KR101764696B1 - Method and System for determination of social network hot topic in consideration of user’s influence and time - Google Patents

Method and System for determination of social network hot topic in consideration of user’s influence and time Download PDF

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KR101764696B1
KR101764696B1 KR1020150136213A KR20150136213A KR101764696B1 KR 101764696 B1 KR101764696 B1 KR 101764696B1 KR 1020150136213 A KR1020150136213 A KR 1020150136213A KR 20150136213 A KR20150136213 A KR 20150136213A KR 101764696 B1 KR101764696 B1 KR 101764696B1
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user
hot topic
index
determined based
social network
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KR20170037709A (en
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유재수
복경수
노연우
김대윤
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충북대학교 산학협력단
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/01—Social networking
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/16—Combinations of two or more digital computers each having at least an arithmetic unit, a program unit and a register, e.g. for a simultaneous processing of several programs
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/30—Transportation; Communications

Abstract

A method and system for determining a social network hot topic considering user influence and time variation. A method for determining a hot topic in a social network service includes extracting a word based on a change in an appearance frequency of a plurality of words included in a plurality of social network contents according to a change in a time slot, Determining a hot topic index in each of a plurality of time slots of the extracted word based on an uploaded user's influence index and an appearance frequency in each of a plurality of time slots of the extracted word, Determining a hot topic index change rate of the extracted word in consideration of a change in each of the plurality of hot topic indexes, and determining whether to select the extracted word based on the hot topic index change rate as a hot topic.

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method and system for determining hot topics in a social network,

The present invention relates to a social network service method and system, and more particularly, to a social network hot topic determination method and system considering user influence and time change.

Recently, according to the development of SNS (Social Network Service), a lot of people use SNS through smart device or the web to post opinions and share information. SNS is a service to help users form a human network based on the Internet and manage their relationship with others through information sharing, network management, and self-expression. In the early days, SNS was mainly used for social networking, but then it was converted into a form of generating and consuming new information as well as information sharing through the relationship between users.

Also, since information shared by friends' recommendations is more reliable and concise than general web search, more people are searching for and using the latest information through SNS rather than general internet search. Accordingly, there is a demand for a technique for finding information that is a recent issue from a large amount of information that is reproduced and shared exponentially.

Twitter, Facebook, Line, Me2Day, and Google+ are some of the most popular SNSs, and Twitter is a rapidly growing service because it allows users to easily network with others on the Internet through a simple interface. In the case of Twitter, it has grown since 2006, with more than 300 million monthly real users and more than 500 million daily tweets. In addition, Twitter has a 140-character limit, so it is easy to find articles that are in real time. Twitter also communicates with a unique feature called follow, which follows the interested party. Also, even if you do not have direct access to the web, Twitter can upload or receive articles through various methods such as text messages on mobile phones or mobile devices such as smart phones, and can post comments and other articles to other users. Tweets are called tweets, and the ability to spread the tweets created by the user you follow to their followers is called retweet. Mention is the ability to send a tweet to a specific user.

KR 10-2006-0116551

One aspect of the present invention provides a method for determining a social network hot topic considering user influence and time variation.

Another aspect of the invention provides a social network hot topic determination system that takes into account user influence and time variations.

A method for determining a hot topic in a social network service according to an aspect of the present invention includes the steps of extracting a word based on a change in an appearance frequency according to a change in a time slot of a plurality of words included in a plurality of social network contents, Determining a hot topic index in each of a plurality of time slots of the extracted word based on an influence index of the user who uploaded the social network content including the word and an appearance frequency in each of the plurality of time slots of the extracted word Determining a hot topic index change rate of the extracted word in consideration of a change in each of the plurality of time slots of the hot topic index; and extracting the extracted word as a hot topic based on the hot topic index change rate And determining whether to select.

On the other hand, the change in the appearance frequency is determined based on the following equation,

≪ Equation &

Figure 112015093713069-pat00001

Here, idf i represents the idf value in the current time slot i, idf 0, i-1 represents the idf value of the time slot from 0 to i-1,

The idf value may be a reciprocal of the number of at least one social network content comprising each of the plurality of words of the plurality of social network content.

Further, the influence index is determined based on the follower element, the mentoring element, and the retriever element, and the follower element is determined based on the number of followers of the user, and the mentoring element is based on the number of mentions to the user And the retweet factor may be determined based on the number of retweits of the user's social network content and the number of followers of other users who have performed the retweeting.

