WO2023148947A1 - Dispositif d'évaluation, procédé d'évaluation et programme - Google Patents

Dispositif d'évaluation, procédé d'évaluation et programme Download PDF

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Publication number
WO2023148947A1
WO2023148947A1 PCT/JP2022/004576 JP2022004576W WO2023148947A1 WO 2023148947 A1 WO2023148947 A1 WO 2023148947A1 JP 2022004576 W JP2022004576 W JP 2022004576W WO 2023148947 A1 WO2023148947 A1 WO 2023148947A1
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Prior art keywords
user
evaluated
evaluation
post
evaluation device
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PCT/JP2022/004576
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English (en)
Japanese (ja)
Inventor
啓太 鈴木
篤 中平
盛徳 大橋
穂乃香 戸田
志高 土屋
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日本電信電話株式会社
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Priority to PCT/JP2022/004576 priority Critical patent/WO2023148947A1/fr
Publication of WO2023148947A1 publication Critical patent/WO2023148947A1/fr

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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—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism

Definitions

  • the present invention relates to a device, method, and program for evaluating the reliability of any person.
  • Non-Patent Document 1 is known as a conventional technique for evaluating the reliability of information on social media.
  • a classifier that classifies whether or not a post is reliable is created in advance, posts to be classified are analyzed, and from there, user posts, repost behavior, posted sentences, and external sources are analyzed. Citations are extracted as features, and the extracted features are given to a classifier to classify posts into reliable and unreliable posts.
  • Non-Patent Document 2 users who have posted certain information are classified into users who are oriented to disseminate information to the general public and users who mainly use tools for communication within the community, Based on the classification results, evaluate whether the information is widely useful information.
  • Non-Patent Document 1 training posts used when creating a classifier are manually evaluated, and human evaluation is performed indirectly when classifying posts to be classified. ing.
  • An object of the present invention is to provide an evaluation device, an evaluation method, and a program for evaluating a user's reliability from information whose reliability has been evaluated.
  • an evaluation device evaluates the credibility of users of social media.
  • the evaluation device defines a user to be evaluated as an evaluated user, information identifying the evaluated user as an evaluated user identifier, a user followed by the evaluated user as a primary follow user, and an evaluated user identifier and 1
  • a follow user collection unit that acquires a list of next follow users, a post information collection unit that acquires posted information of rated users and posted information of primary follow users, evaluates posts of primary follow users, a confidence value calculation unit that evaluates the evaluated user based on the evaluation.
  • the present invention it is possible to evaluate the user's reliability from information whose reliability has been evaluated.
  • the evaluation result can be used for personnel matching and the like.
  • FIG. 4 is a diagram for explaining a method of evaluating user reliability from information
  • FIG. 2 is a functional block diagram of the evaluation device according to the first embodiment
  • FIG. 5 is a diagram showing an example of data stored in a follow user storage unit
  • FIG. 4 is a diagram showing an example of data stored in a posted information storage unit
  • FIG. 4 is a diagram for explaining a processing flow of a reliability value calculation unit
  • FIG. 4 is a diagram showing an example of data stored in a reliability value storage unit
  • Social media A general term for various information exchange services formed by individuals transmitting information using Internet technology. Social media users can post and view posts. For example, twitter (registered trademark) is known as a service that provides social media.
  • Posting means publishing information such as sentences, images, videos, etc., or information that has been made public, at a designated place on the Internet. For example, posts include tweets on twitter.
  • Profile Information indicating characteristics of the user.
  • users register their own profiles.
  • Post evaluation value is a value representing the user's evaluation of a certain post. For example, if there is a function in social media for each user to indicate positive intentions (like, fun, instructable, interested, etc.) for a certain post, the number of users who indicated positive intentions is Post rating value. For example, the number of "likes" for a certain tweet on twitter corresponds to the evaluation value.
  • the number of reactions represents the total number of posts (replies) to a certain post, posts quoting a certain post, and the like. For example, the total number of replies and retweets to a certain tweet on twitter corresponds to the number of reactions.
  • social media users users to be evaluated, hereinafter also referred to as "evaluated users" post by follower B of user A are calculated as follows:
