KR20210148573A - Systems and methods for gathering public data of SNS user channel and providing influence reports based on the collected public data - Google Patents

Abstract

The present invention relates to a technical idea for quantitatively evaluating SNS user channels and measuring influence based on the evaluated values. According to an embodiment of the present invention, an influence measurement system includes: a collection processing unit which collects public data about the SNS user channels classified by pre-registered accounts from an SNS (Social Network Service) server; an analysis unit which analyzes the collected public data to analyze an influence score for the SNS user channel; and report generation unit for generating an influence report based on the analyzed influence score. The present invention provides objective evaluation criteria for the SNS user channels.

Description

Systems and methods for gathering public data of SNS user channel and providing influence reports based on the collected public data}

The present invention relates to a technical idea for quantitatively evaluating an SNS user channel and measuring influence based on the evaluated values.

Influencer is the suffix '-er', meaning 'person', added to the word 'influence', which means 'to influence', and means 'a person who exerts influence'. It is interpreted as a collective term for 'power bloggers' who run blogs, social network service (SNS) users with hundreds of thousands of followers, or single broadcasters. Marketing using SNS user channels based on influencers is a marketing tool that uses them to promote products or services.

Influencer marketing has been attracting attention as an effective marketing method since the late 2010s due to the relatively cheap price compared to using a famous actor as a model by paying a high price, and due to the surge in SNS users.

Nevertheless, there is a problem in influencer marketing that objective verification of influencers is difficult.

For this reason, it is difficult to objectively verify the publicity effect, and as a result, it is often the case that the advertising effect falls short of the expected effect expected by the company.

Since the value standard set by the influencer is determined by the subjectivity of the influencer, there is no choice but to give too much value to one's influence.

Korean Patent Registration No. 10-2013186 "E-commerce system using influencers" Korean Patent Registration No. 10-2068123 "Apparatus and method for providing marketing service using influencers"

An object of the present invention is to provide an objective evaluation criterion for an SNS user channel.

An object of the present invention is to evaluate the relationship between the SNS user channel and the sales increase of the company by quantifying the influence on the SNS user channel.

An object of the present invention is to provide the influence score and SNS user channel data finally provided to the user in the form of a report by utilizing the influence solution.

Influence measurement system according to an embodiment, a collection processing unit that requests and collects public data for SNS user channels classified by accounts previously registered as an SNS (Social Network Service) server, or collects them by a crawling method, the collected disclosure It may include an analysis unit that analyzes the data to analyze the influence score for the SNS user channel, and a report generation unit that generates an influence report based on the analyzed influence score.

The collection processing unit according to an embodiment, the public data including at least one of profile information registered in the SNS (Social Network Service) server, and posting information, channel information, image information, and image information, the SNS (Social Network Service) Network Service) can be collected by querying the server.

The collection processing unit according to an embodiment may collect public data related to the SNS user channel that is published online by the SNS (Social Network Service) server through crawling.

The collection processing unit according to an embodiment may collect the public data in a crawling manner using at least one of a virtual mobile simulator, a virtual hosting-multi-browser, and an API request.

The report generating unit according to an embodiment, based on the influence score, SNS account basic information, SNS account analysis related information, the actual active related account ratio, real response rate, can generate an influence report that reflects at least one of the reach rate have.

The operating method of the system for measuring the influence of the SNS user channel according to an embodiment includes the steps of collecting public data on the SNS user channel classified by the previously registered account from the SNS (Social Network Service) server, the collected public data Analyzing and analyzing the influence score for the SNS user channel, and generating an influence report based on the analyzed influence score.

The collecting of the public data according to an embodiment includes public data including at least one of profile information registered in the SNS (Social Network Service) server, and posting information, channel information, image information, and image information, It may include the step of collecting by querying the SNS (Social Network Service) server.

Collecting the public data according to an embodiment may include collecting public data related to the SNS user channel that is published online by the SNS (Social Network Service) server through crawling.

According to an embodiment, it is possible to provide an objective evaluation criterion for an SNS user channel.

According to an embodiment, by quantifying the influence on the SNS user channel, it is possible to evaluate the correlation between the SNS user channel and the increase in sales of the company.

