JP2023121070A - Information processor, information processing system, and method for processing information - Google Patents

Abstract

To provide an information processor, an information processing system, and a method for processing information which can precisely and efficiently extract seemingly reliable information from a large volume of information.SOLUTION: The information processor includes a processor. The processor collects subjective information posted from an information sender through a network, analyzes the collected subjective information and calculates the reliability of evaluating the reliability of the content of the information by using an analysis rule for analyzing the subjective information, and outputs the content of the collected subjective information and the calculated reliability. The analysis rule includes a plurality of different determination conditions. The processor makes a gradual determination using the determination conditions to calculate the reliability of the objective information.SELECTED DRAWING: Figure 1

Description

The present invention relates to an information processing device, an information processing system, and an information processing method.

In order to deal with disasters, it is important to accurately recognize events and their related situations. For example, Patent Document 1 discloses an estimation system that collects natural language information posted to SNS within a unit time and estimates the occurrence of an earthquake or the like based on the number of occurrences of words included in the natural language information. ing.

JP 2019-144905 A

As an example, information transmitted by social networking services (SNS) may contain false rumors, rumors, and misinformation. Here, with the technique of Patent Literature 1, for example, it is not easy to accurately and efficiently extract useful information from a huge amount of information in the confusion of an emergency.

SUMMARY OF THE INVENTION Accordingly, it is an object of the present invention to provide an information processing apparatus, an information processing system, and an information processing method capable of accurately and efficiently extracting information considered to be highly reliable from a vast amount of information. .

According to a first aspect of the present invention, the following information processing device is provided. That is, the information processing device includes a processor. A processor collects subjective information posted by an information sender via a network. The processor analyzes the collected subjective information using an analysis rule for analyzing subjective information to calculate a reliability that evaluates the reliability of the content of the information. The processor outputs the content of the collected subjective information and the calculated reliability. The analysis rule then includes a plurality of different determination conditions. The processor calculates the reliability of the subjective information by performing stepwise determination using a plurality of determination conditions.

According to a second aspect of the present invention, the following information processing system is provided. That is, the information processing system includes an information processing device, and an information system connected to the information processing device via a network and in which an information sender posts subjective information. The information processing device includes a processor. The processor collects subjective information posted by information senders from the information system. The processor analyzes the collected subjective information using an analysis rule for analyzing subjective information to calculate a reliability that evaluates the reliability of the content of the information. The processor outputs the content of the collected subjective information and the calculated reliability. The analysis rule then includes a plurality of different determination conditions. The processor calculates the reliability of the subjective information by performing stepwise determination using a plurality of determination conditions.

According to a third aspect of the present invention, the following information processing method is provided. That is, this method is a method performed using a processor. The information processing method uses an information collection process of collecting subjective information posted by an information sender via a network, and an analysis rule for analyzing the subjective information, and analyzes the collected subjective information to obtain information. An information analysis step of calculating a reliability for evaluating the reliability of content, and an information output step of outputting the content of the collected subjective information and the calculated reliability. The analysis rule then includes a plurality of different determination conditions. In the information analysis process, stepwise determination is performed using a plurality of determination conditions, and the reliability of subjective information is calculated.

ADVANTAGE OF THE INVENTION According to this invention, the information processing apparatus, information processing system, and information processing method which can extract information considered highly reliable from a huge amount of information accurately and efficiently are provided. Problems, configurations, and effects other than those described above will be clarified by the following description of the mode for carrying out the invention.

It is the figure which showed the system configuration example of this invention. It is a figure which shows an example of the hardware constitutions of a quality assurance apparatus. It is the figure which showed an example of the information processing method. It is the figure which showed an example of the content of the collection database. It is the figure which showed an example of the content of an analysis result database. It is the figure which showed an example of the content of an information originator database. FIG. 4 is a diagram showing an example of the content of a collection rule; FIG. 4 is a diagram showing an example of the contents of an analysis rule; It is the figure which showed an example of an information collection process. It is the figure which showed an example of an information analysis process. It is a flow chart which shows an example of reliability evaluation. FIG. 11B is a continuation of the process of FIG. 11A and is a flowchart showing an example of reliability evaluation. FIG. 11B is a continuation of the process of FIG. 11B and is a flowchart showing an example of reliability evaluation. It is the figure which showed an example of the information display process. It is a figure showing an example of a maintenance process.