  • the number of follower users of follow user B (number of followers), the number of interactions between evaluated user A and follow user B, the content of posts, the time of posting, and the like are used for evaluation.
  • the following users of the evaluated user are also referred to as primary following users, and the following users of the primary following user are also referred to as secondary following users.
  • the interaction means a series of exchanges between the rated user A and the follow user B in response to a post by the rated user A or the follow user B.
  • the number of interactions means the number of posts included in a certain interaction. For example, when follower user B makes a post and rated user A posts (replies) to the post, the number of interactions is two. Furthermore, when evaluating user A, in addition to information viewed by evaluated user A (posts of primary follow users), the degree of involvement and matching of preferences between evaluated user A and primary follow users take degrees into account.
  • FIG. 3 is a functional block diagram of the evaluation device according to the first embodiment, and FIG. 4 shows its processing flow.
  • the evaluation device includes a follow user collection unit 110, a follow user storage unit 115, a post information collection unit 120, a post information storage unit 125, a confidence value calculation unit 130, a confidence value storage unit 135, and an output unit 140. including.
  • the evaluation device receives information identifying the evaluated user (hereinafter also referred to as "evaluated user identifier") and evaluates the evaluated user.
  • the evaluation device receives a plurality of evaluated user identifiers, evaluates the plurality of evaluated users, and stores the evaluated user identifiers and the evaluation results.
  • a plurality of user identifiers to be evaluated may be given by the administrator of the evaluation device or the like.
  • a plurality of rated user identifiers may be obtained by using the user as the rated user.
  • the evaluation device outputs evaluation results corresponding to the input evaluated user identifier.
  • the follow user collection unit 110 receives a plurality of evaluated user identifiers, acquires the evaluated user identifiers and a list of primary follow users from the server 90 that provides social media (S110), and stores them in the follow user storage unit 115. .
  • the list of primary follow users includes information for identifying primary follow users (hereinafter also referred to as "primary follow user identifier").
  • FIG. 5 shows an example of data stored in the follow user storage unit 115. As shown in FIG. In FIG. In FIG.
  • the identifier used in the evaluation apparatus is given to the evaluated user identifier used in the server 90, but this does not necessarily have to be given.
  • a UID User Identifier
  • a URI Uniform Resource Identifier
  • the posted information collection unit 120 extracts the rated user identifier and the list of primary follow users from the follow user storage unit 115, and acquires the posted information of the rated user and the posted information of the primary follow user from the server 90 ( S120).
  • Posted information is information related to a post, and is information that can be obtained from the server 90 .
  • the posted information collection unit 120 extracts or generates the following information from the posted information of the rated user and the posted information of the primary follow user, and stores the information in the posted information storage unit 125 .
  • the posted information collection unit 120 may extract or generate the following information and store it in the posted information storage unit 125 .
  • FIG. 6 shows an example of data stored in the posted information storage unit 125 .
  • the post information collection unit 120 generates a record for each post by the primary follow user, and stores the record in the post information storage unit 125 .
  • posted content of the rated user is a post related to the "Post of the 1st follow user” of the record. Posts that are replied by “posts”, posts that quote “posts of the primary follow user”, posts that are quoted by “posts of the primary follow user”, and the like. Furthermore, the posted information collection unit 120 extracts “posts in which the rated user has indicated positive intentions” from the posted information of the rated user, and stores them in the posted information storage unit 125 in association with the rated user identifier. do. However, "posts in which the evaluated user has indicated a positive intention” are not illustrated.
  • the confidence value calculation unit 130 evaluates the post of the rated user's primary follow user, evaluates the rated user based on this evaluation (S130), and stores the evaluation value of the rated user in the confidence value storage unit 135.
  • the rated user can be evaluated based on the information surrounding the rated user.
  • the confidence value calculation unit 130 updates the evaluation of the post by the primary follow user of the rated user, taking into consideration the categories that are assumed to be of interest to the rated user and the primary follow user.
