Information processing program, information processing apparatus, and information processing system

Abstract

To carry out output based on reliability of web articles.SOLUTION: An Information processing apparatus 10 according to the present invention has a control unit 11. The control unit 11 acquires web articles D1, D2, D3 through retrieval based on an input keyword and also acquires additional information ed1, ed2, ed3 allowing verification of reliability of the web articles D1, D2, D3 associated with the web articles D1, D2, D3. The control unit 11 detects sympathy numbers N1, N2, N3 as the number of sympathy added to the additional information ed1, ed2, ed3 to calculate evaluation values V1, V2, V3 of reliability of the web articles D1, D2, D3 from the sympathy numbers N1, N2, N3. Then, the control unit 11 arranges the web articles D1, D2, D3 in an order based on the evaluation values V1, V2, V3.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an information processing program, an information processing device, and an information processing system. [Background technology]

[0002] Using an Internet search engine, users enter keywords that indicate the subject they wish to search for on the search screen, and obtain data (web articles) published on the web as search results.

[0003] Related technologies include, for example, a technology that determines the reliability coefficient of content by receiving input about the quality of the content from users other than the author of the content for content that is publicly available online.Also, a technology has been proposed that determines the translator to be ordered based on an evaluation score that is weighted according to the degree of similarity between the identity information of a new translation requester and the identity information of previous translation requesters.

[0004] Furthermore, a technique is proposed for transmitting a combined list of web page entries generated from the page scores of the web page entries and the weighted page scores of the friend web page entries to a terminal.Furthermore, a technique is proposed for determining a content score that reflects the verified reliability of data input by a user. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Special Publication No. 2011-507110 [Patent Document 2] JP 2009-122785 A [Patent Document 3] US Patent Application Publication No. 2019 / 0114297 [Patent Document 4] US Patent Application Publication No. 2020 / 0202071 Summary of the Invention [Problem to be solved by the invention]

[0006] Search results from search engines on the Internet do not always provide accurate and reliable information, and may contain erroneous information or fake news. For this reason, in recent years, architectures have been developed that can obtain additional information that serves as the basis for the reliability of Web articles on the Internet (the trustworthiness of Web articles).

[0007] This means that when a user browses a web article on the Internet, they can obtain additional information about the web article, allowing them to verify and judge the reliability of the web article themselves.

[0008] On the other hand, it is time-consuming for users to verify the reliability of Web articles themselves using such an architecture. In one aspect, the present invention has an object to provide an information processing program, an information processing device, and an information processing system that are capable of outputting Web articles based on their reliability. [Means for solving the problem]

[0009] In order to solve the above problem, an information processing program is provided that causes a computer to execute a process of acquiring additional information that can verify the reliability of a plurality of first Web articles, detecting a number of likes that indicates the number of likes that is given to the additional information when the additional information is deemed valid for verifying the reliability, and ranking the plurality of first Web articles based on the number of likes.

[0010] In order to solve the above problem, there is provided an information processing device that executes control similar to that of the above information processing program. Furthermore, in order to solve the above problem, an information processing system that executes control similar to that of the above information processing program is provided. Effect of the Invention

[0011] On the one hand, it enables output based on the reliability of web articles. [Brief description of the drawings]

[0012] [Figure 1] FIG. 1 is a diagram illustrating an example of an information processing device. [Diagram 2] FIG. 1 is a diagram illustrating an example of the architecture of the Trustable Internet. [Diagram 3] FIG. 13 is a diagram showing an example of endorsement data. [Figure 4] FIG. 1 is a diagram illustrating an example of an endorsement graph. [Diagram 5] FIG. 1 illustrates an example of a configuration of an information processing system. [Figure 6] FIG. 2 is a diagram illustrating an example of functional blocks of an information processing device. [Figure 7] FIG. 2 illustrates an example of a hardware configuration of an information processing device. [Figure 8] FIG. 11 is a diagram showing an example of an operation for acquiring information from endorsement data. [Figure 9] 13 is a flowchart showing an example of a search operation for a Web article. [Figure 10] FIG. 13 is a diagram showing an example of a state in which sympathy is added to endorsement data. [Figure 11] FIG. 13 is a diagram illustrating an example of a weighting table. [Figure 12] FIG. 13 is a diagram showing an example of a display after Web articles are rearranged. [Figure 13] FIG. 11 is a diagram for explaining updating of a first weighting value. [Figure 14] FIG. 13 is a diagram for explaining weighting of the number of likes by a first weighting value. [Figure 15] FIG. 11 is a diagram for explaining an example of a state in which a first weighting value is updated. [Figure 16] 11 is a flowchart showing an example of an operation from searching for a Web article to displaying the search results. [Figure 17] 13 is a flowchart showing an example of an operation when an attribute is acquired from endorsement data. [Figure 18] 10 is a flowchart showing an example of an operation for setting and updating a first weighting value. [Figure 19] 13 is a flowchart illustrating an example of an operation for acquiring the number of verifications and the number of non-verifications. [Figure 20] 13 is a flowchart showing an example of an operation for setting and updating a second weighting value. [Figure 21] 13 is a flowchart showing an example of an operation when an attribute is acquired from endorsement data. [Figure 22] FIG. 11 is a diagram for explaining an example of updating a first weighting value. [Figure 23] 10 is a flowchart showing an example of an operation for setting and updating a first weighting value. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] The present embodiment will be described below with reference to the drawings. 1 is a diagram for explaining an example of an information processing device. The information processing device 10 is a device that includes a control unit 11 and performs a web search using the Internet or the like. The function of the control unit 11 is realized, for example, by a processor (not shown) included in the information processing device 10 executing a predetermined program.

[0014] [Step S1] The control unit 11 acquires Web articles D1, D2, and D3 by searching based on keywords for searching Web articles input by the user A. The control unit 11 also acquires additional information ed1, ed2, and ed3 associated with the Web articles D1, D2, and D3, respectively.

[0015] Here, the additional information is information that can verify the reliability of a Web article. For example, assume that a Web article describes the content of an "occurrence of a flood," and the additional information associated with the Web article is "river water levels." In this case, viewers of the Web article can verify the reliability of the "occurrence of a flood" described in the Web article by themselves from the signature information attached to the additional information "river water levels."

[0016] The additional information ed1 is information that can verify the reliability of the Web article D1. Similarly, the additional information ed2 is information that can verify the reliability of the Web article D2, and the additional information ed3 is information that can verify the reliability of the Web article D3.