Further, the influence index of the user is determined based on the follower element, the mentoring element, and the retweet element,

The follower element

Figure 112015093713069-pat00002
Is determined based on the following equation,

≪ Equation &

Figure 112015093713069-pat00003

here,

Figure 112015093713069-pat00004
Is the number of followers of the user,
Figure 112015093713069-pat00005
Is a weight,

The Rettwit element

Figure 112015093713069-pat00006
Is determined based on the following equation,

≪ Equation &

Figure 112015093713069-pat00007

here,

Figure 112015093713069-pat00008
Is the number of content that has been tweeted by the user,
Figure 112015093713069-pat00009
Is the number of retweits for the content that has been tweeted by the user,
Figure 112015093713069-pat00010
Is the number of users' followers,
Figure 112015093713069-pat00011
Is the average number of followers of said follower of said user,
Figure 112015093713069-pat00012
Is a weight,

The mentoring element

Figure 112015093713069-pat00013
Is determined based on the following equation,

≪ Equation &

Figure 112015093713069-pat00014

here,

Figure 112015093713069-pat00015
Is the number of mentions for the user,
Figure 112015093713069-pat00016
Can be a weight.

Also, the influence index of the user may be determined based on the following equation.

≪ Equation &

Figure 112015093713069-pat00017

In addition, the hot topic index may be determined based on a product of the influence index and the appearance frequency in each of the plurality of time slots of the extracted word.

In addition, the hot topic index change ratio is determined based on the following equation,

≪ Equation &

Figure 112015093713069-pat00018

here,

Figure 112015093713069-pat00019
Is a hot topic index at time t-1 of the extracted word,
Figure 112015093713069-pat00020
May be a hot topic index at time t of the extracted word.

A hot topic determination system for determining a hot topic determination in a social network service according to another aspect of the present invention includes a processor for determining a frequency of appearance of a plurality of words included in a plurality of social network contents Extracting a word based on a change of a word, extracting a word based on a change, extracting a plurality of extracted words based on an influence index of a user who uploads a social network content including the extracted word, and an appearance frequency in each of a plurality of time slots of the extracted word Determines a hot topic index change rate of each of the extracted words in consideration of a change in each of the plurality of time slots of the hot topic index, and determines the hot topic index change rate It is possible to determine whether or not to select the extracted word as a hot topic.

On the other hand, the change in the appearance frequency is determined based on the following equation,

≪ Equation &

*

Figure 112015093713069-pat00021

Here, idf i represents the idf value in the current time slot i, idf 0, i-1 represents the idf value of the time slot from 0 to i-1,

The idf value may be a reciprocal of the number of at least one social network content comprising each of the plurality of words of the plurality of social network content.

Further, the influence index is determined based on the follower element, the mentoring element, and the retriever element, and the follower element is determined based on the number of followers of the user, and the mentoring element is based on the number of mentions to the user And the retweet factor may be determined based on the number of retweits of the user's social network content and the number of followers of other users who have performed the retweeting.

Further, the influence index of the user is determined based on the follower element, the mentoring element, and the retweet element,

The follower element

Figure 112015093713069-pat00022
Is determined based on the following equation,

≪ Equation &

Figure 112015093713069-pat00023

here,

Figure 112015093713069-pat00024
Is the number of followers of the user,
Figure 112015093713069-pat00025
Is a weight,

The Rettwit element

Figure 112015093713069-pat00026
Is determined based on the following equation,

≪ Equation &

Figure 112015093713069-pat00027

here,

Figure 112015093713069-pat00028
Is the number of content that has been tweeted by the user,
Figure 112015093713069-pat00029
Is the number of retweits for the content that has been tweeted by the user,
Figure 112015093713069-pat00030
Is the number of users' followers,
Figure 112015093713069-pat00031
Is the average number of followers of said follower of said user,
Figure 112015093713069-pat00032
Is a weight,

The mentoring element

Figure 112015093713069-pat00033
Is determined based on the following equation,

≪ Equation &

Figure 112015093713069-pat00034

here,

Figure 112015093713069-pat00035
) Is the number of mentions for the user,
Figure 112015093713069-pat00036
Can be a weight.

Also, the influence index of the user may be determined based on the following equation.