  • the reliability value calculation unit 130 updates the evaluation of the post by the primary follow user of the rated user, taking into consideration the posting time of the primary follow user.
  • FIG. 7 is a diagram for explaining the processing flow of the reliability value calculation unit 130.
  • the reliability value calculation unit 130 evaluates the reliability of a post by a primary follow user of an evaluated user through the following process.
  • the confidence value calculation unit 130 retrieves a record corresponding to the post S p (q p ) of the primary follow user from the post information storage unit 125 . Included in it ⁇ The evaluation value B(S p (q p )) of the post S p (q p ) of the first follower user ⁇ Reaction value C(S p (q p )) of post S p (q p ) of primary follower user ⁇ The number of followers D(p) of the primary follower user is used, the reliability evaluation value A(S p (q p )) of the post S p (q p ) of the primary follower user is obtained by the following equation.
  • A(S p (q p )) (b ⁇ B(S p (q p ))+c ⁇ C(S p (q p )))/D(p)
  • b and c are weights for the evaluation value B (S p (q p )) and the reaction value C (S p (q p )), respectively, and are calculated by simulation or the like prior to evaluation.
  • the confidence value calculation unit 130 calculates information indicating whether or not the URL is included in the post S p (q p ), which is included in the record corresponding to the post S p (q p ), and the post S p (q p ).
  • the evaluation value A(S p (q p )) is updated.
  • ⁇ is a constant greater than 1, and is calculated by simulation or the like prior to evaluation.
  • Category Classification Confidence Value Calculation Unit 130 retrieves from post information storage unit 125 posts that the evaluated user has indicated positive intentions (like, fun, can instruct, is interested in, etc.), and posts them. Included in the record corresponding to S p (q p ) ⁇ Posted content of the 1st follower user ⁇ Profile of the 1st follower user ⁇ Posted content of the rated user ⁇ Profile of the rated user The category of the post indicating the intention is classified, and the classified result is linked to the post and the profile and stored in the posted information storage unit 125 . Prior to the evaluation, a classifier for category classification may be prepared in a server or the like and obtained from the server or the like.
  • Categories may include various categories such as careers, games, sports, business, computer technology, and so on. A single post or profile may be classified into multiple categories.
  • the confidence value calculation unit 130 calculates the contents of the post S p (q p ) included in the record corresponding to the post S p (q p ), the post content of the primary follow user and its category classification result, and the evaluated user and the number of interactions with first-order follow users, the coefficient ⁇ is set so that the greater the number of interactions with posts in a certain category, the larger the coefficient ⁇ .
  • the coefficient ⁇ for category A is set to be large.
  • the coefficient ⁇ for each primary follow user p may be set for each primary follow user p and for each category, or different R coefficients ⁇ may be set for the top R categories for each primary follow user p. .
  • ⁇ is a constant greater than 1, and is calculated by simulation or the like prior to evaluation.
  • the confidence value calculation unit 130 uses the contents of posts by the rated user and their categorization results and the profile of the rated user and their categorization results, which are included in the record corresponding to the post S p (q p ), to A category in which the evaluated user is highly interested is obtained from the classification result. If the category in which the rated user is highly interested and the category of the post S p (q p ) are the same, the number of interactions related to the post S p (q p ) is multiplied by a constant, and the constant multiplied number of interactions is used to calculate the coefficient ⁇ set.
  • Confidence value calculation unit 130 extracts from post information storage unit 125 posts that the evaluated user has indicated positive intentions for, and their classification results, and sets coefficient ⁇ to increase as the number of posts in each category increases.
  • the confidence value calculation unit 130 updates the evaluation value A(S p (q p )) of the post S p (q p ) to one that considers the category, using the following equation.
  • the reliability value calculation unit 130 uses the posting time of the primary follow user included in the record corresponding to the post S p (q p ) to calculate the posting time
  • the confidence value calculation unit 130 updates the evaluation value A(S p (q p )) of the post S p (q p ) to one that considers the category and the posting time using the following equation.
  • Evaluated User's Evaluation Confidence Value Calculation Unit 130 calculates the sum of evaluation values A(S p (q p )) of all posts by all first-order follow users for an evaluated user. and the total number of posts by all first-order follow users for a given rated user. to obtain the evaluation value of the reliability of a certain evaluated user, and the combination of the evaluated user identifier and the evaluation value is recorded in the reliability value storage unit 135 . Furthermore, categories that are presumed to be of interest to the rated user may be stored together.