[0017] [Step S2] The control unit 11 detects the number of sympathies ("Likes") given to the additional information. Sympathies are given by viewers of the Web article (Internet users, etc.), for example, when the additional information is deemed valid for verifying its reliability.

[0018] The control unit 11 detects the number N1 of likes given by Internet users or the like to additional information ed1 that can verify the reliability of the Web article D1. Similarly, the control unit 11 detects the number N2 of sympathies given to additional information ed2 that can verify the reliability of Web article D2, and detects the number N3 of sympathies given to additional information ed3 that can verify the reliability of Web article D3.

[0019] [Step S3] The control unit 11 calculates an evaluation value of the reliability of each of the Web articles D1, D2, and D3 from the number of likes. It is assumed that an evaluation value V1 is calculated from the number of likes N1, an evaluation value V2 is calculated from the number of likes N2, and an evaluation value V3 is calculated from the number of likes N3.

[0020] [[Step S4]] The control unit 11 sorts (for example, rearranges) the Web articles D1, D2, and D3 based on the evaluation values. For example, if the magnitudes of the evaluation values are V2 < V1 < V3, among the Web articles D1, D2, and D3, the article with the highest reliability of the article content is Web article D3, the next most reliable one is Web article D1, and the least reliable one is Web article D2.

[0021] Therefore, the control unit 11 rearranges the Web articles D3, D1, and D2 in descending order of the evaluation value (reliability) based on the reliability evaluation value. Also, the control unit 11 can perform the screen display of the rearranged Web articles D3, D1, and D2.

[0022] In this way, in the information processing apparatus 10, for the retrieved Web articles, additional information that can verify the reliability of the article content and is associated with the Web articles is acquired, and the Web articles are sorted based on the number of sympathies given to the additional information. As a result, an output based on the reliability of the Web articles becomes possible.

[0023] Also, in the information processing apparatus 10, an evaluation value of the reliability of the Web articles can be calculated based on the number of sympathies given to the additional information, and the Web articles can be sorted based on the evaluation value. As a result, when a search is performed using a search engine, it becomes possible to more accurately perform an output such as a screen display based on the reliability of the Web articles without the user having to verify the reliability of the Web articles themselves.

[0024] <Trustable Internet> Next, Trustable Internet that enables the acquisition of additional information that is the basis for the reliability of Web articles on the Internet will be described.

[0025] 2 is a diagram showing an example of the architecture of the Trustable Internet. The Trustable Internet 2 has an architecture in which a layer called the Endorsement Layer is added between the Internet Layer 22, which is above the Physical Layer 21, and the Web / Application Layer 24.

[0026] The additional information that serves as the basis for the reliability of a Web article is called endorsement data, and the endorsement layer 23 has a function of managing the endorsement data ED.

[0027] The endorsement layer 23 associates and connects the endorsement data ED added by the user at any timing, and accumulates it on the endorsement layer 23 as graph data (endorsement graph EG).

[0028] A user can view the connections of endorsement data for a Web article for which the reliability is to be determined via the endorsement graph EG. Furthermore, when the user retrieves a Web article on the Internet, the user can search and check the endorsement data ED from the endorsement layer 23. Alternatively, the user can verify and determine the reliability of the Web article that the user wants to retrieve by requesting the endorsement data ED required for judging the reliability.

[0029] <Endorsement data> Figure 3 shows an example of endorsement data. Endorsement data is information about web articles on the Internet, or information about individuals, corporations, devices, etc., recorded in a form that cannot be tampered with using digital signatures.

[0030] The example of FIG. 3 shows endorsement data ED41 for a Web article titled "Flood Occurrence," and endorsement data ED42 for the identity of an individual, corporation, device, etc. who has given the endorsement data ED41.

[0031] In endorsement data, information that is the basis for the reliability of a web article on the Internet is treated as a "property." A property represents the article data of a web article, or the attributes or functions of an individual, corporation, device, etc. For example, for endorsement data ED41 for a web article titled "Flooding," the properties would be "Posted by: Mr. X" and "Photo date and time: 20xx.xx.xx."

[0032] A property is one piece of information that serves as the basis for the reliability of a web article for its viewers. An individual, corporation, device, etc. that generates a property and issues endorsement data is called an endorser.

[0033] In this example, the endorser who generated the property related to the Web article "Occurrence of the Flood" is the sender, Mr. X. Furthermore, the endorsement data ED42 related to Mr. X is linked to the endorsement data ED41.

[0034] The endorsement data ED42 has properties such as Mr. X's address (city B) and date of birth (19yy.yy.yy), and the issuer of the endorsement data ED42 that generated the properties is the City Hall of B, the municipality to which Mr. X belongs.

[0035] By adding such endorsement data to web articles on the Internet, readers of the web articles can confirm the creator and place of creation of the web article, and can judge the reliability of the web article from the endorsement data.In addition, the endorsement data also expresses the identity information of the individuals, corporations, and devices (sensor measurements, etc.) that added the endorsement data, so readers can also judge the reliability of the web article from such identity information.

[0036] <Endorsement graph> Figure 4 shows an example of an endorsement graph. The endorsement graph EG is a graph that represents the web articles that are the target data for which the reliability is judged, the endorsement data that is the basis for the reliability judgment, and the endorsers who issued the endorsement data.

[0037] This example shows an example of an endorsement graph EG created for a Web article D21 posted by a member of the public saying "Flooding is likely to occur" and a Web article D22 including a posted image showing the state of a river. Note that hereafter, a digital signature may be simply referred to as a signature.

[0038] Here, the endorsement data ED43 "Water level: low" is attached to the Web article D21 that states "Flooding is likely to occur," and the endorsement data ED43 is linked to the endorser a1 of the "government agency."

[0039] In addition, the endorsement data ED44 of "Contributor: X" is attached to the Web article D21, and the endorsement data ED44 is linked to the endorser a2 of "ordinary citizen." Furthermore, the endorsement data ED45 of "Address: City B" is attached to the endorser a2, and the endorsement data ED45 is linked to the endorser a3 of "City B."