≪ Equation &

Figure 112015093713069-pat00037

In addition, the hot topic index may be determined based on a product of the influence index and the appearance frequency in each of the plurality of time slots of the extracted word.

In addition, the hot topic index change ratio is determined based on the following equation,

≪ Equation &

Figure 112015093713069-pat00038

here,

Figure 112015093713069-pat00039
Is a hot topic index at time t-1 of the extracted word,
Figure 112015093713069-pat00040
May be a hot topic index at time t of the extracted word.

The method and system for determining a social network hot topic in consideration of user influence and time change according to an embodiment of the present invention can be implemented by considering a influence of a user who uploads a specific word on the SNS within the SNS and a frequency of occurrence of a specific word within the SNS In SNS, many users can accurately determine the hot topic they are interested in.

1 is a flowchart illustrating a hot topic selection method of a hot topic determination system according to an embodiment of the present invention.
2 is a conceptual diagram illustrating an IDF determination method according to an embodiment of the present invention.
FIG. 3 is a conceptual diagram illustrating a method for determining a user's influence index according to an embodiment of the present invention.
4 is a conceptual diagram illustrating a method for determining a hot topic based on a hot topic index change rate according to an embodiment of the present invention.
5 is a flowchart illustrating a hot topic index determination method according to an embodiment of the present invention.
6 is a conceptual diagram illustrating a hot topic determination system according to an embodiment of the present invention.

The following detailed description of the invention refers to the accompanying drawings, which illustrate, by way of illustration, specific embodiments in which the invention may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the invention. It should be understood that the various embodiments of the present invention are different, but need not be mutually exclusive. For example, certain features, structures, and characteristics described herein may be implemented in other embodiments without departing from the spirit and scope of the invention in connection with an embodiment. It is also to be understood that the position or arrangement of the individual components within each disclosed embodiment may be varied without departing from the spirit and scope of the invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is to be limited only by the appended claims, along with the full scope of equivalents to which such claims are entitled, if properly explained. In the drawings, like reference numerals refer to the same or similar functions throughout the several views.

Hereinafter, preferred embodiments of the present invention will be described in more detail with reference to the drawings.

Users can use social networks to express their status or share various information. It is a difficult task to find the information that is actually desired from such a large amount of generated social information, and it causes various problems in terms of efficiency. Recently, researches are being conducted to detect hot topic that is becoming an issue or a key topic in social networks.

In the present invention, a reliable hot topic determination method considering a user's influence in a social network environment is proposed. The hot topic determination method according to an embodiment of the present invention may include a method of determining whether a word (or a word or a word) that occurs instantaneously at a specific time using a modified TF (inverse document frequency) A hot topic index is derived by taking the frequency of occurrence of a word and the influence of a user into consideration, and a hot topic can be determined based on a change rate of a hot topic index, which is a change of a hot topic index over time have.

There is a high correlation between the user's influence and the reliability and efficiency of the detection results. Thus, when the user influence is given to the word as a weight, the accuracy and reliability of the hot topic detection result can be higher. In the SNS environment, there are various factors related to the reliability of the hot topic, but the influence of the user is most related to the reliability of the hot topic.

For the sake of convenience in the following description, terms such as tweet, retweet, follower, following, and mentions are used, but tweet acts to upload content to the SNS, The follower is the other user who receives his / her content on the SNS, the follower is another user (or the user) that the user wants to receive the content, and the mentor is the action of delivering the content only to the specific user on the SNS . ≪ / RTI >

Hereinafter, a method of determining a hot topic on a social network service considering a modified TF-IDF algorithm and a user's influence will be described in detail in an embodiment of the present invention.

1 is a flowchart illustrating a hot topic selection method of a hot topic determination system according to an embodiment of the present invention.

FIG. 1 illustrates a hot topic selection method for assigning a weight to a tweeted content based on a Twitter user influence.

Referring to FIG. 1, a set of words that occur instantaneously is extracted using the modified TF-IDF algorithm from the tweeted contents (step S100).

A hot topic can be defined as a set of words (or contents) within a content that is instantaneously referred to as a change in time. In order to determine a hot topic, the frequency of appearance of a word must first be considered. Considering the appearance frequency of the word, a set of words which are instantaneously issued can be extracted.