  • FIG. 8 shows an example of data stored in the reliability value storage unit 135. As shown in FIG. By storing together the categories that the evaluating user is presumed to be interested in, it becomes clear in which category the reliability value of the evaluated user indicates the reliability value.
  • the output unit 140 receives the evaluated user identifier, acquires the evaluation value corresponding to the evaluated user identifier from the reliability value storage unit 135, and outputs it (S140).
  • the user of the evaluation device wants to obtain an evaluation of the reliability of an evaluated user
  • the user inputs the identifier of the user to be evaluated into the evaluation device, and the evaluation device outputs the evaluation result.
  • the evaluation device may be configured to evaluate the user to be evaluated, that is, to perform S110, S120, and S130 each time the user of the evaluation device inputs the identifier of the user to be evaluated, and output the evaluation result.
  • the evaluation device does not include the confidence value storage unit 135 and the output unit 140, the follow user collection unit 110 performs the processing S110 based on the input evaluated user identifier, and the output of the confidence value calculation unit 130 is output to the evaluation device. is the output of
  • the confidence value calculation unit 130 may not consider whether or not the post S p (q p ) includes a URL or an image.
  • A(S p (q p )) ⁇ A(S p (q p )) omits the process of updating the evaluation value A(S p (q p )).
  • the confidence value calculation unit 130 may not consider the category, in which case A(S p (q p )) ⁇ A(S p (q p )) omits the process of updating the evaluation value A(S p (q p )). Therefore, processing S130-2 and S130-3 can be omitted. Furthermore, the processing related to only one of ⁇ and ⁇ may be omitted.
  • the confidence value calculation unit 130 may not consider the posting time, in which case A(S p (q p )) ⁇ A(S p (q p )) omits the process of updating the evaluation value A(S p (q p )). Therefore, processing S130-4 can be omitted.
  • the evaluation values are designed so that the higher the evaluation value, the higher the evaluation of the post and the user being evaluated.
  • An evaluation value may be designed. In short, any index may be used as long as it is possible to determine whether the evaluation is high or low based on the magnitude of the evaluation value.
  • the evaluation device may output an evaluated user identifier with a high reliability value for the input category.
  • the output unit 140 receives information specifying a category, acquires from the reliability value storage unit 135, and outputs the evaluated user identifiers for the top R evaluation values in the category.
  • the present invention is not limited to the above embodiments and modifications.
  • the various types of processing described above may not only be executed in chronological order according to the description, but may also be executed in parallel or individually according to the processing capacity of the device that executes the processing or as necessary.
  • appropriate modifications are possible without departing from the gist of the present invention.
  • a program that describes this process can be recorded on a computer-readable recording medium.
  • Any computer-readable recording medium may be used, for example, a magnetic recording device, an optical disk, a magneto-optical recording medium, a semiconductor memory, or the like.
  • this program is carried out, for example, by selling, transferring, lending, etc. portable recording media such as DVDs and CD-ROMs on which the program is recorded.
  • the program may be distributed by storing the program in the storage device of the server computer and transferring the program from the server computer to other computers via the network.
  • a computer that executes such a program for example, first stores the program recorded on a portable recording medium or the program transferred from the server computer once in its own storage device. Then, when executing the process, this computer reads the program stored in its own recording medium and executes the process according to the read program. Also, as another execution form of this program, the computer may read the program directly from a portable recording medium and execute processing according to the program, and the program is transferred from the server computer to this computer. Each time, the processing according to the received program may be executed sequentially. In addition, the above-mentioned processing is executed by a so-called ASP (Application Service Provider) type service, which does not transfer the program from the server computer to this computer, and realizes the processing function only by its execution instruction and result acquisition. may be It should be noted that the program in this embodiment includes information used for processing by a computer and conforming to the program (data that is not a direct command to the computer but has the property of prescribing the processing of the computer, etc.).
  • ASP Application Service Provide
  • the device is configured by executing a predetermined program on a computer, but at least part of these processing contents may be implemented by hardware.

