Risk detection method and device, computer readable storage medium, electronic equipment and computer program product

Abstract

The embodiment of the invention discloses a risk detection method and device, a computer readable storage medium, electronic equipment and a computer program product. The risk detection method comprises the steps that after target content needing to be published by a user is obtained, the target content is published; according to the embodiment of the invention, the risk degree of the user determined by the user historical data generated based on the historical content publishing behavior of the user can be obtained, so that the risk detection strategy of which the strict degree is in positive correlation with the risk degree of the user during risk detection is utilized to carry out risk detection processing on the target content required to be published by the user; and obtaining a risk detection result of the target content.

Description

Technical Field

[0001] The present invention relates to the field of risk detection technology, and in particular to a risk detection method, device, computer-readable storage medium, electronic device, and computer program product. Background Art

[0002] With the increasing demand for interaction and communication and the development and progress of science and technology, people have gradually begun to use applications installed on terminal devices to create content and engage in digital social interaction. At present, when users publish content in these applications, service providers usually need to conduct a more comprehensive risk detection of the content that users want to publish to prevent the spread of inappropriate content. However, with the explosive growth of content that users need to publish, how to improve the efficiency and accuracy of risk detection for content that users need to publish, so as to reduce the risks brought by the content published by users, has become a technical problem that needs to be solved urgently. Summary of the invention

[0003] The embodiments of this specification provide a risk detection method, device, computer-readable storage medium, electronic device and computer program product, which can utilize a risk detection strategy in which the strictness of risk detection is positively correlated with the risk level of the user to perform risk detection processing on the target content that the user needs to publish, which is beneficial to improving the efficiency and accuracy of risk detection for the target content that the user needs to publish, so as to reduce the risks brought by the target content published by the user.

[0004] An embodiment of this specification provides a risk detection method, the method comprising: Obtain the target content that the user needs to publish; Obtaining the risk level of the user determined based on the user's historical data; wherein the user's historical data includes data generated based on the user's historical content publishing behavior; According to a risk detection strategy having a corresponding relationship with the risk level of the user, the target content is subjected to risk detection processing to obtain a risk detection result of the target content; wherein the strictness of the risk detection strategy is positively correlated with the risk level of the user.

[0005] The embodiment of this specification also provides a risk detection device, including: The first acquisition module is used to acquire the target content that the user needs to publish; A second acquisition module is used to acquire the risk level of the user determined based on the user's historical data; wherein the user's historical data includes data generated based on the user's historical content publishing behavior; The risk detection module is used to perform risk detection processing on the target content according to a risk detection strategy that corresponds to the risk level of the user, and obtain a risk detection result of the target content; wherein the strictness of the risk detection strategy is positively correlated with the risk level of the user.

[0006] The embodiments of this specification also provide a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the above method are implemented.

[0007] An embodiment of the present specification also provides an electronic device, comprising: a processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the steps of the above method.

[0008] The embodiments of the present specification also provide a computer program product, on which at least one instruction is stored, and when the at least one instruction is executed by a processor, the steps of the above method are implemented.

[0009] In an embodiment of the present specification, after obtaining the target content that the user needs to publish, the risk level of the user determined by the user's historical data generated based on the user's historical content publishing behavior can be obtained; by utilizing a risk detection strategy in which the strictness of risk detection is positively correlated with the user's risk level, risk detection processing is performed on the target content that the user needs to publish to obtain a risk detection result for the target content, which is beneficial to improving the efficiency and accuracy of risk detection for the target content that the user needs to publish, so as to reduce the risks brought by the target content published by the user. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 A schematic diagram of an application scenario of a risk detection solution provided in an embodiment of this specification.

[0011] Figure 2 A flowchart of a risk detection method provided in an embodiment of this specification.

[0012] Figure 3 A schematic diagram of the structure of a risk detection device provided in an embodiment of this specification.

[0013] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solutions and advantages of this specification more clear, the technical solutions of this specification will be clearly and completely described below in combination with the specific embodiments of this specification and the corresponding drawings. Obviously, the described embodiments are only part of the embodiments of this specification, not all of them. Based on the embodiments in this specification, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this specification.

[0015] Before specifically describing the risk detection solution provided in the embodiments of this specification, the relevant technical background is first described.