Embodiments of the present invention will be described below with reference to the drawings. The embodiments are examples for explaining the present invention, and are appropriately omitted and simplified for clarity of explanation. The present invention can also be implemented in various other forms. Unless otherwise specified, each component may be singular or plural.
The position, size, shape, range, etc. of each component shown in the drawings may not represent the actual position, size, shape, range, etc., in order to facilitate understanding of the invention. As such, the present invention is not necessarily limited to the locations, sizes, shapes, extents, etc., disclosed in the drawings.
Examples of various types of information are described using expressions such as “table”, “list”, and “queue”, but various types of information may be expressed in data structures other than these. For example, when describing identification information, expressions such as "identification information", "identifier", "name", "ID", and "number" are used, but these can be replaced with each other.

<First embodiment>
1st Embodiment demonstrates the case where disaster response is performed using the quality assurance apparatus 101 (information processing apparatus). FIG. 1 is a diagram showing a system configuration example. As illustrated, the quality assurance system (information processing system) is a system that collects and manages information on disasters and uses it for disaster response. As an example, this system can be configured to include a quality assurance device 101 and an information system 120 . Here, the quality assurance device 101 collects disaster information via a network 150 such as the Internet and analyzes it into information that contributes to situational awareness. , and to provide effective information to take action according to the situation.

The information system 120 provides information such as SNS to the quality assurance device 101 . In addition, this system may acquire information from an appropriate information source, or may acquire information from an information source 130 based on sensor data or an information source 140 such as a disaster prevention system owned by a disaster-related organization. .

There may be multiple information systems 120, multiple information sources 130, and multiple information sources 140, respectively. The types of information transmitted/received via the network 150 include text information, position information, time information, audio information, images, moving images, and additional information related to the sender of the information.

A representative software configuration of the quality assurance device 101 will be described with reference to FIG. The quality assurance device 101 includes a data collection unit 102, a data analysis unit 103, an information display unit 104, a maintenance unit 105, a rule DB 106, a collection DB 107, an information originator DB 108, and an analysis result DB 109.

The data collection unit 102 includes an information system 120 that provides information such as SNS, information sources 130 such as seismographs and river water level gauges, information media such as news reports and the Internet, and information sources such as disaster prevention systems owned by disaster prevention organizations. 140 or the like based on the collection rule 110 stored in the rule DB 106 and stores it in the collection DB 107 . Here, the collection rule 110 describes information necessary for data collection, such as collection frequency, collection area, collection keywords, collection targets, and the like.

The data analysis unit 103 calculates the credibility (that is, the reliability indicating the reliability of the content of the information) for the data stored in the collection DB 107 based on the analysis rule 111 stored in the rule DB 106, and calculates the credibility. to the information of the information system 120 collected by the data collection unit 102 and registered in the analysis result DB 109 . Here, in the analysis rule 111, a factor (e.g., "earthquake") that affects the content of the post (e.g., "water outage") around the time the SNS is posted is included in the collected objective information (information sources 130, 140). ), whether there are more than a threshold number of similar posts from places geographically close to the time when the information was posted, and the attribute of the information sender. Are listed.

In addition, the data analysis unit 103 stores the information of the information sender in the information sender DB 108 assuming that the information sender determined to be highly reliable is "a person who has a track record of providing useful disaster information". . In this embodiment, the information in the information originator DB 108 is also used for reliability determination.

Based on the data stored in the analysis result DB 109, the information display unit 104 displays a list of information, such as SNS, to which the user 160 has been given reliability.

The maintenance unit 105 creates collection rules 110 describing data collection destinations and collection frequencies used by the data collection unit 102, collection keywords, collection targets, etc., and analysis rules 111 describing analysis algorithms used by the data analysis unit 103. It is created based on the input of the user 160 and stored in the rule DB 106 .

The collection rules 110 and analysis rules 111 of the rule DB 106 and the information senders of the information sender DB 108 may be added, updated, or deleted by the maintenance unit 105 during operation.

The quality assurance device 101 continuously collects and monitors information during normal times, and uses the collected data to detect abnormalities immediately after the occurrence of a critical event such as a disaster and to provide the user 160 with the transition of the situation. can be done.