A modified TF-IDF algorithm can be used to calculate the frequency of word occurrences. In the conventional algorithm, the temporal property for extracting instantaneous words is ignored. In the hot topic selection method according to the embodiment of the present invention, a modified TF-IDF algorithm may be used in which a weight is given to a tweet to extract instantaneous generated words by modifying the existing TF-IDF algorithm.

In a modified TF-IDF algorithm according to an embodiment of the present invention, a term frequency (TF) can be determined based on a frequency-of-arrival scheme. The called frequency scheme can be a way of giving a 1 if the word w appears once in a tweet, or a 0 if it is not. IDF (inverse document frequency) can be calculated by measuring the amount of change in idf over time. idf may be the reciprocal of the number of the tweeted contents including the specific word that was tweeted in a specific time period (or a specific time slot period).

The following equation (1) is a formula for calculating the IDF.

&Quot; (1) "

*

Figure 112015093713069-pat00041

Referring to Equation (1), when a slot of a specific time is denoted by i, idf i represents the idf value in the current time slot i, and idf 0 and i-1 denote the idf value of the time slot from 0 to i-1.

IDF represents the amount of change of the idf value of the current time slot with respect to the past time slot, and the amount of change of the tweeted content (or document) including specific words instantaneously appearing based on the IDF is measured, A word having a possibility of being determined can be extracted.

The hot topic index for the extracted word is calculated by taking into account both the frequency of appearance of words included in the tweeted content and the influence of the user (step S110).

A hot topic index for the extracted word is determined and a hot topic among the extracted words can be determined in consideration of the determined hot topic index. The hot topic index for the extracted word based on the step S100 can be determined in consideration of the appearance frequency of the specific word as well as the influence of the user who created the tweet including the specific word. The larger the hot topic index for a particular word, the more likely it is that a particular word has been tweeted or retwitched by a relatively influential user, and that a relatively large number of people have tweeted / reattempted. A method for determining a specific user's influence index and hot topic index will be described later.

And determines a change (hot topic index change rate) of the hot topic index according to the word for each word (step S120).

The rate of change of the hot topic index, which is the amount of change in the hot topic index with respect to words over time, can be determined. The hot topic index change rate may indicate how the hot topic index for a particular word changes over time. A method for determining the specific hot topic index change rate will be described later.

The words are ranked based on the hot topic index change rate to determine N words as hot topics (step S130).

N words out of the extracted words based on the hot topic index change rate of the extracted words can be determined as hot topics. For example, N words can be determined to be hot topics in the order of the largest change in the hot topic index among the extracted words. A word determined as a hot topic may be recommended to the user.

2 is a conceptual diagram illustrating an IDF determination method according to an embodiment of the present invention.

In FIG. 2, a method for calculating an IDF for a specific word (for example, a time call, an infinite challenge) on a specific time slot section is disclosed.

Referring to the tweets per time slot disclosed in FIG. 2, idf 0, i-1 is the reciprocal of the number of tweeted contents in which a specific word appeared at 00:00, and idf i is the number of tweeted contents Is the reciprocal of.

For example, for the word 'infinite challenge', 'infinite challenge' appears once at 00:00, so idf 0, i-1 is the reciprocal value of 1, 1. Since 'Infinite Challenge' appears twice at 01, idf i is calculated as 1/2, the reciprocal value of 2. Therefore, the IDF value of the 'infinite challenge' may be 1/2 the value of idf i divided by idf 0, i-1. Based on the IDF, a word having a possibility of being determined as a hot topic can be extracted from a set of the entirely-tweeted contents, and a hot topic index for the extracted word can be determined.

Hereinafter, a method of calculating a user's influence for calculating a hot topic index is disclosed.

Tweets are registered with various users' tweets. Some of them are tweets by famous and influential users, and tweets by users who are not. When a hot topic is detected based only on the frequency of appearance of a specific word as in the conventional technique, the influence of each user can be ignored and the same weight can be given. If the influence of each user is ignored and the same weight is given, the reliability of the determined hot topic is not high. Thus, reliability of a hot topic can be improved by measuring a user's influence and giving a higher weight to a tweet registered by an influential user.

In an SNS environment, there may be a variety of factors associated with reliability of hot topics. Recently, the purpose of users to use SNS is to share and search information using contents uploaded by users. Thus, attention is paid to the content generated by a more influential user. Therefore, it can have a strong relation with the influence of the user and the reliability with respect to the hot topic, and the content that is overwritten by the influential user may be more likely to correspond to the hot topic.