According to an embodiment, it aims to provide the influence score and SNS user channel data finally provided to the user in the form of a report by utilizing the influence solution.

1 is a diagram illustrating the entire system to which the influence measurement system is applied.
2 is a view for explaining an influence measurement system according to an embodiment.
3 is a view for explaining a collection processing unit according to an embodiment.
4 is a view for explaining a method of operating an influence measurement system according to an embodiment.

Specific structural or functional descriptions of the embodiments according to the concept of the present invention disclosed herein are only exemplified for the purpose of explaining the embodiments according to the concept of the present invention, and the embodiment according to the concept of the present invention These may be embodied in various forms and are not limited to the embodiments described herein.

Since the embodiments according to the concept of the present invention may have various changes and may have various forms, the embodiments will be illustrated in the drawings and described in detail herein. However, this is not intended to limit the embodiments according to the concept of the present invention to specific disclosed forms, and includes changes, equivalents, or substitutes included in the spirit and scope of the present invention.

Terms such as first or second may be used to describe various elements, but the elements should not be limited by the terms. The above terms are used only for the purpose of distinguishing one component from other components, for example, without departing from the scope of rights according to the concept of the present invention, a first component may be named a second component, Similarly, the second component may also be referred to as the first component.

When an element is referred to as being “connected” or “connected” to another element, it is understood that it may be directly connected or connected to the other element, but other elements may exist in between. it should be On the other hand, when it is said that a certain element is "directly connected" or "directly connected" to another element, it should be understood that the other element does not exist in the middle. Expressions describing the relationship between elements, for example, “between” and “between” or “directly adjacent to”, etc. should be interpreted similarly.

The terminology used herein is used only to describe specific embodiments, and is not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly dictates otherwise. In the present specification, terms such as "comprise" or "have" are intended to designate that the described feature, number, step, operation, component, part, or combination thereof exists, and includes one or more other features or numbers, It should be understood that the possibility of the presence or addition of steps, operations, components, parts or combinations thereof is not precluded in advance.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and should not be interpreted in an ideal or excessively formal meaning unless explicitly defined in the present specification. does not

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, the scope of the patent application is not limited or limited by these embodiments. Like reference numerals in each figure indicate like elements.

1 is a diagram illustrating the entire system 100 to which the influence measurement system 120 is applied.

Reference numeral 110 denotes a terminal of a user who intends to examine the influence on the SNS user channel, which is defined as a user terminal and may be interpreted as a communication device such as a smartphone, a computer terminal, and a tablet.

The user can view the influence report on the SNS user channel to review the influence on the SNS user channel.

To this end, the user may input information for uniquely identifying the SNS user channel, for example, account information for the SNS user channel, into the user terminal 110 .

Accordingly, the user terminal 110 may request an influence report on the SNS user channel to the influence measurement system using the input information.

The influence measurement system 120 according to an embodiment may operate by receiving a request for an influence report.

To this end, the influence measurement system 120 may collect public data on the SNS user channel classified by the pre-registered account from the SNS (Social Network Service) server 130 .

The public data may be interpreted to include both data directly or indirectly provided from each SNS server or user data (profile, activity, media data) that can be collected and disclosed on the SNS.

Next, the influence measurement system 120 may analyze the influence score for the SNS user channel by analyzing the collected public data.

As an example, the influence score is based on a comprehensive influence score calculated according to the number of followers of the account created through the engine, and the influence measurement system 120 ranks SNS user channels with the influence score and ranks according to the upper percentage It can be displayed as

A detailed configuration for calculating and analyzing an influence score using public data will be described in more detail with reference to FIG. 2 below.

In addition, the influence measurement system 120 may generate an influence report based on the analyzed influence score.

2 is a view for explaining the influence measurement system 200 according to an embodiment.

The influence measurement system 200 according to an embodiment is a solution that can manage and manage a series of processes from collecting SNS data to report generation, and provides APIs for the final report and calculated data through collection, analysis, and generation processes. (Protocol) can produce a result.

The collection processing unit 210 according to an embodiment may collect public data for SNS user channels classified by pre-registered accounts from a social network service (SNS) server.