Next, an example of the hardware configuration of the quality assurance device will be described. As shown in FIG. 2, the quality assurance device 101 includes a processor 171, a storage device 172, and a communication IF 173. The processor 171 functions as a subject of data processing. The storage device 172 stores the software configuration (102-109) described above. The communication IF 173 is an interface and used for communication via the network 150 .

The processor 171 is configured by a CPU (Central Processing Unit) as an example, but may be configured by other semiconductor devices. The storage device 172 can be configured using an auxiliary storage device and a main storage device. Examples of auxiliary storage devices include HDDs (Hard Disk Drives) and SDDs (Solid State Drives). An example of the main storage device is a RAM (Random Access Memory). The processor 171 reads the program into the main storage device and executes it in processing. The quality assurance device 101 may also include an input device 174 used by the user 160 to input information, and a display device 175 for presenting information to the user 160 by displaying output data on the screen.

Next, an example of the information processing method in this embodiment will be described. FIG. 3 is a diagram showing an example of an information processing method according to this embodiment. The information processing method mainly includes an information collection process 201 , an information analysis process 202 , an information display process 203 (information output process), and a maintenance process 204 .

In the information collection step 201, information is collected from the information system 120 and information sources (130, 140), and the collected information is stored in the collection DB 107. FIG.

In the information analysis process 202 , information is analyzed using the data in the collection DB 107 processed and stored in the information collection process 201 , and the analyzed result is stored in the analysis result DB 109 .

In the information display step 203 , information is provided to the user 160 using the data in the analysis result DB 109 processed and stored in the information analysis step 202 .

In the maintenance process 204 , the rule DB 106 and the information sender DB 108 are updated based on the input from the user 160 . The rule DB 106 and information originator DB 108 are used in the information collection process 201 and the information analysis process 202 .

Next, the contents of each database in this embodiment will be described. First, the contents of the collection DB 107 will be described with reference to FIG. FIG. 4 is a diagram showing an example of the contents of the collection DB 107 in this embodiment.

As shown in FIG. 4, the collection DB 107 includes an information system 120 that provides information such as SNS collected by the data collection unit 102, information sources 130 such as seismometers and river water level gauges, and information media such as news reports and the Internet. , and information about information sources 140 such as disaster prevention systems owned by each disaster prevention organization. As an example, this DB includes data ID 401 for identifying data, acquisition time 402, sender information (system name, SNS information sender, sensor type, etc.) 403 regarding information sources, location information (for example, latitude/longitude) 404, collection category (SNS, security system, information media, sensor device, etc.) 405, text 406, which is the main body of data, link destination 407 of text, link destination 408 of image information, link destination 409 of audio information, link of video information It contains destination 410 and is stored as records 411 and 412 .

Since the collection DB 107 accumulates a huge amount of information, it is desirable to efficiently and effectively accumulate data by eliminating redundant data, deleting/compressing data according to reference frequency, and the like. Here, it is not necessary to register values for all the items of the information collected by the data collection unit 102, and items without corresponding values may be registered with blanks (for example, if the image information has a link destination). If not, the image information link 408 may be left blank for registration). Also, when a word related to location information such as a place name is extracted from the text 406, it may be registered in the location information 404 of the record.

Next, the contents of the analysis result DB 109 will be described with reference to FIG. FIG. 5 is a diagram showing an example of the content of the analysis result DB 109 in this embodiment.

A reliability calculated by the analysis rule 111 is assigned to the record registered in the collection DB 107 and having the collection classification 405 of “SNS”, and the result is registered in the analysis result DB 109 . In the example of FIG. 5, the analysis result DB 109 includes data ID 501 transcribed from the collection DB 107, acquisition time 502, information originator 503, location information 504, body text 506, text link destination 507, and image information link destination 508. , a link destination 509 of audio information, a link destination 510 of video information, a calculated reliability 505, and an information source 511 used to determine the reliability, and are stored as records 512 and 513 . As in FIG. 4, items that do not have corresponding values may be registered as blanks. The reliability of all records or part of the records may be reviewed based on subsequent information collected in the information collection step 201 .