According to the embodiment of the present invention, the influence of the user is determined based on three factors (the number of followers, the number of tweets, and the number of mentions) highly correlated with the influence of the user among the various activities that the user can perform on the tweeter .

FIG. 3 is a conceptual diagram illustrating a method for determining a user's influence index according to an embodiment of the present invention.

3, a method for determining a user's influence index for calculating a hot data index for a particular word is disclosed.

Referring to FIG. 3, to determine a user influence index 350, a user's influence index (e.g., the number of user's received follow-up, the number of user received memos, 350) can be determined.

The user's influence index 350 can be determined by adding log values for the number of followers, the number of mentions received by the user, and the number of retweits for the content that has been tweeted by the user. Equation (2) below is a formula for calculating the influence index 350 of the user.

&Quot; (2) "

Figure 112015093713069-pat00042

here,

Figure 112015093713069-pat00043
Is the influence index 350 of the user,
Figure 112015093713069-pat00044
The number of followers of the user (hereinafter referred to as follower element 310)
Figure 112015093713069-pat00045
The number of mentions received by the user (hereinafter referred to as mentoring element 320)
Figure 112015093713069-pat00046
Indicates the number of retweets (hereinafter referred to as retweet element 330) for the content that has been watched by the user.

Referring to Equation (2), the influence index 350 of the user can be calculated as a sum of all the log values for the follower element 310, the mentoring element 320 and the retrieve element 330, respectively. Since the elements are not related to each other, the influence value 350 of the user can be calculated by summing the log values for the respective elements and summing them. Since the distribution of each element represents the exponential distribution type, the log value is taken for each element so that the influence is not deviated for one element.

Each of the follower element 310, the mentoring element 320 and the retriever element 330 which determine the influence index of the user can be determined based on the following Equations 3 to 5.

The follower element 310 is associated with the number of followers of the user. The follower element 310 may indicate the degree of interest of other users' tweets of the user based on the number of users following the user. The follower element 310, which is an element for determining the influence index of the user, can be determined based on Equation (3) below.

&Quot; (3) "

Figure 112015093713069-pat00047

Referring to Equation (3), the follower element (310) determines the number of followers (Followers) of the user as a normalization constant

Figure 112015093713069-pat00048
As shown in FIG. Therefore, the greater the number of followers of a particular user, the more the user influence can be determined.

Equation (4) below represents a retriever element 330, which is an element that determines the influence index 350 of the user.

&Quot; (4) "

Figure 112015093713069-pat00049

Referring to Equation (4), as a component for deriving a user influence, a user's retweet element 330 may be calculated by taking an average retweet rate per tweets of a user and a spreading power of followers Can be determined. The retweet element 330 multiplies the ratio of the retweet to the total tweet of the user by the value of the follower's number of followers to the total number of followers,

Figure 112015093713069-pat00050
As shown in FIG. The larger the retweet element 330, the higher the user influence can be determined. The propagation power of the followers can be determined based on the average follower number of a specific user's followers. That is, the average follower number of the user's followers can be used to determine the retriever element 330 of the user. Accordingly, when a particular user uploads a tweet, on average, how many users are contacted will be determined based on the retriever 330.

Equation (5) below may represent a mentor element 320, which is an element that determines the influence index 350 of the user.

Equation (5)

Figure 112015093713069-pat00051

A high number of users receiving a mention indicates that the user is interested in another user. The mentoring element 320 may include a user's total number of mentions as a normalization constant

Figure 112015093713069-pat00052
As shown in FIG. Thus, as with the follower element, the larger the mentoring element 320, the more likely the user will be influenced.
Figure 112015093713069-pat00053
Are normalization constants for normalizing each element.

The following is an example of how to determine the influence index 350 of a specific user.

Normalization constant

Figure 112015093713069-pat00054
Is 1000, and the number of followers (100), the number of tweets (150), the number of retries (300), the number of followers (2000) Each element, mentement element, and rettowel element can be determined as shown in Equations (6), (7) and (8) below.

Equation (6) represents the user's follower element (310).

&Quot; (6) "

Figure 112015093713069-pat00055

Referring to Equation (6), the total number of followers is 100

Figure 112015093713069-pat00056
0.0 > 310 < / RTI > of the user.

Equation 7 represents the retriever element 330 of the user.