[0040] Meanwhile, in Web article D22 of a river image related to Web article D21, endorsement data ED46 of "Location: City A, Date and time: . . . " is attached, and endorser a4 of "camera c made by company b" is linked to the endorsement data ED46. Furthermore, endorsement data ED47 of "Manufacturer: b" is attached to this endorser a4, and endorser a5 of "camera manufacturer b" is linked to the endorsement data ED47.

[0041] Viewers of Web articles D21 and D22 can recognize from such endorsement graph EG that the image posted in Web article D22 was taken in a place other than the residence of poster X, and that data contrary to the posted content from a government agency has been provided. This enables the viewer to determine that Web article D21 is not trustworthy.

[0042] In this way, Trustable Internet2 allows users to share endorsement graphs, which are graphs of endorsement data (additional information). Users can then obtain and view the endorsement data that is the basis for the reliability of web articles as needed while still using the existing Internet, and can judge for themselves whether the data is true or false.

[0043] This embodiment utilizes the Trustworthy Internet 2 described above, making it possible for users to obtain Web articles ordered according to their trustworthiness when performing a search on a search engine, without the user having to verify the reliability of the data themselves.

[0044] <System configuration> 5 is a diagram showing an example of a configuration of an information processing system 1. The information processing system 1 includes a server device 10a and a client device 10b, and the server device 10a and the client device 10b are connected via a network 4.

[0045] In the above-described FIG. 1, the information processing device 10 functions as a device that receives input search keywords, performs the processes from step S1 to step S4, and displays ordered Web articles.

[0046] In contrast, in the information processing system 1, a device that accepts input search keywords, displays ordered Web articles, and allows Web article browsing is the client device 10b, and a device that performs the processes from step S1 to step S4 is the server device 10a. In addition, fact-checking of Web articles may be performed by the client device 10b. The functions of the present invention can be configured in such a system.

[0047] <Function block> 6 is a diagram showing an example of a functional block of an information processing device. The control unit 11 includes a search result list acquisition unit 11a, an endorsement graph acquisition unit 11b, an endorsement graph management unit 11c, a weighting value update unit 11d, an evaluation value calculation unit 11e, a search result sorting unit 11f, and a search result display unit 11g. The information processing device 10 also includes a storage unit 12, and a weighting table T1 is stored in the storage unit 12.

[0048] The search result list acquisition unit 11a accepts keywords input from the user, searches for Web articles according to the input keywords, and acquires a list of Web articles as search results. The endorsement graph acquisition unit 11b acquires the endorsement graph of the specified Web article. The endorsement graph management unit 11c manages the endorsement graph.

[0049] The weighting value update unit 11d sets and updates the weighting value used when calculating an evaluation value of the reliability of a Web article from the number of likes. The evaluation value calculation unit 11e refers to the weighting table T1 and assigns weights to the acquired number of likes to calculate an evaluation value of the Web article. The search result sorting unit 11f ranks multiple Web articles based on the calculated evaluation values. The search result display unit 11g displays the ranked Web articles on a screen.

[0050] <Hardware> 7 is a diagram showing an example of the hardware configuration of an information processing device. The information processing device 10 is entirely controlled by a processor 101 having the functions of a control unit 11. A memory 102 and a plurality of peripheral devices are connected to the processor 101 via a bus 109. The processor 101 may be a multiprocessor. The processor 101 is, for example, a central processing unit (CPU), a micro processing unit (MPU), or a digital signal processor (DSP). At least a part of the functions realized by the processor 101 executing a program may be realized by an electronic circuit such as an application specific integrated circuit (ASIC) or a programmable logic device (PLD).

[0051] The memory 102 is used as a main storage device of the information processing device 10. The memory 102 temporarily stores at least a part of the OS (Operating System) program and application programs to be executed by the processor 101. The memory 102 also stores various data used for processing by the processor 101. As the memory 102, for example, a volatile semiconductor storage device such as a RAM (Random Access Memory) is used.

[0052] The peripheral devices connected to the bus 109 include a storage device 103, a GPU (Graphics Processing Unit) 104, an input interface 105, an optical drive device 106, a device connection interface 107, and a network interface 108.

[0053] The storage device 103 has the function of the memory unit 12, and writes and reads data electrically or magnetically to and from a built-in recording medium. The storage device 103 is used as an auxiliary storage device for the information processing device 10. The storage device 103 stores an OS program, application programs, and various data. Note that, for example, an HDD (Hard Disk Drive) or an SSD (Solid State Drive) can be used as the storage device 103.

[0054] The GPU 104 is a computing device that performs image processing, and is also called a graphics controller. A display 201 is connected to the GPU 104. The GPU 104 displays an image on the screen of the display 201 in accordance with an instruction from the processor 101. The display 201 may be a display device using an organic EL (Electro Luminescence) display device, a liquid crystal display device, or the like.

[0055] A keyboard 202 and a mouse 203 are connected to the input interface 105. The input interface 105 transmits signals sent from the keyboard 202 and the mouse 203 to the processor 101. Note that the mouse 203 is an example of a pointing device, and other pointing devices can also be used. Examples of other pointing devices include a touch panel, a tablet, a touch pad, and a trackball.

[0056] The optical drive device 106 uses a laser beam or the like to read data recorded on an optical disc 204 or write data to the optical disc 204. The optical disc 204 is a portable recording medium on which data is recorded so that it can be read by reflection of light. The optical disc 204 includes a DVD (Digital Versatile Disc), a DVD-RAM, a CD-ROM (Compact Disc Read Only Memory), a CD-R (Recordable) / RW (Re Writable), and the like.

[0057] The device connection interface 107 is a communication interface for connecting peripheral devices to the information processing device 10. For example, a memory device 205 or a memory reader / writer 206 can be connected to the device connection interface 107. The memory device 205 is a recording medium equipped with a communication function with the device connection interface 107. The memory reader / writer 206 is a device that writes data to the memory card 207 or reads data from the memory card 207. The memory card 207 is a card-type recording medium.

[0058] The network interface 108 is connected to the network 4. The network interface 108 transmits and receives data to and from other computers or communication devices via the network 4. The network interface 108 is a wired communication interface that is connected by a cable to a wired communication device such as a switch or a router. The network interface 108 may also be a wireless communication interface that is communicatively connected to a wireless communication device such as an access point via radio waves.

[0059] The information processing device 10 can realize the processing function of the present invention by using the above-mentioned hardware. The information processing device 10 realizes the processing function of the present invention by executing a program recorded on a computer-readable recording medium, for example. The program describing the processing content to be executed by the information processing device 10 can be recorded on various recording media.