Next, the contents of the information originator DB 108 will be described with reference to FIG. FIG. 6 is a diagram showing an example of the contents of the information originator DB 108 in this embodiment.

An SNS information sender determined to be highly reliable by the data analysis unit 103 is registered in the information sender DB 108 as "a person who has a track record of providing useful disaster information." As shown in FIG. 6, the information originator DB 108 includes, for example, a user ID 601, a screen name 602, a user name 603, a current location 604, a URL 605, a self-introduction sentence 606, presence/absence of an authenticated account 607, a number of followers 608, and a number of followers. 609, the number of lists 610, and the total number of tweets 611, stored as records 612 and 613. The number of followers, followers, lists, and all tweets are the number at the time of data acquisition. Each record in the information originator DB 108 may be added, updated, or deleted by the user 160 during operation.

Next, the contents of the collection rule 110 in the rule DB 106 will be described with reference to FIG. FIG. 7 is a diagram showing an example of the contents of collection rules in this embodiment.

The data collection unit 102 collects information based on collection rules 110 stored in the rule DB 106 . As shown in FIG. 7, the rule DB 106 includes, for example, a collection rule ID 701 for identifying a rule, collection frequency (seconds, minutes, hours, etc.) 702, collection area (prefecture/city, latitude/longitude) Range specification etc.) 703, collection targets (URLs and IP addresses of information sources such as information systems such as SNS, sensors of seismometers and river water gauges, information media, disaster prevention systems, etc.) 704, collection keywords related to disasters 705, Narrow down to public users (yes, no) 706 whether to narrow down information senders to public users, narrow down to real users (yes, no) 707 whether to narrow down to real users, number of followers Number of followers (more than, less than, less than, etc.) 708 whether to narrow down by the number of, limited to tweets with photos (Yes, No) 710, tweets with videos Limit to tweets with video (Yes, No) 711, whether to limit to tweets with sound (Yes, No) 712, whether to enable this record (rule) It contains valid/invalid (valid, invalid) 713 as to whether to invalidate, and is stored as records 714 and 715 . A plurality of collection areas 703, collection targets 704, and disaster-related collection keywords 705 can be specified. Each record of the collection rule 110 may be added, updated, or deleted by the user 160 during operation.

Next, the contents of the analysis rule 111 in the rule DB 106 will be described with reference to FIG. FIG. 8 is a diagram showing an example of the contents of analysis rules in this embodiment.

The data analysis unit 103 calculates credibility (reliability) of collected information based on analysis rules 111 stored in the rule DB 106 . The rule DB 106 includes, for example, an analysis rule ID 801 for identifying rules, a rule priority 802, an analysis algorithm 803 for determining reliability, a threshold value 804 for determination, and an analysis for setting processing after analysis algorithm evaluation. Contains algorithm evaluation (if true) 805, (if false) 806, enable/disable (valid, invalid) 807 whether to enable or disable this record (rule), like records 808, 809 Stored. Note that, in this example, the determination of reliability is executed in the order from the lowest value of the priority 802 . Depending on the contents of the analysis algorithm 803, the threshold 804 may be blank. Each record of the analysis rule 111 can be edited by the user 160. For example, the user 160 can change the order of priority, add, update, or delete records during operation.

Next, each step in the information processing method described above will be specifically described. First, the information gathering process will be explained. FIG. 9 is a diagram showing an example of the information gathering process in this embodiment. In information gathering step 201 , processor 171 executes data gathering section 102 .

The quality assurance device 101 uses a data collection unit 102 to provide information systems 120 that provide information such as SNS, information sources 130 such as seismographs and river water level gauges, information media such as news reports and the Internet, and disaster prevention related organizations. Information is collected from an information source 140 such as a disaster prevention system based on the collection frequency, collection area, collection keyword, collection target, etc. described in the collection rule 110 stored in the rule DB 106 , and stored in the collection DB 107 .

Next, the information analysis process will be explained. FIG. 10 is a diagram showing an example of the information analysis process in this embodiment. In the information analysis step 202 , the processor 171 executes the data analysis section 103 .

