Estimation device and estimation method

Abstract

This estimation device (10) comprises: a user misinformation sharing behavior estimation unit (144) that uses user data acquired on the basis of social network data to estimate misinformation sharing behavior of each user; and a network intervention effect estimation unit (145) that constructs a social network of friendship relations from friend data of the user, and estimates an intervention effect in the social network on the basis of the misinformation sharing behavior of each user.

Description

Estimation device and estimation method

[0001] The present invention relates to an estimation device and an estimation method.

[0002] Social media is widely used by many people as a platform for online information dissemination and communication. However, in recent years, the spread of misinformation on social media has had serious negative effects on a wide range of areas, from politics to public health, including hindering the formation of healthy public opinion, distorting election results, and encouraging ineffective or harmful self-medication practices.

[0003] As a way to address the problem of misinformation on social media, various user intervention techniques are being researched, such as presenting fact-checking articles, media literacy education, and nudges. These user intervention techniques are known to have a certain level of intervention effectiveness at the individual level, such as reducing users' cognitive vulnerability to misinformation (ease with which they are gullible).

[0004] However, whether an individual shares misinformation is generally strongly influenced not only by their own cognitive factors but also by factors such as social relationships between individuals. Therefore, to effectively counter the spread of misinformation, it is necessary to quantitatively evaluate the intervention effect at the group level (e.g., how much the intervention reduces the spread of misinformation within a group) that takes into account social relationships between individuals, rather than just the individual level (e.g., how much the intervention reduces the spread of misinformation within a group), and then design an optimal intervention strategy.

[0005] A technique for evaluating the effectiveness of user intervention techniques at a group level has been proposed (Non-Patent Document 1). In the conventional technique, first, the amount of posts y about a certain misinformation content on social media at time t is calculated. t We build a statistical model to predict the amount of posts related to misinformation, and fit various parameters of the statistical model based on actual social media data. Next, we use numerical simulations to evaluate whether user intervention techniques such as fact-checking, nudges, and account suspensions can affect the amount of posts related to misinformation. tEvaluate how much it reduces

[0006] Joseph B. Bak-Coleman, Ian Kennedy, Morgan Wack, et al., "Combining interventions to reduce the spread of viral misinformation," Nat Hum Behav 6, 1372-1380 (2022).

[0007] However, conventional techniques directly model the volume of misinformation posts across social media, ignoring factors such as the influence of individual users, social relationships, and individual differences in intervention effectiveness. As a result, conventional techniques cannot capture the impact of interventions by micro-users on macro-groups, i.e., the impact of interventions on an individual on the entire group. Therefore, conventional techniques have the drawback of being unable to help design intervention strategies to combat the spread of misinformation.

[0008] The present invention has been made in consideration of the above, and aims to provide an estimation device and an estimation method that can evaluate the spread of misinformation in a group when a certain user is involved.

[0009] In order to solve the above-mentioned problems and achieve the objectives, the estimation device of the present invention is characterized by having a first estimation unit that estimates the misinformation sharing behavior of each user using user data obtained based on social network data, and a second estimation unit that builds a social network of friendships from the user's friend data and estimates the intervention effect in the social network based on the misinformation sharing behavior of each user.

[0010] Furthermore, the estimation method of the present invention is an estimation method executed by an estimation device, and is characterized by including the steps of: estimating the misinformation sharing behavior of each user using user data obtained based on social network data; and constructing a social network of friendships from the user's friend data, and estimating the intervention effect in the social network based on the misinformation sharing behavior of each user.

[0011] According to the present invention, it is possible to evaluate the spread of misinformation in a group when a certain user is involved.

[0012] FIG. 1 is a diagram schematically illustrating an example of the configuration of an estimation device according to an embodiment. FIG. 2 is a diagram illustrating an example of a method for estimating misinformation sharing behavior. FIG. 3-1 is a diagram illustrating an example of output information of the estimation device shown in FIG. 1. FIG. 3-2 is a diagram illustrating an example of output information of the estimation device shown in FIG. 1. FIG. 4 is a flowchart illustrating the processing procedure of estimation processing according to an embodiment. FIG. 5 is a diagram illustrating an example of a computer that realizes the estimation device by executing a program.

[0013] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. Note that the present invention is not limited to this embodiment. In addition, in the description of the drawings, the same parts are designated by the same reference numerals.