For example, the collection processing unit 210 collects public data including at least one of profile information registered in an SNS (Social Network Service) server, and posting information, channel information, image information, and image information, SNS (Social Network Service) ) can be collected by querying the server.

Meanwhile, the collection processing unit 210 may collect public data related to an SNS user channel published online by an SNS (Social Network Service) server through crawling.

In particular, the collection processing unit 210 may collect the public data in a crawling manner using at least one of a virtual mobile simulator, a virtual hosting-multi browser, and an API request.

As a more specific example, the public data collected by the collection processing unit 210 from the SNS server may have a different form according to each service of the SNS.

For example, in the case of Instagram, the collection processing unit 210 may collect profile information and posting information.

The profile information may include a unique user value, a user ID (text), a biography (text), a profile image (image), a user name, and the like.

In addition, posting information may include uploaded posts (recent 2 weeks), uploaded images (images), posts (text), number of comments, number of likes, comment list, like list, etc. It may include information, comment content, and comment information of a comment.

In addition, the like lease may include profile information of a user who has selected 'Like'.

In the case of YouTube or TikTok, channel information and video information can be collected.

Channel information can indicate channel unique values, channel ID (text), description (text), profile image (image), and channel title, and video information includes uploaded video information, video title, video description, number of comments, number of likes, You can display the list of comments, the number of views, and keywords.

The collection processing unit 210 may crawl public data by using a crawling bot or the like.

In particular, the collection processing unit 210 may utilize a bot-based automation technology that automatically collects, stores, and controls data based on keywords or trends of user data (profile, activity, media data) that are publicly available on SNS.

A method for the collection processing unit 210 to collect data may include the following three methods.

First, the collection processing unit 210 may collect public data by using a virtual mobile simulator. For example, the collection processing unit 210 may generate about 100 or more virtual devices per hour and may collect data by accessing public data of SNS in the same manner as a request of an actual user.

Also, the collection processing unit 210 may collect public data by using a virtual hosting-multi-browser. For example, the collection processing unit 210 may collect data by searching for data of keywords and related profiles through virtual hosting in the same environment as a PC browser.

The collection processing unit 210 may collect public data through an API request. For example, the collection processing unit 210 may collect the permitted data by using an access right token.

On the other hand, the collection processing unit 210 can utilize the above method central cloud computing to determine by situation, by time, whether an error occurs, etc., and comprehensively manage it efficiently through load balancing.

The collection processing unit 210 may classify the collected public data into metadata, captions, images, images, etc., store each data as time series data, and transmit it to the analysis unit 220 .

Through this, the collection processing unit 210 is capable of collecting more than about 30,000 data per hour, such as SNS accounts, related accounts, and related posts.

The analysis unit 220 according to an embodiment may analyze the influence score for the SNS user channel by analyzing the collected public data.

The analysis unit 220 according to an embodiment may analyze the influence score by classifying the collected public data and storing the classified public data in time series.

The analysis unit 220 according to an embodiment may analyze at least one of language, ID, and recent activity of the audience from the collected public data to select a genuine audience.

As a more specific example, the analysis unit 220 according to an embodiment may process an audience analysis based on the collected public data.

Audience can be interpreted as subscribing followers, people who commented on posts, and people who input 'likes'. The analysis unit 220 may calculate audience quality by analyzing fake/real patterns of the audiences based on the collected public data.

For example, the analysis unit 220 according to an embodiment may analyze the audience in a manner of finding fake signs.

More specifically, the analysis unit 220 according to an embodiment may analyze the language used by the followers, abnormal IDs of the followers, recent activities of the followers, etc. based on the public data.

The analysis unit 220 according to an embodiment may extract accounts using a language other than the country of the corresponding SNS user channel as the language used by the followers.

While social media user channels only tag specific languages, accounts that speak a completely different language are more likely to be fake accounts.

Meanwhile, the analysis unit 220 according to an exemplary embodiment may analyze abnormal IDs of followers, and in particular, may examine an identification code or a computer-generated ID pattern pattern.

For example, the analysis unit 220 may determine that there is a high possibility of a fake account with respect to an unknown ID form such as 'ewiafji.5ijiena'.