The quality assurance device 101 uses the data analysis unit 103 to analyze (reliability evaluation) the SNS posts based on the analysis rules 111 registered in the rule DB 106 using each data record accumulated in the collection DB 107 . After analysis, the SNS post is given a degree of reliability and the like, and stored in the analysis result DB 109 . An SNS information sender determined to be a highly reliable post by the data analysis unit 103 is stored in the information sender DB 108 as "a person who has a track record of providing useful disaster information." The sender information stored in the information sender DB 108 is used for calculating the reliability.

The information collection process 201 and the information analysis process 202 are repeatedly executed according to the collection frequency described in the collection rule 110 . Even if the reliability is evaluated as "highly reliable" in the information analysis step 202, the subsequent information collected in the information collection step 201 may cause the reliability to be reconsidered.

Here, an example of reliability evaluation performed in the information analysis process will be described with reference to FIG. 11 . 11A to 11C are flowcharts showing an example of reliability evaluation performed in the information analysis process in this embodiment.

For example, the quality assurance device 101 evaluates the reliability of each data record stored in the collection DB 107 and having the collection classification of “SNS”. Evaluation criteria are based on analysis rules 111 . In this embodiment, the reliability is calculated by a plurality of stepwise determinations using the description of the analysis algorithm 803 and analysis algorithm evaluation. In the example of FIG. 11, first, it is determined whether the posted content of the record is primary information (original information serving as an information source) (S501). If the information is not primary information ("NO" in S501), the reliability is determined to be "low" (S502), and the process proceeds to S513, which will be described later.

If the information is primary information ("YES" in S501), the quality assurance device 101 determines that the collection DB 107 contains negative information such as false information or false alarms for the record that is equal to or greater than the threshold. It is determined whether there is any (S503). Based on the value indicated by the threshold 804, if there is more negative information than the threshold ("NO" in process 503), the reliability is determined to be "low" (S502), and the process proceeds to S513, which will be described later. do. On the other hand, if there is no negative information above the threshold ("YES" in S503), the factor affecting the record is in the objective information in the collection DB 107 (collection classification is crime prevention system, information media , sensor device, etc.) (S504). That is, in S504, the relevance between the posted content and the objective information is evaluated.

In S504, if there is objective information ("YES" in S504), the quality assurance device 101 determines that the reliability is "high" (S511), and the process proceeds to S513, which will be described later. On the other hand, if there is no objective information ("NO" in S504), it is determined whether or not there are more than a threshold number of similar posts near the time the record was posted and from a location geographically close to the location information of the record. (S505).

In S505, if there are similar posts equal to or greater than the threshold value based on the value indicated by the threshold value 804 ("YES" in S505), the quality assurance device 101 determines that the reliability is "high" (S511), and performs processing. moves to S513. On the other hand, if there are no more similar posts than the threshold ("NO" in S505), it is determined whether the information originator of the record is a public user or a user registered with a real name (S506).

In S506, if the information sender is a public user or a user registered under a real name ("YES" in S506), the quality assurance device 101 determines that the reliability is "high" (S511), The process moves to S513. On the other hand, if the information sender is not a public user or a user registered with a real name ("NO" in S506), it is determined whether the information sender is registered in the information sender DB 108 (S507).

In S507, if registered in the information sender DB 108 ("YES" in process 507), the quality assurance device 101 determines the reliability as "medium" (S512), and the process proceeds to S513. On the other hand, if it is not registered in the information originator DB 108 ("NO" in S507), it is determined whether an image or video has been posted to the record (S508).

In S508, if no image or video has been posted ("NO" in S508), the reliability is determined to be "low" (S502), and the process proceeds to S513. On the other hand, if an image or video has been posted ("YES" in S508), an image or video search is executed on the Internet (S509), and it is determined whether or not there is any relevant one in the past (S510). .

In S510, if there is a corresponding one ("NO" in S510), the quality assurance device 101 determines that the reliability is "low" (S502), and the process proceeds to S513. On the other hand, if there is no applicable item ("YES" in S510), the reliability is determined to be "medium" (S512), and the process proceeds to S513.

The quality assurance device 101 assigns the determined reliability and the information source used for the determination to the record and registers it in the analysis result DB 109 (S513). Here, when the reliability is "low", the information source used to determine the reliability is blank.