[0060] For example, the program to be executed by the information processing device 10 can be stored in the storage device 103. The processor 101 loads at least a part of the program in the storage device 103 into the memory 102 and executes the program. The program to be executed by the information processing device 10 can also be recorded in a portable recording medium such as the optical disk 204, the memory device 205, or the memory card 207. The program stored in the portable recording medium becomes executable after being installed in the storage device 103 under the control of the processor 101, for example. The processor 101 can also read out the program directly from the portable recording medium and execute it.

[0061] <Obtaining data from endorsement data> 8 is a diagram showing an example of an operation for acquiring information from endorsement data, in which, for example, endorser information and a category of a Web article are acquired as information acquired from endorsement data.

[0062] [Step S11] The control unit 11 obtains data on the contributor X as part of the endorsement data ED44 from the endorsement graph EG of the Web article D21. [Step S12] The control unit 11 verifies the digital signature of the acquired data.

[0063] [Step S13] After the control unit 11 confirms that the digital signature verification has been successful, it acquires information about the user (endorser) who granted the endorsement data ED44 and information about the category of the Web article D21 from the acquired endorsement data ED44.

[0064] <Search behavior> FIG. 9 is a flow chart showing an example of a search operation for a Web article. [Step S21] When a user performs a search operation by inputting a keyword indicating a subject to be searched for on the search screen, the control unit 11 obtains a list of searched Web articles.

[0065] [Step S22] The control unit 11 obtains, for each Web article included in the obtained Web article list, an endorsement graph attached to the Web article. [Step S23] The control unit 11 acquires the number of likes given to the endorsement data in the acquired endorsement graph and the identification information of the user who gave the likes.

[0066] [Step S24] The control unit 11 acquires the weighting values ​​(first weighting value and second weighting value) set for the user who has given sympathy, based on the weighting table T1.

[0067] [Step S25] The control unit 11 weights the number of likes based on the first weighting value and the second weighting value, and calculates an evaluation value of the reliability of each Web article. When calculating the weighted number of likes weighted by the first weighting value and the second weighting value, the control unit 11 uses, for example, the following formula (1). Note that the constants C1 and C2 in formula (1) are preset values.

[0068] Weighted number of sympathies = number of sympathies + (first weighting value w1 × constant C1 + second weighting value w2 × constant C2) ÷ 2 (1) [Step S26] The control unit 11 ranks the Web articles according to the calculated evaluation values ​​and displays them on the screen as search results.

[0069] <Calculation of evaluation value and sorting of web articles> Next, calculation of evaluation values ​​and sorting of Web articles will be described with reference to Figs. 10 to 12. Fig. 10 is a diagram showing an example of a state in which sympathy is added to endorsement data. Assume that the control unit 11 acquires Web articles D11, D12, and D13 in response to a user's search operation. In addition, categories are added to the Web articles to be searched for in order to classify the article contents. Web articles D11, D12, and D13 in Fig. 10 are articles related to IT (Information Technology) and belong to the IT category.

[0070] An endorsement graph EG1 is attached to the Web article D11. The endorsement graph EG1 includes endorsement data ED11, ED12, and ED13, and the endorsement data ED11, ED12, and ED13 are graphed.

[0071] Furthermore, five likes ("Likes") are given to the endorsement data ED11, two likes are given to the endorsement data ED12, and one like to the endorsement data ED13. Of the two likes for the endorsement data ED12, one is given by user B, and the other is given by user C.

[0072] An endorsement graph EG2 is attached to the Web article D12. The endorsement graph EG2 includes endorsement data ED21, ED22, and ED23, and the endorsement data ED21, ED22, and ED23 are graphed.

[0073] Furthermore, five sympathies are given to the endorsement data ED21, two sympathies are given to the endorsement data ED22, and one sympathy is given to the endorsement data ED23. Of the two sympathies given to the endorsement data ED22, one sympathy is given by an anonymous user, and the other sympathy is given by user D.

[0074] An endorsement graph EG3 is attached to the Web article D13. The endorsement graph EG3 includes endorsement data ED31, ED32, and ED33, and the endorsement data ED31, ED32, and ED33 are graphed.

[0075] In addition, five sympathies are given to the endorsement data ED31, two sympathies are given to the endorsement data ED32, and one sympathy is given to the endorsement data ED33. Of the two sympathies in the endorsement data ED32, one is given by an anonymous user, and the other is given by user A.

[0076] 11 is a diagram showing an example of a weighting table. The weighting table T1 has items of user (user identification information), category, first weighting value w1, and second weighting value w2.

[0077] In the weighting table T1, it is shown that when user A gives sympathy to endorsement data contained in a Web article about IT-related content, the number of sympathies of user A is calculated using the first weighting value w1 and the second weighting value w2.

[0078] In addition, when user B sympathizes with endorsement data included in a Web article about sports, the number of sympathies of user B is calculated by the first weighting value w1 and the second weighting value w2. The same can be said about users C and D.

[0079] Here, in the Web article D11, the endorsement data ED12 is given sympathy by users B and C who are registered in the weighting table T1. However, the weighting table T1 indicates that the number of sympathies for user B is calculated based on a weighting value when the category is sports, and that the number of sympathies for user C is calculated based on a weighting value when the category is medical.

[0080] Therefore, because the category of Web article D11 is IT, the evaluation value is calculated without using a weighting value. In this case, the sum of the number of sympathies is calculated, and the evaluation value of Web article D11 is 8 (=5+2+1).

[0081] In Web article D12, endorsement data ED22 is given sympathy by an anonymous user and user D who is registered in weighting table T1. Weighting table T1 indicates that the number of sympathies of user D is weighted based on a first weighting value w1 and a second weighting value w2 when the category is IT, with the first weighting value w1=1 and the second weighting value w2=2.

[0082] Therefore, the weighted number of sympathies of user D is calculated by formula (1). If constant C1 = constant C2 = 1, then the weighted number of sympathies of user D = 1 + (1 x 1 + 2 x 1) ÷ 2 = 2.5. Therefore, the evaluation value of Web article D12 is 8.5 (= 5 + 2.5 + 1).

[0083] In the Web article D13, the endorsement data ED32 is given sympathy by an anonymous user and user A who is registered in weighting table T1. Weighting table T1 indicates that the number of sympathies of user A is weighted based on a first weighting value w1 and a second weighting value w2 when the category is IT, with the first weighting value w1=3 and the second weighting value w2=1.