&Quot; (7) "

Figure 112015093713069-pat00057

Referring to Equation (7), the value obtained by dividing the total number of retweets of 300 for the total number of tweets by 300 is multiplied by the number of followers of the user's followers for the total number of followers of 100 divided by 2000,

Figure 112015093713069-pat00058
And 0.4 is determined as the upper limit element 330. [

Equation (8) represents the mentoring element 320 of the user.

&Quot; (8) "

Figure 112015093713069-pat00059

Referring to Equation (8), the total number of mentions, 100,

Figure 112015093713069-pat00060
(0.1) is determined as the mentoring element (320).

Based on the user's influence index 350 determined based on the user's follower element 310, retweet element 330 and mentoring element 320 and the number of occurrences of a specific word in a specific time slot interval, A hot topic index can be determined. At this time, the specific word for which the hot topic index is determined may be a word selected (or extracted) based on the modified TF-IDF algorithm described above. For example, based on the TF-IDF algorithm, a hot topic index 360 can be calculated for words that exceed a threshold TF and / or a threshold IDF. Specifically, the word-by-word hot topic index can be determined based on the user's influence index 350 for the word and the count number 355 of the word.

Finally, the change rate of the hot topic index 360 over time (hereinafter, the hot topic index change rate 370) is determined, and the hot topic can be determined according to the hot topic index change rate 370.

Equation (9) below is a mathematical expression for determining the hot topic index change ratio 370.

&Quot; (9) "

Figure 112015093713069-pat00061

Referring to Equation 9, the hot topic index change rate for word w can be determined as a ratio of the difference between the hot topic index at time t and the hot topic index at time t-1 for word w.

That is, in the social network hot topic determination method and system considering user influence and time change according to the embodiment of the present invention, not only the occurrence frequency of a specific word but also the influence 350 of a user who created a tweet including the corresponding word is assigned as a weight A hot topic can be determined by considering the change ratio 370 of the hot topic index over time after determining the total sum as a hot topic index 360 of a specific word.

4 is a conceptual diagram illustrating a method for determining a hot topic based on a hot topic index change rate according to an embodiment of the present invention.

In FIG. 4, a hot topic index for a specific word is determined based on an influence index for each user and the occurrence frequency of words, and a method for determining a hot topic index change rate according to a hot topic index is disclosed.

Referring to FIG. 4, the tweet data and the influence of each user over time are disclosed. The word 'infinite challenge' that appears in the tweet at 00 counts 1 as the number of occurrences of the word. Since the user influence index of the tweet is 0.6, 0.6 multiplied by the number of occurrences of the user and the influence index of the user is determined as the hot topic index for the 'Infinite Challenge'.

In the second tweet at 00:00, the word "seoyukho" is counted as 1 word count. Since the user influence index of the tweet is 0.4, 0.4, which is multiplied by the user's influence index and the number of occurrences of a certain word, is determined as the hot topic index for the 'time call'.

After all exponential values of the words are calculated, the rate of change of the hot topic index over time is determined. In the case of 'Infinite Challenge', the hot topic index change ratio (-0.5) is determined by the difference of the sum based on the hot topic index of 01:00 and the hot topic index of 0.6 at 00:00. , The hot topic index change ratio (0.6) is determined by the sum of the sum based on the hot topic index of 1.6 at 01:00 and the hot topic index of 0.4 at 00:00. The larger the ratio, the change in the hot topic index over time, may be closer to the hot topic. In other words, 'Seowall' may be relatively hot topic rather than 'Infinite Challenge'.

N words having a high hot topic index based on the hot topic index change rate for a plurality of words determined in the above manner can be determined as hot topics.

The hot topic determination system may provide the determined N words to the user as a hot topic.

5 is a flowchart illustrating a hot topic index determination method according to an embodiment of the present invention.

Referring to FIG. 5, an influential index for each user is determined in consideration of a user's follower element, retweet element, and mentation element (step S500).

As described above, the influence index of the user can be determined in consideration of the number of followers, the number of tweets, the number of followers, the number of followers of the followers, the number of mentions, and the like, as shown in Equations 2 to 5 above.

A hot topic index is determined for each word (or keyword) based on the word occurrence frequency and the user influence (step S510).

A hot topic index for a specific word can be determined based on the appearance frequency of a specific word generated in a specific time slot and the influence of the user determined based on step S500.