[0014] In this embodiment, the social network between users is taken into consideration to estimate how and / or how much misinformation will spread in a social network when a user is involved. A social network is a social network built on the web via social media such as social networking services, electronic bulletin boards, posting sites, video streaming sites, and information sharing sites.

[0015] In the embodiment, when intervention is performed to suppress the spread of misinformation that spreads from user to user on social media, for example, the above estimation can be performed to select users who are effective in suppressing the spread of misinformation. In the embodiment, the intervention effect of the user intervention technique is quantitatively evaluated at the group level, making it possible to select intervening users who will efficiently suppress the spread of misinformation.

[0016] [Estimation Apparatus] An estimation apparatus according to an embodiment will be described below. Fig. 1 is a diagram schematically illustrating an example of the configuration of an estimation apparatus according to an embodiment.

[0017] The estimation device 10 according to the embodiment is realized, for example, by loading a predetermined program into a computer or the like including a ROM (Read Only Memory), a RAM (Random Access Memory), a CPU (Central Processing Unit), etc., and causing the CPU to execute the predetermined program. The estimation device 10 also has a communication interface for transmitting and receiving various information to and from other devices connected via a network, etc. The estimation device 10 is realized by a general-purpose computer such as a workstation or a personal computer.

[0018] The estimation device 10 according to the embodiment includes a data collection unit 11 , a data processing unit 12 , and an estimation processing unit 13 .

[0019] The data collection unit 11 collects social network data, for example, from social media, by communicating with an external device. The social network data is data related to information posted, distributed, or shared on social media, and includes the content posted, distributed, or shared, user information of the source of the posting, distribution, or sharing, information of the destination of the posting, distribution, or sharing, and the conditions for posting, distribution, or sharing. The data collection unit 11 collects data by using an API (Application Programming Interface) or scraping from social media, receiving the data from a company operating the social media, purchasing the data from the company operating the social media, or the like. The data collection unit 11 may collect various information related to users from users.

[0020] The data processing unit 12 acquires user data based on, for example, data collected by the data collection unit 11 (including social network data), converts it into a format (e.g., vector) that can be processed by the estimation unit 14 (described later), and then outputs it to the estimation unit 14.

[0021] Here, the user is a person who is the subject of estimation in the estimation device 10. Alternatively, the user is a person who belongs to the same group as the subject of estimation. The estimation device 10 estimates what kind of misinformation sharing behavior the user will exhibit when exposed to misinformation, and how the misinformation sharing behavior will change when receiving intervention.

[0022] User data is data about a user, and includes, for example, user activity data, user characteristic data, and friend data.

[0023] The user activity data is data indicating the user's past activities. For example, the user's postings and sharing on social media are examples of the user's activity data. The user characteristic data is data indicating the user's interests and political leanings. The friend data is data indicating the user's friendships, and in social media, users who are followed are considered to be friends. The data processing unit 12 classifies the data collected by the data collection unit 11 into, for example, user activity data, user characteristic data, friend data, and social network data.

[0024] The estimation processing unit 13 includes an estimation unit 14 and an estimation result output unit 15 that outputs the estimation result obtained by the estimation unit 14 .

[0025] The estimation unit 14 includes a user activity data unit 141 that stores user activity data, a user characteristic data unit 142 that stores user characteristic data, a social network data unit 143 that stores social network data, a user misinformation sharing behavior estimation unit 144 (first estimation unit), and a network (NW) intervention effect estimation unit 145 (second estimation unit).

[0026] The user misinformation sharing behavior estimation unit 144 estimates the misinformation sharing behavior of a user using user data acquired based on social network data. The user misinformation sharing behavior estimation unit 144 uses a machine learning model to estimate, based on user activity data and / or user characteristic data, what kind of misinformation sharing behavior a user will exhibit when exposed to misinformation and / or how the misinformation sharing behavior will change when receiving intervention.

[0027] The machine learning model used by the user misinformation sharing behavior estimation unit 144 learns, for each user, the user's past activities, the user's characteristics, and the user's misinformation sharing behavior in response to past input misinformation. The machine learning model used by the user misinformation sharing behavior estimation unit 144 receives as input the user to be estimated and the misinformation given to this user, and outputs the user's susceptibility to being deceived in this case.

[0028] Various methods can be applied as a method for the user misinformation sharing behavior estimation unit 144 to estimate misinformation sharing behavior.