Finally, the quality assurance device 101 determines the reliability calculated in the process (S514), and if the reliability is "high" ("YES" in S514), the information sender of the record The person is registered in the person DB 108 as "a person who has a track record of providing useful disaster information" (S515), and the process ends. On the other hand, if the reliability is other than "high" ("NO" in S514), the process ends there.

Next, the information display process will be described. FIG. 12 is a diagram showing an example of the information display process in this embodiment. In the information display step 203 , the processor 171 executes the information display section 104 .

The quality assurance device 101 provides the records of the analysis result DB 109 accumulated in the information analysis step 202 to the user 160 via the information display section 104 . That is, data in the analysis result DB 109 is output to the display device 175 .

Next, the maintenance process will be explained. FIG. 13 is a diagram showing an example of a maintenance process according to this embodiment. In the maintenance process 204 , the processor 171 executes the maintenance section 105 .

As an example, the quality assurance device 101 always collects data in normal times based on the collection rule 110 and registers the data in the collection DB 107 . The data registered in the collection DB 107 are analyzed based on the analysis rule 111 and registered in the analysis result DB 109 . By creating and changing each rule by the user 160, the amount and quality of data can be improved, and quicker and more effective measures can be taken. In addition to the rules, the user 160 can also add, update, and delete the contents of the information sender DB 108 used for credit evaluation during operation.

According to this embodiment, it is possible to accurately and efficiently extract information (subjective information) that is considered to be highly reliable from a huge amount of information. As an example, subjective information on SNS, supplementary information of information senders, sensor devices such as seismometers and river water level gauges, information media such as news reports and the Internet, and information sources such as disaster prevention systems owned by disaster prevention organizations By multiplying objective information by , it is possible to more accurately and efficiently extract information that is considered to be highly reliable from a huge amount of information.

According to the present embodiment, as an example, even in situations where an unexpected event occurs, a person responding to an event such as a disaster minimizes the impact of false alarms and rumors, and is concerned about the occurrence and occurrence. It is possible to quickly and continuously collect information about events that occur, and to conduct effective situational awareness. In other words, the quality assurance device 101 can accurately and efficiently extract information considered to be highly reliable in an emergency such as a disaster.

Although the embodiments of the present invention have been described in detail above, the present invention is not limited to the above-described embodiments, and various designs can be made without departing from the spirit of the invention described in the claims. Changes can be made. For example, the above-described embodiments have been described in detail in order to explain the present invention in an easy-to-understand manner, and are not necessarily limited to those having all the described configurations. It is possible to add, delete, or replace part of the configuration of the embodiment with another configuration.

The flow of reliability evaluation executed in the information analysis step 202 shown in FIG. 11 may be changed as appropriate according to changes in the analysis rule 111 shown in FIG.

The mode of information display by the information display unit 104 is not particularly limited. For example, display using characters, numerical values, graphs, symbols, and the like may be performed.

The information display unit 104 may output to a device different from the display device 175 . For example, the data of the analysis result DB 109 is output via the network 150 to a terminal device owned by a worker (user) who responds at a disaster site, and the terminal device displays the data, thereby providing information to the worker. may be presented.

The quality assurance device 101 may be arranged on the cloud. This makes it possible to manage data on the cloud. In this case, the input device 174 and the display device 175 may be omitted, and input and output are performed via communication.

In the present embodiment, an example of processing information posted on an SNS as subjective information has been specifically described, but the present invention is not limited to this example. Subjective information may be collected and processed.

The quality assurance device 101 may collect objective information from a configuration different from the sensors and systems described above. In the embodiment, a seismograph and a river water level gauge were exemplified, but objective information from other different disaster prevention countermeasure sensors may be collected. In addition, the quality assurance device 101 may process other types of disaster information other than earthquakes, water leaks, etc., and the user 160 can appropriately edit each rule.