A hot topic index change ratio with time is determined (step S520)

A hot topic index for a specific word is determined based on step S510, and a hot topic index change rate according to a change of time as described in Equation (9) can be determined.

6 is a conceptual diagram illustrating a hot topic determination system according to an embodiment of the present invention.

6, the hot topic determination system includes a TF-IDF unit 600, a user influence index determination unit 610, a hot topic index determination unit 620, a hot topic index change rate determination unit 630, and a processor 640 ). Each component can be implemented to perform the hot topic determination operation disclosed in Figs. 1 to 5 described above. For example, each of the TF-IDF unit 600, the user influence index determining unit 610, the hot topic index determining unit 620, the hot topic index change rate determining unit 630, and the processor 640, Can be performed.

The TF-IDF unit 600 may be implemented to determine the amount of change of the content (or document) that is included in the tweeted content including the frequency of appearance of a specific word and specific words that appear instantaneously based on the frequency.

The user influence index determiner 610 may be implemented to determine a user's influence index based on each of the follower element, the mentoring element, and the retriever element.

The hot topic exponent determiner 620 may be implemented to determine a hot topic exponent for a particular word based on a user's influence index and the frequency of occurrence of a particular word.

The hot topic index change rate determination unit 630 may be implemented to determine a hot topic index change rate for a specific word.

The processor 640 is implemented to control the operations of the TF-IDF unit 600, the user influence index determination unit 610, the hot topic index determination unit 620, and the hot topic index change rate determination unit 630, respectively .

Such a social network hot topic determination method considering user influence and time change may be implemented in an application or may be implemented in the form of program instructions that can be executed through various computer components and recorded in a computer-readable recording medium. The computer-readable recording medium may include program commands, data files, data structures, and the like, alone or in combination.

The program instructions recorded on the computer-readable recording medium may be ones that are specially designed and configured for the present invention and are known and available to those skilled in the art of computer software.

Examples of computer-readable recording media include magnetic media such as hard disks, floppy disks and magnetic tape, optical recording media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like.

Examples of program instructions include machine language code such as those generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware device may be configured to operate as one or more software modules for performing the processing according to the present invention, and vice versa.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it will be understood by those skilled in the art that various changes and modifications may be made therein without departing from the spirit and scope of the invention as defined in the appended claims. It will be possible.

Claims (14)

Hot topic determination methods in social network services,
Extracting a word based on a change in appearance frequency according to a change in a time slot of a plurality of words included in a plurality of social network contents;
A hot topic index in each of a plurality of time slots of the extracted word based on an influence index of a user uploading a social network content including the extracted word and an appearance frequency in each of a plurality of time slots of the extracted word ;
Determining a hot topic index change rate of the extracted word in consideration of a change in each of the plurality of time slots of the hot topic index; And
Determining whether to select the extracted word as a hot topic based on the hot topic index change rate
Lt; / RTI >
The change in the appearance frequency is determined based on the following equation,
≪ Equation &
Figure 112016064033816-pat00108

Where idf i represents the idf value in the current time slot i, idf 0, i-1 represents the idf value of the time slot from 0 to i-1, and the idf value represents the idle value of the plurality of words The reciprocal of the number of at least one social network content comprising each < RTI ID = 0.0 >
How to determine hot topics in social network services. delete The method according to claim 1,
The influence index is determined based on the follower element, the mentoring element, and the retriever element,
Wherein the follower element is determined based on a number of the followers of the user,
Wherein the mentoring element is determined based on the number of mentions to the user,
Wherein the retweet factor is determined based on the number of retweits of the user for the social network content and the number of followers of other users who have performed the retweeting. The method according to claim 1,
The influence index of the user is determined based on the follower element, the mentoring element, and the retriever element,
The follower element
Figure 112016064033816-pat00063
Is determined based on the following equation,
≪ Equation &
Figure 112016064033816-pat00064

here,
Figure 112016064033816-pat00065
Is the number of followers of the user,
Figure 112016064033816-pat00066
Is a weight,
The Rettwit element
Figure 112016064033816-pat00067
Is determined based on the following equation,
≪ Equation &
Figure 112016064033816-pat00068

here,
Figure 112016064033816-pat00069
Is the number of content that has been tweeted by the user,
Figure 112016064033816-pat00070
Is the number of retweits for the content that has been tweeted by the user,
Figure 112016064033816-pat00071
Is the number of users' followers,
Figure 112016064033816-pat00072
Is the average number of followers of said follower of said user,
Figure 112016064033816-pat00073
Is a weight,
The mentoring element
Figure 112016064033816-pat00074
Is determined based on the following equation,
≪ Equation &
Figure 112016064033816-pat00075

here,
Figure 112016064033816-pat00076
Is the number of mentions for the user,
Figure 112016064033816-pat00077
Is a weighted value in the social network service. 5. The method of claim 4,
And the influence index of the user is determined based on the following equation
≪ Equation &
Figure 112015093713069-pat00078