[0029] 2 is a diagram illustrating an example of a method for estimating misinformation sharing behavior. For example, the user misinformation sharing behavior estimation unit 144 receives vectorized user data 31 as input and uses machine learning techniques such as a neural network 1441 to learn and build a prediction model that outputs prediction results 32, such as the probability that a user will be deceived by misinformation or the probability that a user will share misinformation (hereinafter simply referred to as vulnerability to misinformation) when a certain intervention is received / not received.

[0030] Another method for estimating misinformation sharing behavior by the user misinformation sharing behavior estimation unit 144 is to conduct a user survey of users with various attributes, identify the main factors that influence users' vulnerability to misinformation, and build a prediction model in which the influencing factors are used as explanatory variables and vulnerability to misinformation is used as a target variable. Major influencing factors include, for example, media literacy ability, political partisanship, nationality, and age.

[0031] The NW intervention effect estimation unit 145 estimates the intervention effect in the social network.

[0032] Generally, it is not economically realistic to intervene on all social media users. Therefore, when intervening on social media, only a small portion of users can be intervened. Therefore, in order to take efficient measures, it is desirable to intervene on users who can most effectively suppress the spread of misinformation on social networks.

[0033] Therefore, the NW intervention effect estimation unit 145 constructs a social network of friendships from the user's friend data. The NW intervention effect estimation unit 145 estimates the intervention effect in the social network based on the user's misinformation sharing behavior in the constructed social network.

[0034] The NW intervention effect estimation unit 145 estimates, based on the misinformation sharing behavior of each user, how much misinformation can be reduced and / or how the prevalence of misinformation will change in the constructed social network when intervention is performed on a specific user or user group, depending on the specific user or user group that performed the intervention. For example, the specific user or user group that performed the intervention is specified by, for example, a countermeasure implementer. The prevalence of misinformation when intervention is performed on a specific user (or user group) is the proportion of users exposed to misinformation in the social network.

[0035] When intervention is performed, the rate at which misinformation spreads and how it spreads changes depending on the user or user group that receives the intervention, making them less susceptible to misinformation. Therefore, the NW intervention effect estimation unit 145 estimates the intervention effect, which indicates how much misinformation can be reduced, based on users' misinformation sharing behavior in the constructed social network.

[0036] Various methods can be applied as a method for estimating the intervention effect in a social network by the NW intervention effect estimation unit 145. For example, the NW intervention effect estimation unit 145 estimates the intervention effect in a social network based on the misinformation sharing behavior of users by, for example, performing a simulation.

[0037] Furthermore, the NW intervention effect estimation unit 145 may perform estimation using an information diffusion model, assuming that misinformation spreads virally among users of a social network according to any information diffusion model (machine learning model). The information diffusion model is a model that estimates the diffusion of information among users of a social network. The NW intervention effect estimation unit 145 may use a method of estimating the prevalence rate of misinformation through theoretical analysis using the information diffusion model or numerical simulation.

[0038] An information diffusion model is a model that defines the mechanism by which information is transmitted from one user to another in a social network. There are various information diffusion models, such as the independent cascade model, linear threshold model, epidemic model, and point process model, but we will not limit ourselves to a specific model here.

[0039] In addition, the network intervention effect estimation unit 145 may use a method of constructing a model that predicts the prevalence rate of misinformation using machine learning techniques such as graph neural networks based on data regarding users' posting and / or sharing history of misinformation content.

[0040] Furthermore, the NW intervention effect estimation unit 145 may estimate users in the social network who will have a high intervention effect, i.e., users whose intervention can significantly contribute to reducing the prevalence of misinformation throughout the social network, and output the estimate to a countermeasure provider. In this case, the NW intervention effect estimation unit 145 estimates users in a predetermined ranking, starting from the top, as users for whom the intervention effect is high, and outputs the estimate to a countermeasure provider. For example, if the estimation result by the user misinformation sharing behavior estimation unit 144 determines that users with a certain characteristic are relatively easily deceived, the NW intervention effect estimation unit 145 may output a user group in the social network who has this specific characteristic. For example, if the number n of users with this specific characteristic is less than a predetermined threshold k, the number of users for whom the intervention effect is high is set to n users, and if the number is equal to or greater than the threshold, the number of users for whom the intervention effect is high is set to the top k users. According to this setting, the NW intervention effect estimation unit 145 estimates the n users or the top k users for whom the intervention effect is high, and outputs the estimate to a countermeasure provider as a user group for whom the intervention effect is high.

[0041] Through the process described above, the estimation device 10 can quantitatively evaluate the effectiveness of suppressing the spread of misinformation when intervention is performed on a specific user (or user group).