Also, the analysis unit 220 may classify a case in which there is a lot of 'Like' or 'Following' activity but no posting or no follower increase as a recent activity of the followers as a fake pattern.

The analysis unit 220 according to an embodiment may perform natural language processing using machine learning on text-based data included in the collected public data, and classify the natural language-processed public data by tagging it.

The analysis unit 220 tags text-based data, such as captions, tags, and comments, as natural language processing using machine learning to tag and classify all related sentences and words of the text, such as false sentences, advertisement keywords, emotions and relevance, and morphological analysis. can do.

In addition, the analysis unit 220 can classify by determining the person classification of the image, the place association, the brand suitability, the category, etc. by using a machine learning and training set that self-learns image-based data such as a profile image and a posting image. .

The analysis unit 220 may classify each word and sentence of language classification into more than 150 languages and guess the language used in the profile based on the most used language of the sentence, and translate into about 50 languages Thus, it is mapped for each related word.

If you have a lot of people who only speak a language that is completely different from the language used on your profile, this can negatively affect your score.

The analysis unit 220 may find a related meaning of the word by using Named Entity Recognition (NER) after breaking the word classification sentence or hashtag into words.

To this end, the analysis unit 220 may use the data set published by Google in the EMNLP, and may assign and tag the meaning of the word itself through the word set being recorded by itself.

The analysis unit 220 may check whether each word and sentence is related to sales or advertisement, and may predict who the target is, such as whether a brand or company is mentioned, or a person is mentioned.

The analysis unit 220 may analyze the positive and negative meanings of words in each text analysis sentence, and connect related people of the words.

In addition, authentic phrases can be detected by the similarity of words or sentences mainly used by bots.

The analysis unit 220 may give a good score if the sentence is related to the posting image in conjunction with the image analysis.

The classified and analyzed texts are all managed as statistical values by the analysis unit 220 , and frequently mentioned or used nouns or phrases such as the latest trending buzzwords are classified.

The analysis unit 220 according to an embodiment may process at least one of face detection, food detection, text extraction, and prediction for image-based data included in the collected public data.

As an example, the analysis unit 220 may analyze all images that can be imaged or extracted, such as a thumbnail and an I-Frame of an image, a posting to which an image is uploaded, a profile image, and the like.

In particular, the analysis unit 220 may check whether a person is included in the face detection profile image or the image of the posting, or how many people appear and what is their age, and the gender may also be predicted using the trained dataset.

In addition, the analysis unit 220 can detect food, the OCR posting image can extract the text by detecting an image consisting of an advertisement poster or phrase, and the extracted text can be classified in connection with the text analysis.

As a prediction for tagging and classification, the analysis unit 220 may predict the location of the image or what objects are included in the image. For example, it is possible to check which posting images are uploaded by predicting cars, streets, beaches, cosmetics, food, etc.

ResNet model of CNN can be utilized for the model of deep learning, and it can classify and categorize more than 10,000 objects by combining pre-trained data and newly-learned data.

In addition, the analysis unit 220 may analyze the images of the most popular posts of the posting certificate of the posting Fit profile.

The analysis unit 220 according to an embodiment processes at least one analysis from among the collected public data, comment analysis, follower user analysis, like user analysis, and posting analysis, and using the processed analysis result, automatic weighting, The influence can be quantified by reflecting at least one of the time weight, the number of users who can truly respond, and the median purchase conversion rate. In addition, the analysis unit 220 according to an embodiment may reflect the quantified influence to the influence score.

The analysis unit 220 according to an embodiment may process the analysis of the comments from the collected public data, and after natural language processing for the comment sentences, determine whether the comment is a real user or a fake user and may be analyzed.

As a result of analyzing the comments, the analysis unit 220 according to an embodiment may determine that the natural language-processed comment sentence is a comment sentence related to a purchase-related inquiry or a posting image as a real user.

As a result of analyzing the comments, the analysis unit 220 according to an embodiment may determine a fake user if the natural language-processed comment sentence is a bot macro comment or a comment of another SNS user channel.

The analysis unit 220 according to an embodiment processes the follower user analysis from the collected public data, and may determine at least one of a real user who is active with respect to the follower, a fake user who is not active, and a business account.