[0084] Therefore, the weighted number of sympathies of user A is calculated by formula (1). If constant C1 = constant C2 = 1, then the weighted number of sympathies of user A = 1 + (3 × 1 + 1 × 1) ÷ 2 = 3. Therefore, the evaluation value of Web article D13 is 9 (= 5 + 3 + 1).

[0085] 12 is a diagram showing an example of the display after the Web articles have been rearranged. As described above, Web article D11 has an evaluation value of 8, Web article D12 has an evaluation value of 8.5, and Web article D13 has an evaluation value of 9. Therefore, the Web articles are rearranged on the search screen in descending order of evaluation value, and, for example, Web article D13, Web article D12, and Web article D11 are displayed from the top of the screen.

[0086] In this way, when calculating the evaluation value, weighting is applied not only to the number of likes but also to the sympathies given by specific users, making it possible to improve the accuracy of the evaluation value that represents the reliability of a Web article.

[0087] <First weighting value> Next, the first weighting value w1 will be described with reference to Figures 13 and 14. Figure 13 is a diagram for explaining the update of the first weighting value.

[0088] [Step S31] A fact check (verification of the truth of the article contents) is performed on Web article #1, which is in the IT category, by a third party or the like. [Step S32] User b assigns endorsement data ED51 to a Web article that has been fact-checked and determined to be true (hereinafter, may be referred to as a fact-checked Web article) (EG in the figure represents an endorsement graph).

[0089] [Step S33] The control unit 11 (or the person who carried out the fact-check of Web article #1) gives a digital signature Sg1 to user b who has given the endorsement data ED51 to the fact-checked Web article #1.

[0090] [Step S34] A third party or the like fact-checks Web article #2, which belongs to the IT category. Web article #2 belongs to the IT category, but the article content is different from Web article #1, for example.

[0091] [Step S35] User b assigns endorsement data ED61 to the fact-checked Web article #2. [Step S36] The control unit 11 (or the person who carried out the fact-check of Web article #2) gives a digital signature Sg2 to user b who has given the endorsement data ED61 to the fact-checked Web article #2.

[0092] In this way, users who provide endorsement data to fact-checked web articles are given a digital signature (hereinafter sometimes referred to as a specific digital signature) as proof of their endorsement.

[0093] [Step S37] The control unit 11 updates the first weighting value w1 in the weighting table T1, which corresponds to the identification information of user b and the category of IT, in accordance with the number of digital signatures assigned to user b.

[0094] 14 is a diagram for explaining weighting of the number of sympathies by the first weighting value. User b has a track record of adding endorsement data to fact-checked web articles (category IT), and has been given a specific digital signature indicating that track record.

[0095] For example, if such a user b gives sympathy to the endorsement data ED71 of the Web article #3 that is in the IT category and has not been fact-checked, the number of sympathies is weighted by the first weighting value w1.

[0096] Now, let us assume that user b, who has a specific digital signature, gives sympathy to endorsement data included in a web article that is in the IT category and has not been fact-checked. In this case, the sympathy given by user b is more reliable than the sympathy given by a user who does not have a specific digital signature to endorsement data included in a web article in the same IT category.

[0097] Therefore, the number of likes given by a user having a specific digital signature for a Web article in a certain category Ct to a Web article in the same category Ct is weighted by the first weighting value w1, and the weighted number of likes given by the first weighting value w1 is used when calculating the evaluation value.

[0098] FIG. 15 is a diagram for explaining an example of a state in which the first weight value is updated. Regarding the information of user b before the update of the first weight value w1, there is a record of the achievement of giving endorsement data to a Web article in the IT category that has been fact-checked, and digital signatures Sg1 and Sg2 are given. At this time, it is assumed that the first weight value w1 corresponding to user b and category IT in the weight table T1-1 is 2.

[0099] As information of user b after the update of the first weight value w1, when new endorsement data is given to a Web article in the IT category that has been fact-checked, as an achievement, a digital signature Sg3 is further given. In this case, as shown in the weight table T1-2, the first weight value w1 corresponding to user b and category IT is updated from 2 to 3.

[0100] In this way, the control unit 11 gives a digital signature (specific digital signature) to an additional information issuing user (user b in this example) who has issued endorsement data to a fact-checked Web article.

[0101] Then, the control unit 11 updates the first weight value w1 corresponding to the identification information of the additional information issuing user and the predetermined category of the Web article registered in the weight table T1 based on the number of digital signatures.

[0102] Thereby, the difference in reliability between the consensus given by a user having a specific digital signature and the consensus given by a user not having a specific digital signature for Web articles in the same category can be reflected in the calculation of the evaluation value, so that the accuracy of the evaluation value representing the reliability can be improved.

[0103] <Operation from search of Web article to display of search result> FIG. 16 is a flowchart showing an example of the operation from search of a Web article to display of the search result.

[0104] [Step S40] The user inputs keywords into the search screen to request a search. [Step S41] The search result list acquisition unit 11a performs a search using a keyword, and acquires a list of Web articles obtained by the search (Web article list).

[0105] [Step S42] The search result list acquisition unit 11a determines whether or not there is a Web article in the list of Web articles for which a weighting value has not been acquired. If there is a Web article for which a weighting value has not been acquired, the process proceeds to step S43. If there is no Web article for which a weighting value has not been acquired, the process proceeds to step S47.

[0106] [Step S43] The endorsement graph acquisition unit 11b selects a Web article for which a weighting value has not yet been acquired. [Step S44] The endorsement graph acquisition unit 11b acquires the endorsement data attached to the selected Web article from the endorsement graph management unit 11c.

[0107] [Step S45] For the endorsement data obtained from the endorsement graph management unit 11c, the evaluation value calculation unit 11e obtains the number of sympathies (number of "likes"), identification information of the user who gave the sympathy, and attributes of the category of the Web article to which the endorsement data belongs.

[0108] [Step S46] The evaluation value calculation unit 11e refers to the weighting table T1, and acquires the first weighting value w1 and the second weighting value w2 using the user (identification information) who gave the sympathy and the category to which the endorsement data belongs as keys. Return to the process of step S42.

[0109] [Step S47] The evaluation value calculation unit 11e weights the number of likes of the acquired endorsement data based on the first weighting value w1 and the second weighting value w2, and calculates an evaluation value for each Web article.