DESCRIPTION OF SYMBOLS 101... Quality assurance apparatus, 102... Data collection part, 103... Data analysis part, 104... Information display part, 105... Maintenance part, 106... Rule DB, 107... Collection DB, 108... Information originator DB, 109... Analysis result DB, 110... Collection rule, 111... Analysis rule, 120... Information system (SNS, etc.), 130... Information source (sensor, etc.), 140... Information source (disaster prevention system, etc.), 150... Network, 160... User

Claims (15)

with a processor
The processor
Through the network, collect subjective information posted by information senders,
Analyzing the collected subjective information using an analysis rule for analyzing the subjective information and calculating a reliability for evaluating the reliability of the content of the information;
Outputting the content of the collected subjective information and the calculated reliability,
The analysis rule is
each containing multiple different criteria,
The processor
Calculate the reliability of the subjective information by performing stepwise determination using the plurality of determination conditions,
An information processing device characterized by: The information processing device according to claim 1,
wherein the analysis rule is user editable;
An information processing device characterized by: The information processing device according to claim 2,
A user can edit the order of making judgments based on the judgment conditions,
An information processing device characterized by: The information processing device according to claim 1,
The above plurality of judgment conditions include:
whether the information posted by the information sender is primary information;
Whether or not there is more than a threshold amount of negative information in the collected subjective information that the posted subjective information is false or misinformation;
whether or not there are factors in the objective information collected via the network that affect the subjective information posted;
Whether or not there are more than a threshold number of similar posts from geographically close locations near the posting time of the subjective information,
whether the information sender is registered as a public user or as a real name;
whether the information sender has a track record of providing useful disaster information in the past;
If the subjective information posted includes images or videos, the past postings are not reused,
including at least two of the conditions of
An information processing device characterized by: The information processing device according to claim 4,
The processor
Collect the objective information from sensor devices used for disaster prevention, information media by news or the Internet, disaster prevention systems owned by disaster prevention related organizations,
An information processing device characterized by: an information processing device;
an information system connected to the information processing device via a network and in which an information sender posts subjective information;
The information processing device is
with a processor
The processor
Collect subjective information posted by the information sender from the information system,
Analyzing the collected subjective information using an analysis rule for analyzing the subjective information and calculating a reliability for evaluating the reliability of the content of the information;
Outputting the content of the collected subjective information and the calculated reliability,
The analysis rule is
each containing multiple different criteria,
The processor
Calculate the reliability of the subjective information by performing stepwise determination using the plurality of determination conditions,
An information processing system characterized by: The information processing system according to claim 6,
The analysis rule can be edited by a user who operates the information processing device.
An information processing system characterized by: The information processing system according to claim 7,
A user who operates the information processing device can edit the order of making determinations based on the determination conditions.
An information processing system characterized by: The information processing system according to claim 6,
The above plurality of judgment conditions include:
whether the information posted by the information sender is primary information;
Whether or not there is more than a threshold amount of negative information in the collected subjective information that the posted subjective information is false or misinformation;
whether or not there are factors in the objective information collected via the network that affect the subjective information posted;
Whether or not there are more than a threshold number of similar posts from geographically close locations near the posting time of the subjective information,
whether the information sender is registered as a public user or as a real name;
whether the information sender has a track record of providing useful disaster information in the past;
If the subjective information posted includes images or videos, the past postings are not reused,
including at least two of the conditions of
An information processing system characterized by: The information processing system according to claim 9,
An information source connected to the information processing device via a network and acquiring the objective information,
Said information sources are:
Acquire the objective information from sensor devices used for disaster prevention, information media by news or the Internet, disaster prevention systems owned by disaster prevention related organizations,
The processor
gathering said objective information from said information sources;
An information processing system characterized by: The information processing system according to claim 6,
A display device that outputs the content of the collected subjective information and the calculated reliability to the user who operates the information processing device,
An information processing system characterized by: The information processing system according to claim 6,
Equipped with a terminal device used by a user who responds at the disaster site,
The processor
Outputting the content of the collected subjective information and the calculated reliability to the terminal device;
An information processing system characterized by: The information processing system according to claim 6,
The information processing device is arranged on the cloud,
An information processing system characterized by: An information processing method using a processor,
an information collection step of collecting subjective information posted by an information sender via a network;
an information analysis step of analyzing the collected subjective information using an analysis rule for analyzing the subjective information and calculating a reliability for evaluating the reliability of the content of the information;
an information output step of outputting the content of the collected subjective information and the calculated reliability;
with
The analysis rule is
each containing multiple different criteria,
In the information analysis step,
The reliability of the subjective information is calculated by performing stepwise determination using the plurality of determination conditions,
An information processing method characterized by: A program that causes a processor to execute the information processing method according to claim 14 .

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