of the post. In claim 13, The at least one post includes a first post and a second post, The user content power is a value obtained by multiplying a first exposure time correction value according to the exposure time of the first post, the post content power of the first post, a second exposure time correction value according to the exposure time of the second post, and the second It is calculated by multiplying the post content power of the post. If the exposure time of the first post is longer than the exposure time of the second post, the first exposure time correction value is equal to or less than the second exposure time correction value. Blog Analytics System. In claim 13, The blog analysis system further comprises a ripple history generation unit for generating a ripple history structurally representing the history of the posting based on the post. In claim 13, And the user content power calculator selects a power user of a blog connection network based on the user content power.

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