The analysis unit 220 according to an embodiment may process the like user analysis from the collected public data, and may determine whether the input likes is an input from a real user or an input from a fake user.

Meanwhile, the analysis unit 220 according to an exemplary embodiment may process posting analysis from the collected public data, but may analyze a sales-related positive factor or a sales-related negative factor in a registered posting.

The analysis unit 220 according to an exemplary embodiment may assign a weight to a sales-related positive factor when there is a posting that has been sold for three or more orders.

The analysis unit 220 according to an embodiment may assign a weight to the sales-related negative factor when the ratio of fake users with likes and comments on sales-related postings is equal to or greater than a standard.

In addition, the analysis unit 220 according to an embodiment may calculate a final calculated score in consideration of a user who can truly respond, an automatic weight, and a time weight, and determine the influence score based on the calculated final calculated score. .

The analysis unit 220 may analyze the collected public data immediately upon collection, and may go through a three-step analysis process based on the profile.

First, correlation and similarity analysis of profile-related data, statistical calculation of related data, big data (Hadoop)-based natural language processing of related texts are possible, and classification and prediction using machine learning of related images can be processed.

The analysis unit 220 according to an embodiment may utilize the result of comment analysis for the final calculated score. Comment analysis is a natural language processing of comment sentences, and it is possible to analyze purchase-related inquiries and comments on posting images to determine whether comments are real users.

In addition, in order to analyze a fake comment user, it is possible to analyze bot macro comments and posts from other SNS user channels, and to analyze a follower user, a general user condition can be analyzed, and it can be confirmed whether it is an active user.

The fake user condition that the analysis unit 220 can analyze may be in the form of no posting, no followers, or a form that is maintained below the standard.

On the other hand, as a condition of a fake user, meaningless IDs and usernames may be in the third language of the language used, such as Arabic or Greek, or as a business account, Instagram business account and user name may contain shop and advertisement related content (natural language processing). have.

Meanwhile, the analysis unit 220 may classify a user who has inputted 'Like' as a real user.

An active user may be interpreted as a user who actually appears in activities such as likes and followers.

In the case of a user who entered a fake 'Like', it is possible to analyze the fake user (analysis result) post analysis. On the other hand, the analysis unit 220 may give a weight to the sales-related positive factor when there is a posting that has been sold for three or more orders. In addition, as a negative factor related to sales, a negative weight may be given to a case where the ratio of fake users of sales related posts is high.

The analysis unit 220 is a user capable of real reactions, and may calculate 'Real user comment * (fake ratio formula) + Like real user * (fake ratio formula) + follower real user ratio'.

In addition, the analysis unit 220 may calculate 'real user comments * (fake ratio formula) + like real users * (fake ratio formula) + follower real user ratio * random weight' as a user who can respond to the real thing.

Meanwhile, the analysis unit 220 may calculate 'real / fake * arbitrary value' as an automatic weight value. In addition, the analysis unit 220 may calculate 'the number of recent postings - within 3 days (5), within 1 week (3), within a month (2), within 3 months (1)' as a time weight value.

Utilizing these information, the analysis unit 220 can calculate 'auto weight * number of users who can truly respond * time weight * median purchase conversion rate + (post analysis factor ratio score)' for the final calculated score have.

The report generating unit 230 according to an embodiment may generate an influence report based on the analyzed influence score.

The impact report may provide a variety of analysis information, including the impact score.

Specifically, the influence report may be provided in the form of an influence score and SNS user channel data, which are ultimately provided to the user by utilizing the influence solution.

In particular, the influence report is SNS account basic information, and may indicate a nickname or ID of an SNS user channel or an SNS general account. In addition, the influence report can be displayed as text or images such as biographies, profile images, and categories. Influence reports are displayed in numbers such as the number of followers (subscribers), the number of followings, and the number of postings (media). SNS account analysis related information - Report generation related information such as analysis time, analysis accuracy, analysis request time, etc. can be displayed. In addition, the influence report is an influence score on the SNS user channel, and may indicate the overall influence of the account created through the engine.

Among them, the influence score is based on each influence index calculated according to the number of followers of the account created through the analysis unit 220, and the influence measurement system 120 ranks the SNS user channels with the influence score. It can be graded and displayed according to the top percentage.