[0042] The estimation result output unit 15 processes the estimation results in a format that supports intervention decisions by countermeasure actors (social media, news media, specialized institutions, etc.) who take measures against the spread of misinformation, and outputs the processed results to the countermeasure actors. The estimation device 10 processes the estimation results in a format such as a list of users who contribute significantly to the intervention effect, or a plot of a simulation result of the intervention effect when intervention is performed on a specified user. Using the processed estimation results in this way can help countermeasure actors design user intervention strategies for efficient countermeasures against the spread of misinformation.

[0043] 3A and 3B are diagrams showing examples of output information of the estimation device 10 shown in FIG.

[0044] Figure 3-1 shows an example of a list of the calculation results for each user's contribution to the intervention effect. In the example of Figure 3-1, user "@GEQpT2994" shows the largest contribution to the intervention effect. Therefore, countermeasures can determine that intervening with user "@GEQpT2994" is likely to effectively curb the spread of misinformation.

[0045] Figure 3-2 shows an example of the simulation results of the intervention effect. Figure 3-2 shows the simulation results of the prevalence of misinformation when users "@DaYVS9617" and "@NaEyL4036" are specified as intervention targets (Input) in a group of 10 people.

[0046] In the output example shown in FIG. 3-2, icons 411 and 412 represent users "@DaYVS9617" and "@NaEyL4036" who intervened. Icons 421, 422, and 423 represent users who shared the misinformation. In this example, three out of ten users shared the misinformation, so the prevalence rate is σ=0.3.

[0047] In this way, by using the estimation results of the estimation device 10, countermeasure personnel can determine what kind of intervention they should take and to whom in order to effectively suppress the spread of false information.

[0048] [Estimation Process] FIG. 4 is a flowchart showing the processing procedure of the estimation process according to the embodiment.

[0049] The data collection unit 11 collects various data including social network data from social media etc. (step S1). The data processing unit 12 processes the data (including social network data) collected by the data collection unit 11 (step S2), for example, and acquires user data including user activity data and user characteristics.

[0050] The user misinformation sharing behavior estimation unit 144 estimates the user's misinformation sharing behavior using user data acquired from the social network data (step S3). Subsequently, the NW intervention effect estimation unit 145 constructs a social network of friendships from the user's friend data and estimates the intervention effect in the social network based on the user's misinformation sharing behavior estimated in step S3 (step S4).

[0051] Then, the estimation result output unit 15 processes the estimation result into a form that will support the countermeasure taker's decision to intervene, and outputs the processed result to the countermeasure taker (step S5).

[0052] [Effects of the embodiment] Conventional user intervention techniques for countering misinformation, such as nudges and media literacy education, have only established methods for measuring the effectiveness of intervention at the user level, such as how less susceptible users are to misinformation.

[0053] In contrast, the estimation device 10 according to the embodiment estimates a user's misinformation sharing behavior using user data acquired from social network data. The estimation device 10 then constructs a social network of friendships from the user's friend data and estimates the intervention effect in the social network based on the user's misinformation sharing behavior. In other words, the estimation device 10 performs two-stage estimation: estimating each user's misinformation sharing behavior and estimating the intervention effect in the social network.

[0054] As a result, the estimation device 10 can quantitatively evaluate the intervention effect of user intervention techniques at the group level. That is, the estimation device 10 can quantitatively evaluate the spread of misinformation when an intervention is made for a certain user in a group. In other words, the estimation device can quantitatively estimate how much the spread of misinformation will change depending on what kind of intervention is made to whom in a group such as a social media community or network.

[0055] By using the estimation results from this estimation device 10, countermeasure providers such as social media, news media, and specialized institutions that take measures to prevent the spread of misinformation can select intervening users to efficiently suppress the spread of misinformation.

[0056] Therefore, by explicitly considering the social networks of users, the estimation device 10 can evaluate how and / or to what extent misinformation will spread if intervention is performed on a certain user. This makes it possible to design an efficient user intervention strategy, such as selecting the optimal user to intervene in order to suppress the spread of misinformation.

[0057] [System Configuration of the Embodiment] The estimation device 10 is a functional concept and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of the functions of the estimation device 10 is not limited to that shown in the figure, and all or part of the functions can be functionally or physically distributed or integrated in any unit depending on various loads, usage conditions, etc.