[0110] [Step S48] The search result sorting unit 11f sorts the Web articles included in the Web article list in order of the reliability of the content of the Web articles according to the evaluation value. [Step S49] The search result display unit 11g displays a list of Web articles including the sorted Web articles on the screen as search results.

[0111] 5, when a search keyword is input to the server device 10a and the above-mentioned process is performed in the server device 10a, for example, the rearranged Web articles are displayed on the screen of the server device 10a. When a search keyword is input to the client device 10b and the above-mentioned process is performed in the server device 10a, for example, the rearranged Web articles are displayed on the screen of the client device 10b.

[0112] <Action to obtain attributes from endorsement data> 17 is a flowchart showing an example of an operation for acquiring an attribute from endorsement data, and is a flowchart showing the detailed operation of step S45 in FIG.

[0113] [Step S51] The evaluation value calculation unit 11e selects the target endorsement data as root data. This means selecting the target endorsement data as the starting endorsement data, and the starting endorsement data corresponds to, for example, the endorsement data ED44 in FIG. 4.

[0114] [Step S52] The evaluation value calculation unit 11e acquires the selected endorsement data. [Step S53] The evaluation value calculation unit 11e verifies the digital signature of the obtained endorsement data.

[0115] [Step S54] If the digital signature verification is successful, proceed to step S55; if not, end the process. [Step S55] The evaluation value calculation unit 11e refers to the endorsement data of the user who gave the endorsement data (endorser's data) from the obtained endorsement data, and selects the endorsement data to be digitally verified next. In other words, it selects the endorsement data that can be traced back next from the obtained endorsement data.

[0116] [Step S56] The evaluation value calculation unit 11e judges whether there is any endorsement data to be selected. If there is no endorsement data to be selected (if there is no endorsement data to be traced next), the process proceeds to step S57, and if there is endorsement data to be selected, the process returns to step S52.

[0117] [Step S57] The evaluation value calculation unit 11e judges whether the user who provided the acquired endorsement data (endorsement data traced to the end) is trustworthy. If the user is trustworthy, the process proceeds to step S58. If the user is not trustworthy, the process ends.

[0118] [Step S58] For the endorsement data acquired in step S51, the evaluation value calculation unit 11e acquires the number of likes, identification information of the user who assigned the like, and attributes of the category to which the endorsement data belongs.

[0119] As described above, the evaluation value calculation unit 11e acquires the endorsement data as a starting point, and verifies whether the endorsement data is reliable by tracing the endorser data. Then, when a reliable endorser is reached as a result of tracing, it is determined that the endorsement data is reliable as a whole, and acquires attributes from the endorsement data.

[0120] <Setting and updating the first weighting value> FIG. 18 is a flowchart showing an example of the operation of setting and updating the first weighting value. [Step S61] The weighting value update unit 11d determines whether a Web article has been fact-checked (whether the content of the fact-checked Web article is true). If the Web article has been fact-checked, the process proceeds to step S62; if the Web article has not been fact-checked, the process ends.

[0121] [Step S62] The weighting value update unit 11d acquires, from the endorsement graph management unit 11c, the identification information of the user who assigned endorsement data to the fact-checked Web article and the category of the endorsement data.

[0122] [Step S63] The weighting value update unit 11d assigns a digital signature to the user who assigned endorsement data to the fact-checked Web article. [Step S64] The weighting value update unit 11d obtains the number of digital signatures of the user using the user's identification information and category as keys.

[0123] [Step S65] The weighting value update unit 11d sets and updates the first weighting value w1 in the weighting table T1 with the number of acquired digital signatures, using the user identification information and category as keys.

[0124] The above steps S62-S65 are executed for each piece of endorsement data attached to a fact-checked Web article. <Setting and updating the second weighting value> 19 is a flowchart showing an example of an operation for acquiring the verification count and the non-verification count. The verification count is the number of times that the endorsement data was verified by the viewer, and the non-verification count is the number of times that the endorsement data was not verified by the viewer.

[0125] [Step S71] The weighting value update unit 11d determines whether or not the Web article has been viewed. If the Web article has been viewed, the process proceeds to step S72. If the Web article has not been viewed, the process ends.

[0126] [Step S72] The endorsement graph management unit 11c checks whether the reader of the Web article has acquired the endorsement data attached to the Web article. [Step S73] The endorsement graph management unit 11c determines whether the reader has acquired endorsement data attached to the Web article. If endorsement data has been acquired, the process proceeds to step S74; if not, the process ends.

[0127] [Step S74] The weighting value update unit 11d checks whether the viewer has verified the obtained endorsement data. [Step S75] The weighting value update unit 11d judges whether the obtained endorsement data has been verified by the viewer. If the obtained endorsement data has been verified, the process proceeds to step S76. If the obtained endorsement data has not been verified, the process proceeds to step S77.

[0128] [Step S76] The weighting value update unit 11d counts up the number of verifications (value v1) in which endorsement data attached to a Web article is obtained and verified when the Web article is viewed, and holds the value a.

[0129] [Step S77] The weighting value update unit 11d counts up the number of non-verification times (value v2) when endorsement data attached to a Web article was obtained and not verified when the Web article was viewed, and stores the value b.

[0130] Through the above process, the verification count (value v1) and non-verification count (value v2) corresponding to the endorsement data are stored. FIG. 20 is a flowchart showing an example of the operation of setting and updating the second weighting value.

[0131] [Step S81] The weighting value update unit 11d determines whether or not a Web article has been viewed by a certain reader. If the Web article has been viewed, the process proceeds to step S82. If the Web article has not been viewed, the process ends.

[0132] [Step S82] The weighting value update unit 11d determines whether or not the reader of the Web article has given sympathy to the endorsement data given to the Web article. If the reader has given sympathy, the process proceeds to step S83. If the reader has not given sympathy, the process ends.

[0133] [Step S83] The weighting value update unit 11d acquires, from the endorsement graph management unit 11c, identification information of the user (viewing user) who gave sympathy to the endorsement data of the viewed Web article and the category of the endorsement data.

[0134] [Step S84] When a Web article is viewed by a viewing user, the weighting value update unit 11d detects the number of verifications (value v1) and the number of non-verifications (value v2) corresponding to the endorsement data that has been assigned to the Web article and to which sympathy has been given.