[0016] With the rapid development of content creation and the digital social industry, the content posted by users on applications has exploded. This phenomenon has not only enriched people's entertainment choices, but also brought huge content risk review challenges to service providers. Traditional content risk review methods often rely on manual review of all types of risks that may exist in the content that each user needs to publish according to unified standards. This content risk review method often consumes a lot of human resources, resulting in high risk detection costs. In addition, since this content risk review method relies on the subjective judgment of the reviewer, some risks may be missed, or too much review time may be spent on some harmless content, which can easily affect the efficiency and accuracy of risk detection for content that users need to publish.

[0017] See also Figure 1 , which is a schematic diagram of an application scenario of a risk detection solution provided in an embodiment of this specification.

[0018] like Figure 1As shown, the terminal devices (e.g., device 102, device 103, device 104, etc.) of the user group may be equipped with a target application, and the server 101 may be a server device of the target application. If the user uses the target application to perform the operation of publishing the target content at the device 103, the device 103 may send the target content that the user needs to publish to the server 101. The server 101 may obtain the risk level of the user determined according to the user's historical data; wherein the user's historical data may include data generated based on the user's historical content publishing behavior; so as to perform risk detection processing on the target content according to the risk detection strategy that has a corresponding relationship with the risk level of the user, and obtain the risk detection result of the target content; wherein the strictness of the risk detection strategy may be positively correlated with the risk level of the user. Thus, risk detection processing of different strictness can be performed for the content that users with different risk levels need to publish, which is conducive to improving the efficiency and accuracy of risk detection for the content that users need to publish, so as to reduce the risk brought by the content published by users.

[0019] See also Figure 2 , is a flow chart of a risk detection method provided in an embodiment of this specification. The execution subject of the process can be a risk detection device, or an application installed on the risk detection device. Figure 2 The process shown is described in detail, and the risk detection method may specifically include the following steps: Step 202: Obtain the target content that the user needs to publish.

[0020] In the embodiments of this specification, users can usually publish target content at the target application, and set the public scope of the target content so that a specified user group can browse and view the target content. In actual applications, as the content sharing functions supported by the target application are different, the types of target content that users need to publish can often be diverse. For example, the target content can include at least one of video content, audio content, image content and text content. In addition, it can also include links, codes, etc., which are not specifically limited.

[0021] For ease of understanding, this is illustrated by example. For example, when the target application is a social networking application that mainly shares pictures and short videos, the target content that the user needs to publish can be the life moments shown by the user through short videos or multiple images. Or, when the target application is an article and blog sharing application with text content as the main content, the target content that the user needs to publish can be a long article written or shared by the user, as well as news, opinions and multimedia content expressed through short text content. Or, when the target application is an application that provides a real-time live broadcast function, the target content that the user needs to publish can be the shopping experience, lifestyle and other content shared by the user in real time, so that the user group can purchase related products; or, the target content that the user needs to publish can also be the real-time live broadcast content of the user for games, entertainment and other aspects, so that the user can interact with the audience in real time. Of course, the target application can also be an application specifically used to share music, photography works, open source code, etc., and the target content that the user needs to publish can be content in the form of audio, images, codes, links, etc. There is no specific limitation on this.

[0022] Step 204, obtaining the risk level of the user determined based on the user's historical data; wherein the user's historical data includes data generated based on the user's historical content publishing behavior.

[0023] In the embodiments of this specification, the historical content publishing behaviors that the user has performed often generate relevant user historical data at the target application. Since the user's behavior pattern, content preference and potential risk can be understood by analyzing the user historical data, it can be used as an important basis for assessing the risk level of the user. Based on this, the risk level of the user can be obtained by performing data analysis on the user historical data generated based on the user's historical content publishing behaviors.

[0024] Generally speaking, the higher the risk level of the user, the more likely it is that the user will publish risky content. Therefore, it is necessary to conduct more stringent risk detection on the target content that the user needs to publish, in order to ensure the comprehensiveness and accuracy of the risk detection results generated for the target content, so as to effectively control the risks brought by the content published by the user. The lower the risk level of the user, the less likely it is that the user will publish risky content. Therefore, the strictness of the control when conducting risk detection on the target content that the user needs to publish can be appropriately relaxed, so as to effectively ensure the accuracy and effectiveness of the risk detection results generated for the target content, improve the efficiency of risk detection, and reduce the resources consumed by risk detection.