The impact report may include a genuine response rate generated through the analysis unit 220 .

Real response rates include average responses to related account posting activity.

If the real response rate is used, it can be used as a criterion for selecting genuine comments by determining whether or not the comments analyzed through the engine's natural language processing are comments generated by simple macro operations.

In addition, it is possible to determine whether the 'Like' is a real 'Like' input through the ratio of active accounts through the real response rate, and the real response rate can be calculated from the real likes and real comments.

In addition, the influence report can display the distribution of people who continuously 'liked' or left comments on a post based on the true response rate.

The impact report may indicate the rate of reach.

Reach rate can be displayed as a reach figure by predicting the reach rate through postings through the real rate of related accounts, and interest accounts (SNS user channel mentions/SNS user channel follows brands / Audiences follow brands) can indicate

In addition, the influence report may display an influence index.

The influence index is each item necessary to quantify the influence on the SNS user channel, and the influence score can be finally calculated by combining the influence indices.

For example, for the influence index, items related to all relevant audience users analyzed based on the target's followers, subscribers, and recent postings for a month may be extracted. In addition, it is possible to comprehensively quantify the influence by calculating the proportion (reach rate) of real users who can reach the target's sales posting or marketing posting, and calculating the response rate and posting frequency.

In the list information, all SNS user channels analyzed in influence can be searched by category and field to list influence scores and SNS accounts. In addition, in the category, you can filter the posted images, captions, related products of videos, and related places, and you can filter by the gender and age group of the corresponding account audience, and the number of followers.

3 is a view for explaining the collection processing unit 300 according to an embodiment.

The collection processing unit 300 according to an embodiment is a bot-based automation technology that automatically collects, stores, and controls data based on keywords or trends for user data (profile, activity, media data) published in social media. is composed

The structure of the data source 310 , the storage 1 320 , and the storage 2 330 may be shown.

First, the data source 310 may collect public data from public accounts through five or more resources.

Resources used for this may include Open Public Data, 3rd Party Data, Browser Data, Public API, Auth API, and the like.

The storage 1 320 may include a mobile simulator 321 , a virtual computing 322 , a development API 323 , a scraping manager 324 , and the like. In addition, the storage 1 320 has a limit in which the amount of data for analyzing the request limit of the general programming interface of the SNS is not sufficient. can be collected and resolved.

The storage 2 330 may include a data lake cloud storage 331 and a database 332 . The storage 2 330 stores only non-identifier data for data privacy protection in the database, and the previously collected raw data may be stored in the data lake story.

In particular, the collection processing unit 300 may generate about 100 or more virtual devices per hour through the virtual mobile simulator 321 , and may collect data by accessing the public data of the SNS in the same manner as a real user's request.

In addition, the collection processing unit 300 uses virtual hosting-multi-browser keywords and associations with virtual hosting in the same environment as the PC browser. You can collect data by searching the data of profiles.

The collection processing unit 300 may collect data by using an access right token for data permitted to a service and a provider.

4 is a view for explaining a method of operating an influence measurement system according to an embodiment.

The method of operation of the influence measurement system according to an embodiment may collect public data on SNS user channels classified by pre-registered accounts from a social network service (SNS) server (step 401).

For example, in order to collect public data, the operating method of the influence measuring system includes at least one of profile information registered in an SNS (Social Network Service) server, and posting information, channel information, image information, and image information. Data may be collected by querying the SNS (Social Network Service) server.

On the other hand, the operating method of the influence measurement system may collect public data related to an SNS user channel published online by a social network service (SNS) server through crawling in order to collect public data.

Next, the method of operation of the influence measurement system may analyze the influence score for the SNS user channel by analyzing the collected public data (step 402).

In addition, an influence report may be generated based on the analyzed influence score (step 403).

The operating method of the influence measurement system may analyze the influence score by classifying the collected public data in order to analyze the influence score, and time-series storing the classified public data.

In addition, the operating method of the influence measurement system may select a genuine audience by analyzing at least one of language, ID, and recent activity for the audience from the collected public data in order to analyze the influence score.