[0135] [Step S85] The weighting value update unit 11d calculates the ratio of the number of non-verifications by v2 / (v1+v2), where v1 is the number of verifications and v2 is the number of non-verifications, and judges whether the ratio is equal to or greater than a threshold. If the ratio is equal to or greater than the threshold, the process proceeds to step S86, and if the ratio is less than the threshold, the process ends.

[0136] [Step S86] The weighting value update unit 11d sets and updates the second weighting value w2 in the weighting table T1 by adding 1 to the existing value, using the viewing user's identification information and the category as keys.

[0137] In this way, when a Web article is viewed and the endorsement data associated with the Web article is given sympathy, the control unit 11 calculates the percentage of non-verification times when the endorsement data was not verified by the viewer out of the number of times the endorsement data was obtained by the viewer up to that point.

[0138] Then, if the ratio is greater than or equal to the threshold, the control unit 11 updates the second weighting value w2 registered in the weighting table T1, which corresponds to the identification information of the user who assigned the endorsement data and the category of the viewed Web article.

[0139] As a result, for endorsement data obtained by users who viewed a web article and for which the proportion of non-verifications is equal to or greater than a threshold, the reliability of empathy can be increased and reflected in the calculation of the evaluation value, thereby making it possible to improve the accuracy of the evaluation value that indicates reliability.

[0140] <Action to get attributes from endorsement> 21 is a flowchart showing an example of an operation for acquiring an attribute from endorsement data, which is a flowchart showing the detailed operation of step S62 in FIG. 18 and step S83 in FIG.

[0141] [Step S91] The weighting value update unit 11d selects the target endorsement data as root data. This means selecting the target endorsement data as the starting endorsement data, and the starting endorsement data corresponds to, for example, the endorsement data ED44 in FIG. 4.

[0142] [Step S92] The weighting value update unit 11d acquires the selected endorsement data. [Step S93] The weighting value update unit 11d verifies the digital signature of the obtained endorsement data.

[0143] [Step S94] If the digital signature verification is successful, proceed to step S95; if not, end the process. [Step S95] The weighting value update unit 11d refers to the endorsement data of the user who gave the endorsement data (endorser's data) from the acquired endorsement data, and selects the endorsement data to be digitally verified next. In other words, it selects the endorsement data that can be traced back next from the acquired endorsement data.

[0144] [Step S96] The weighting value update unit 11d judges whether there is any endorsement data to be selected. If there is no endorsement data to be selected (if there is no endorsement data to be traced next), the process proceeds to step S97. If there is endorsement data to be selected, the process returns to step S92.

[0145] [Step S97] The weighting value update unit 11d judges whether the user who provided the acquired endorsement data (endorsement data traced all the way to the end) is trustworthy. If the user is trustworthy, the process proceeds to step S98. If the user is not trustworthy, the process ends.

[0146] [Step S98] The weighting value update unit 11d acquires, for the endorsement data acquired in step S91, the number of likes, identification information of the user who assigned the likes, and attributes of the category to which the endorsement data belongs.

[0147] As described above, the weighting value update unit 11d acquires the endorsement data as the starting point, and verifies whether the endorsement data is reliable by tracing the endorser data. Then, when a reliable endorser is reached as a result of tracing, it is determined that the endorsement data is reliable as a whole, and acquires attributes from the endorsement data.

[0148] <Modification of setting and updating of first weighting value> Next, a modified example of setting and updating the first weighting value will be described with reference to Fig. 22 and Fig. 23. Fig. 22 is a diagram for explaining an example of updating the first weighting value. Web article #4 is a Web article that has been fact-checked by a fact checker and belongs to the IT category.

[0149] The endorsement data ED81-ED83 are given to the Web article #4 in the IT category by the users b1-b3, respectively. In this state, a fact check is performed on the Web article #4.

[0150] If the fact check determines that the contents of Web article #4 are true, the control unit 11 assigns digital signatures (specific digital signatures) Sg1, Sg2, and Sg3 to users b1, b2, and b3, respectively, who assigned endorsement data ED81, ED82, and ED83 to Web article #4.

[0151] Here, in the update of the first weighting value w1 described above in Figures 13-15, the number of digital signatures assigned to a user becomes the first weighting value w1, but in a modified example, the number of assigned digital signatures is divided by the number of endorsement data assigned to Web articles in one category, and the divided value (sometimes called a normalized weighting value) is added to the first weighting value w1 to update it.

[0152] 22, three pieces of endorsement data ED81, ED82, and ED83 are assigned to Web article #4, which has been fact-checked and is in the IT category. Therefore, the division value 1 / 3 obtained by dividing the number of digital signatures Sg1 assigned to user b1 by 3 becomes the normalized weighting value for updating the first weighting value w1 of user b1.

[0153] Similarly, the division value 1 / 3 obtained by dividing the number of digital signatures Sg2 assigned to user b2 by 3 becomes the normalized weighting value for updating the first weighting value w1 of user b2. Furthermore, the division value 1 / 3 obtained by dividing the number of digital signatures Sg3 assigned to user b3 by 3 becomes the normalized weighting value for updating the first weighting value w1 of user b3.

[0154] FIG. 23 is a flowchart showing an example of the operation of setting and updating the first weighting value. [Step S101] The weighting value update unit 11d determines whether a Web article has been fact-checked (whether the content of the fact-checked Web article is true). If the Web article has been fact-checked, the process proceeds to step S102, and if the Web article has not been fact-checked, the process ends.

[0155] [Step S102] The weighting value update unit 11d determines whether or not there is an unacquired endorsement graph. If there is an unacquired endorsement graph, the process proceeds to step S103. If there is no unacquired endorsement graph, the process proceeds to step S105.

[0156] [Step S103] The weighting value update unit 11d acquires an endorsement graph that has not yet been acquired. [Step S104] The weighting value update unit 11d acquires the identification information of the user who assigned the acquired endorsement data and the category of the endorsement data (the same as the category of the Web article) from the endorsement graph management unit 11c.

[0157] [Step S105] The weighting value update unit 11d assigns a digital signature to each user who has assigned endorsement data to a fact-checked Web article having the acquired category, as proof that the endorsement data was assigned to the fact-checked article.

[0158] [Step S106] The weighting value update unit 11d calculates a normalized weighting value for each user who has given endorsement data to a fact-checked Web article having the obtained category.