How to determine hot topics in social network services. 6. The method of claim 5,
Wherein the hot topic index is determined based on a product of the influence index and the appearance frequency in each of the plurality of time slots of the extracted word. The method according to claim 6,
The hot topic index change ratio is determined based on the following equation,
≪ Equation &
Figure 112015093713069-pat00079

here,
Figure 112015093713069-pat00080
Is a hot topic index at time t-1 of the extracted word,
Figure 112015093713069-pat00081
Is a hot topic index at time t of the extracted word. A hot topic determination system for determining a hot topic decision in a social network service,
Wherein the hot topic determination system comprises a processor,
The processor extracts a word based on a change in an occurrence frequency according to a change of a time slot of a plurality of words included in a plurality of social network contents,
A hot topic index in each of a plurality of time slots of the extracted word based on an influence index of a user uploading a social network content including the extracted word and an appearance frequency in each of a plurality of time slots of the extracted word Lt; / RTI >
Determining a hot topic index change rate of the extracted word in consideration of a change in each of the plurality of time slots of the hot topic index,
And to determine whether to select the extracted word as a hot topic based on the hot topic index change rate,
The change in the appearance frequency is determined based on the following equation,
≪ Equation &
Figure 112017006425267-pat00109

Where idf i represents the idf value in the current time slot i, idf 0, i-1 represents the idf value of the time slot from 0 to i-1, and the idf value represents the idle value of the plurality of words Wherein the at least one social network content is a reciprocal of the number of the at least one social network content. delete 9. The method of claim 8,
The influence index is determined based on the follower element, the mentoring element, and the retriever element,
Wherein the follower element is determined based on a number of the followers of the user,
Wherein the mentoring element is determined based on the number of mentions to the user,
Wherein the retweet factor is determined based on a number of retweits of the user's social network content and a number of followers of another user who performed retweeting. 9. The method of claim 8,
The influence index of the user is determined based on the follower element, the mentoring element, and the retriever element,
The follower element
Figure 112016064033816-pat00083
Is determined based on the following equation,
≪ Equation &
Figure 112016064033816-pat00084

here,
Figure 112016064033816-pat00085
Is the number of followers of the user,
Figure 112016064033816-pat00086
Is a weight,
The Rettwit element
Figure 112016064033816-pat00087
Is determined based on the following equation,
≪ Equation &
Figure 112016064033816-pat00088

here,
Figure 112016064033816-pat00089
Is the number of content that has been tweeted by the user,
Figure 112016064033816-pat00090
Is the number of retweits for the content that has been tweeted by the user,
Figure 112016064033816-pat00091
Is the number of users' followers,
Figure 112016064033816-pat00092
Is the average number of followers of said follower of said user,
Figure 112016064033816-pat00093
Is a weight,
The mentoring element
Figure 112016064033816-pat00094
Is determined based on the following equation,
≪ Equation &
Figure 112016064033816-pat00095

here,
Figure 112016064033816-pat00096
Is the number of mentions for the user,
Figure 112016064033816-pat00097
Is a weighted value. 12. The method of claim 11,
And the influence index of the user is determined based on the following equation
≪ Equation &
Figure 112015093713069-pat00098

Hot topic determination system in social network services. 13. The method of claim 12,
Wherein the hot topic index is determined based on a product of the influence index and the appearance frequency in each of the plurality of time slots of the extracted word. 14. The method of claim 13,
The hot topic index change ratio is determined based on the following equation,
≪ Equation &
Figure 112015093713069-pat00099

here,
Figure 112015093713069-pat00100
Is a hot topic index at time t-1 of the extracted word,
Figure 112015093713069-pat00101
Is a hot topic index at a time t of the extracted word.
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