The operation method of the influence measurement system is to perform natural language processing using machine learning on the text-based data included in the collected public data to analyze the influence score, and to classify the natural language-processed public data by tagging it. can

The operation method of the influence measurement system may process at least one of face detection, food detection, text extraction, and prediction for image-based data included in the collected public data in order to analyze the influence score.

In order to analyze the influence score, the method of operation of the influence measurement system processes at least one analysis from among the collected public data, comment analysis, follower user analysis, like user analysis, and posting analysis, automatic weighting, time weighting, real reaction The influence can be quantified by reflecting at least one of the number of available users and the median purchase conversion rate.

In addition, the operating method of the influence measurement system can reflect the quantified influence to the influence score.

After all, by using the present invention, it is possible to provide an objective evaluation criterion for the SNS user channel. In addition, by quantifying the influence on the SNS user channel, the correlation between the SNS user channel and the company's sales increase can be evaluated, and the influence score and SNS user channel data that are finally provided to users by using the influence solution are reported. can be provided in the form.

The device described above may be implemented as a hardware component, a software component, and/or a combination of the hardware component and the software component. For example, devices and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable array (FPA), It may be implemented using one or more general purpose or special purpose computers, such as a programmable logic unit (PLU), microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. A processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For convenience of understanding, although one processing device is sometimes described as being used, one of ordinary skill in the art will recognize that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that can include For example, the processing device may include a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as parallel processors.

Software may comprise a computer program, code, instructions, or a combination of one or more thereof, which configures a processing device to operate as desired or is independently or collectively processed You can command the device. The software and/or data may be any kind of machine, component, physical device, virtual equipment, computer storage medium or apparatus, to be interpreted by or to provide instructions or data to the processing device. , or may be permanently or temporarily embody in a transmitted signal wave. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored in one or more computer-readable recording media.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic media such as floppy disks. - includes magneto-optical media, and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

As described above, although the embodiments have been described with reference to the limited drawings, various modifications and variations are possible by those skilled in the art from the above description. For example, the described techniques are performed in an order different from the described method, and/or the described components of the system, structure, apparatus, circuit, etc. are combined or combined in a different form than the described method, or other components Or substituted or substituted by equivalents may achieve an appropriate result.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (8)

a collection processing unit that requests and collects public data for SNS user channels classified by accounts previously registered as an SNS (Social Network Service) server, or collects them by a crawling method;
an analysis unit that analyzes the collected public data to analyze an influence score indicating influence on the SNS user channel; and
Report generation unit for generating an influence report based on the analyzed influence score
Influence measurement system for SNS user channels, including. According to claim 1,
The collection processing unit,
SNS for collecting public data including at least one of profile information registered in the SNS (Social Network Service) server and posting information, channel information, image information, and image information by querying the SNS (Social Network Service) server Impact measurement system for user channels. According to claim 1,
The collection processing unit,
An influence measurement system for SNS user channels that collects public data related to the SNS user channels published online by the SNS (Social Network Service) server through crawling. 4. The method of claim 3,
The collection processing unit,
An influence measuring system for SNS user channels that collects the public data in a crawling manner using at least one of a virtual mobile simulator, a virtual hosting-multi-browser, and an API request. According to claim 1,
The report generating unit,
Based on the influence score, SNS account basic information, SNS account analysis related information, the actual active related account rate, the real response rate, the influence measurement system for the SNS user channel to generate an influence report that reflects at least one of the reach rate. Collecting public data on SNS user channels classified by previously registered accounts from a Social Network Service (SNS) server;
analyzing the collected public data to analyze an influence score indicating influence on the SNS user channel; and
Generating an influence report based on the analyzed influence score
An operating method of an influence measurement system for an SNS user channel comprising a. 7. The method of claim 6,
Collecting the public data includes:
Collecting public data including at least one of profile information registered in the SNS (Social Network Service) server and posting information, channel information, image information, and image information by querying the SNS (Social Network Service) server
An operating method of an influence measurement system for an SNS user channel comprising a. 7. The method of claim 6,
Collecting the public data includes:
Collecting public data related to the SNS user channel published online by the SNS (Social Network Service) server through crawling
An operating method of an influence measurement system for an SNS user channel comprising a.

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