[0159] [Step S107] The weighting value update unit 11d acquires the first weighting value w1 currently registered for each user using the identification information and category as keys. [Step S108] The weighting value update unit 11d adds the normalized weighting value to the acquired first weighting value w1 to set or update the first weighting value w1.

[0160] In this way, multiple additional information issuing users (users b1, b2, b3) issue endorsement data for fact-checked Web articles of a predetermined category. In this case, the control unit 11 assigns specific digital signatures (Sg1, Sg2, Sg3) indicating that the endorsement data has been issued to the fact-checked Web articles.

[0161] In addition, the control unit 11 updates the first weighting value w1 corresponding to the identification information of the additional information issuing user and a specified category of the Web article, which is registered in the weighting table T1, based on the value obtained by dividing the number of specific digital signatures assigned to each additional information issuing user by the total number of multiple endorsement data.

[0162] This allows the number of likes given by a user with a specific digital signature to be divided by the number of endorsement data given to Web articles in the same category, thereby normalizing the trustworthiness of each user to whom a digital signature has been given and reflecting this in the calculation of the evaluation value, thereby making it possible to improve the accuracy of the evaluation value representing trustworthiness.

[0163] The information processing device and the information processing system of the present invention described above can be realized by a computer. In this case, a program is provided that describes the processing contents of the functions that the information processing device and the information processing system should have. By executing the program on a computer, the above processing functions are realized on the computer.

[0164] The program describing the processing contents can be recorded on a computer-readable recording medium. Examples of computer-readable recording media include magnetic storage units, optical disks, magneto-optical recording media, and semiconductor memories. Examples of magnetic storage units include hard disk drives (HDDs), flexible disks (FDs), and magnetic tapes. Examples of optical disks include CD-ROM / RWs. Examples of magneto-optical recording media include MOs (Magneto Optical disks).

[0165] When distributing a program, for example, a portable recording medium such as a CD-ROM on which the program is recorded is sold. Also, the program can be stored in the memory of a server computer and transferred from the server computer to other computers via a network.

[0166] A computer that executes a program stores, for example, a program recorded on a portable recording medium or a program transferred from a server computer in its own storage unit. The computer then reads the program from its own storage unit and executes processing according to the program. Note that the computer can also read the program directly from a portable recording medium and execute processing according to the program.

[0167] The computer can also execute processing according to the programs it receives each time the programs are transferred from a server computer connected via a network. At least a part of the processing functions can be realized by electronic circuits such as DSPs, ASICs, and PLDs.

[0168] Although the embodiments have been described above, the configurations of the parts shown in the embodiments can be replaced with other parts having similar functions. Any other components or steps may be added. Furthermore, any two or more configurations (features) of the above-mentioned embodiments may be combined. [Explanation of symbols]

[0169] 10. Information processing device 11 Control section D1, D2, D3 Web Articles ed1, ed2, ed3 Additional information N1, N2, N3 Number of empaths V1, V2, V3 evaluation values

Claims

1. On the computer, Acquire additional information capable of verifying the reliability of the first Web articles; Detecting a number of sympathies indicating a number of sympathies to be given to the additional information when the additional information is deemed valid for verifying the reliability, ordering the plurality of first Web articles based on the number of likes; An information processing program that executes processing.

2. The process detects the sympathy given to the additional information included in the additional information group for the first Web articles to which an additional information group in which the additional information is associated is given. The information processing program according to claim 1.

3. The process calculates an evaluation value of the reliability of the first Web articles from the number of sympathies; ordering the plurality of first Web articles based on the evaluation values; 3. The information processing program according to claim 2.

4. The process comprises: a category of the second Web article is assigned as an attribute to each of the additional information of the second Web article that is the subject of the search and includes the first Web article; generating and managing a weighting table in which identification information of the user who gave the sympathy, the category, and a weighting value including a first weighting value and a second weighting value correspond to each other; when the user having the identification information registered in the weighting table gives the sympathy to the additional information having an attribute of the category registered in the weighting table, the number of the given sympathies is weighted by the corresponding weighting value to calculate the evaluation value.

4. The information processing program according to claim 3.

5. The process comprises: a digital signature is assigned to an additional information issuing user who issued the additional information to a third Web article among the plurality of second Web articles, the third Web article being determined to be true through a fact check and the article content being a fact-checked third Web article of a predetermined category; updating the first weighting value corresponding to the identification information of the additional information issuing user and the predetermined category of the third Web article based on the number of the digital signatures; 5. The information processing program according to claim 4.

6. The process comprises: When it is detected that a fourth Web article is browsed by the browsing user among the plurality of second Web articles and the sympathy is added to the additional information associated with the fourth Web article, Calculating a ratio of non-verification times in which the additional information was not verified by the viewing user to the number of times the additional information was obtained by the viewing user; when it is detected that the ratio is equal to or greater than a threshold, updating the second weighting value registered in the weighting table and corresponding to the identification information of the browsing user and the category of the browsed fourth Web article; 5. The information processing program according to claim 4.

7. The process comprises: Among the plurality of second Web articles, a digital signature is assigned to each of a plurality of additional information issuing users who have issued a plurality of first additional information to a third Web article that has been fact-checked and whose article content has been determined to be true and whose article content is a fact-checked third Web article of a predetermined category; updating the number of the digital signatures assigned to each of the additional information issuing users, for the identification information of the additional information issuing users and the first weighting value corresponding to the predetermined category of the third Web article, based on a value obtained by dividing the number of the digital signatures assigned to each of the additional information issuing users by a total number of the first additional information; 5. The information processing program according to claim 4.

8. the process includes transmitting the ordered first Web articles as the search results to a client device that receives the input keywords and outputs search results; The information processing program according to claim 1.

9. Acquire additional information capable of verifying the reliability of the first Web articles; Detecting a number of sympathies that indicates a number of sympathies that are given to the additional information when the additional information is deemed to be valid for verifying the reliability; a control unit that orders the plurality of first Web articles based on the number of likes; An information processing device having the above configuration.

10. A client device; a server device that acquires additional information capable of verifying the reliability of a plurality of first Web articles, detects a number of likes indicating the number of likes given to the additional information when the additional information is deemed valid for verifying the reliability, ranks the plurality of first Web articles based on the number of likes, and transmits the ranked first Web articles to the client device as the search result; An information processing system having the above configuration.

Images