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Abstract

La présente invention concerne un dispositif d'évaluation et analogues pour évaluer la fiabilité d'un utilisateur à partir d'informations évaluées en ce qui concerne la fiabilité. Le dispositif d'évaluation évalue la fiabilité d'un utilisateur de média social. Si on appelle « utilisateur à évaluer » un utilisateur soumis à une évaluation, « identifiant d'utilisateur à évaluer » des informations identifiant l'utilisateur à évaluer, et « utilisateurs suivis primaires » des utilisateurs suivis par l'utilisateur à évaluer, le dispositif d'évaluation comprend une unité de collecte d'utilisateurs suivis qui obtient l'identifiant d'utilisateur à évaluer et une liste d'utilisateurs suivis primaires, une unité de collecte d'informations de publication qui obtient des informations sur des publications effectuées par l'utilisateur à évaluer et des informations sur des publications effectuées par les utilisateurs suivis primaires, et une unité de calcul de valeur de fiabilité qui évalue des publications effectuées par les utilisateurs suivis primaires et utilise cette évaluation comme base pour l'évaluation de l'utilisateur à évaluer.
PCT/JP2022/004576 2022-02-07 2022-02-07 Dispositif d'évaluation, procédé d'évaluation et programme WO2023148947A1 (fr)

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Citations (3)

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Publication number Priority date Publication date Assignee Title
JP2007241983A (ja) * 2006-03-07 2007-09-20 Opinity Ap Inc ユーザの評判スコアを提供するレビュースコアリング方法およびシステム
JP2013084253A (ja) * 2011-10-11 2013-05-09 Tata Consultancy Services Ltd ソーシャルプラットフォームのコンテンツ品質及び利用者契約
JP2020035022A (ja) * 2018-08-27 2020-03-05 日本電信電話株式会社 評価更新装置、方法、及びプログラム

Patent Citations (3)

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Publication number Priority date Publication date Assignee Title
JP2007241983A (ja) * 2006-03-07 2007-09-20 Opinity Ap Inc ユーザの評判スコアを提供するレビュースコアリング方法およびシステム
JP2013084253A (ja) * 2011-10-11 2013-05-09 Tata Consultancy Services Ltd ソーシャルプラットフォームのコンテンツ品質及び利用者契約
JP2020035022A (ja) * 2018-08-27 2020-03-05 日本電信電話株式会社 評価更新装置、方法、及びプログラム

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HONOKA TODA, SHIGERU FUJIMURA, ATSUSHI NAKAHIRA, JUN SAGADA: "1I3-GS-4b-02: A study on visualizing the influence of individuals belonging to a group", THE 35TH NATIONAL CONFERENCE OF THE SOCIETY FOR ARTIFICIAL INTELLIGENCE; JUNE 8TH-11TH, 2021, JAPANESE SOCIETY FOR ARTIFICIAL INTELLIGENCE, JP, 31 December 2021 (2021-12-31) - 11 June 2021 (2021-06-11), JP, pages 1 - 2, XP009548389, DOI: 10.11517/pjsai.JSAI2021.0_1I3GS4b02 *
RYUTARO USHIGOME, TAKESHI MATSUDA, MICHIO SONODA, TAKESHI TAKAHASHI, MIO SUZUKI, JINHUI CHAO: "A consideration on the possibility of automatic classifying for anomalous posts on Twitter", IEICE TECHNICAL REPORT, IEICE, JP, vol. 118, no. 108 (ICSS2018-9), 18 June 2018 (2018-06-18), JP , pages 55 - 60, XP009548307, ISSN: 2432-6380 *

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