[0058] Furthermore, all or any part of the processes performed in the estimation device 10 may be realized by a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), and a program analyzed and executed by the CPU and the GPU (Graphics Processing Unit). Furthermore, each process performed in the estimation device 10 may be realized as hardware using wired logic.

[0059] Furthermore, among the processes described in the embodiments, all or part of the processes described as being performed automatically can be performed manually. Alternatively, all or part of the processes described as being performed manually can be performed automatically using a known method. In addition, the processing procedures, control procedures, specific names, and information including various data and parameters described above and illustrated can be changed as appropriate unless otherwise specified.

[0060] 5 is a diagram showing an example of a computer in which the estimation device 10 is realized by executing a program. The computer 1000 has, for example, a memory 1010 and a CPU 1020. The computer 1000 also has a hard disk drive interface 1030, a disk drive interface 1040, a serial port interface 1050, a video adapter 1060, and a network interface 1070. These components are connected by a bus 1080.

[0061] The memory 1010 includes a ROM 1011 and a RAM 1012. The ROM 1011 stores a boot program such as a BIOS (Basic Input Output System). The hard disk drive interface 1030 is connected to a hard disk drive 1090. The disk drive interface 1040 is connected to a disk drive 1100. A removable storage medium such as a magnetic disk or optical disk is inserted into the disk drive 1100. The serial port interface 1050 is connected to a mouse 1110 and a keyboard 1120, for example. The video adapter 1060 is connected to a display 1130, for example.

[0062] The hard disk drive 1090 stores, for example, an operating system (OS) 1091, an application program 1092, a program module 1093, and program data 1094. That is, a program that defines each process of the estimation device 10 is implemented as a program module 1093 in which code executable by the computer 1000 is written. The program module 1093 is stored, for example, in the hard disk drive 1090. For example, a program module 1093 for executing processes similar to those of the functional configuration of the estimation device 10 is stored in the hard disk drive 1090. Note that the hard disk drive 1090 may be replaced with an SSD (Solid State Drive).

[0063] Furthermore, setting data used in the processing of the above-described embodiment is stored as program data 1094, for example, in memory 1010 or hard disk drive 1090. Then, CPU 1020 reads out program module 1093 or program data 1094 stored in memory 1010 or hard disk drive 1090 into RAM 1012 as necessary and executes them.

[0064] The program module 1093 and program data 1094 may not necessarily be stored in the hard disk drive 1090, but may also be stored in a removable storage medium and read by the CPU 1020 via the disk drive 1100 or the like. Alternatively, the program module 1093 and program data 1094 may be stored in another computer connected via a network (such as a local area network (LAN) or a wide area network (WAN)). The program module 1093 and program data 1094 may then be read by the CPU 1020 from the other computer via the network interface 1070.

[0065] Although the present invention has been described above as an embodiment, the present invention is not limited to the descriptions and drawings that form part of the disclosure of the present invention. In other words, other embodiments, examples, and operational techniques that can be made by those skilled in the art based on the present invention are all included in the scope of the present invention.

[0066] REFERENCE SIGNS LIST 10 Estimation device 11 Data collection unit 12 Data processing unit 13 Estimation processing unit 14 Estimation unit 15 Estimation result output unit 31 User data 32 Prediction result 141 User activity data 142 User characteristic data 143 Social network data 144 User misinformation sharing behavior estimation unit 145 Network intervention effect estimation unit

Claims

1. An estimation device comprising: a first estimation unit that estimates each user's misinformation sharing behavior using user data acquired based on social network data; and a second estimation unit that builds a social network of friendships from the user's friend data and estimates the intervention effect in the social network based on each user's misinformation sharing behavior.

2. The estimation device described in claim 1, characterized in that the first estimation unit uses a machine learning model to estimate what kind of misinformation-sharing behavior a target user will exhibit when exposed to misinformation and / or how misinformation-sharing behavior will change when the target user receives intervention, based on user activity data indicating the past activities of each user and / or user characteristic data indicating the interests and political bias of each user.

3. The estimation device described in claim 1, characterized in that the second estimation unit estimates, based on the misinformation sharing behavior of each user, how much misinformation can be reduced in the social network when intervention is made with a specific user or user group, and / or how the prevalence rate of the misinformation will change depending on the specific user or user group that made the intervention.

4. An estimation method executed by an estimation device, comprising: a step of estimating the misinformation sharing behavior of each user using user data acquired based on social network data; and a step of constructing a social network of friendships from the user's friend data and estimating the intervention effect in the social network based on the misinformation sharing behavior of each user.

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