[0025] Step 206, performing risk detection processing on the target content according to a risk detection strategy that has a corresponding relationship with the risk level of the user, and obtaining a risk detection result of the target content; wherein the strictness of the risk detection strategy is positively correlated with the risk level of the user.

[0026] In the embodiments of the present specification, differentiated risk detection strategies corresponding to the risk levels of each user can be flexibly configured in advance, and the strictness of the risk detection strategy can be positively correlated with the risk level of the corresponding user, so that more stringent risk detection can be performed on the content that needs to be published by users with higher risk levels, and more relaxed risk detection can be performed on the content that needs to be published by users with lower risk levels. This not only ensures the efficiency and accuracy of content risk detection and improves user experience, but also effectively reduces the risk management cost for content that users need to publish, and has good practicality.

[0027] Figure 2 The middle solution, by utilizing a risk detection strategy in which the strictness of risk detection is positively correlated with the risk level of the user, performs risk detection on the target content that the user needs to publish to obtain a risk detection result for the target content. This is beneficial to improving the efficiency and accuracy of risk detection for the target content that the user needs to publish, thereby reducing the risks brought by the target content published by the user and the risk detection costs.

[0028] based on Figure 2 The method in this specification also provides some specific implementation plans of the method, which are described below.

[0029] In the embodiment of this specification, step 204: obtaining the risk level of the user determined based on the user historical data may include: Obtaining user history data of a preset type generated based on the user's historical content publishing behavior.

[0030] The user's historical data is processed using a risk assessment model constructed based on a machine learning algorithm to obtain a risk assessment result output by the risk assessment model that reflects the risk level of the user.

[0031] In the embodiments of this specification, by using training samples in advance to train and tune the risk assessment model built based on the machine learning algorithm, the risk assessment model can be enabled to extract useful feature data and the correlation between feature data from a large amount of user historical data to accurately assess the user's risk level. Since the risk assessment model built based on the machine learning algorithm can achieve automated, efficient and accurate user risk level assessment, user historical data samples can be set in advance according to actual needs to train the risk assessment model, and then the user's user historical data can be input into the risk assessment model to obtain the risk assessment result output by the risk assessment model to reflect the user's risk level.

[0032] In actual applications, the specific content of the risk assessment result can be pre-set according to actual needs. For example, the risk assessment result may include information reflecting the user's risk level (for example, high, medium, low, etc.); or, the risk assessment result may also include information reflecting the user's risk score (for example, a risk score based on a percentage, a risk score based on a ten-point scale, etc.); in addition, the risk assessment result may also specifically include information such as the user's risk level / risk score in one or more preset risk areas, wherein the preset risk area is not specifically limited.

[0033] In the embodiment of the present specification, the obtaining of preset types of user history data generated based on the user's historical content publishing behavior may include: Obtain at least one of the following data: the historical published content of the user, the interaction data of the user group on the historical published content, and the violation record data of the historical published content to obtain the user historical data. Or, According to a preset data analysis strategy, data analysis is performed on at least one of the historical published content of the user, the interaction data of the user group on the historical published content, and the violation record data of the historical published content to obtain the user historical data.

[0034] In the embodiments of this specification, the user's historical published content may include the content that the user has successfully published at the target application, and of course, may also include the content that the user has attempted to publish but has not successfully published. Since the user's preferred content topics (e.g., current affairs, health, entertainment, etc.) can be understood by analyzing the user's historical published content, this can to a certain extent reflect whether the content that the user wants to publish is likely to cause controversy or may violate regulations. Therefore, the user's risk level can be assessed in combination with the user's historical published content.

[0035] The user group's interactive data on the historically published content may include: the user group's comments on the user's historically published content, collection behavior data, forwarding behavior data, like behavior data, etc. Since the user's social attitude (for example, positive or negative) and whether there is an attack against other users can be analyzed by analyzing the interactive behavior and communication data between the user group and the user, the risk level of the user can be assessed in combination with the user group's interactive data on the user's historically published content.

[0036] The violation record data of the user's historical published content may include: the violation type, violation time, and punishment measures (for example, content deletion, account restriction, etc.) of the user's historical published content that have been determined. Since there is a clear correlation between the violation record data of the user's historical published content and the user's risk level. In addition, the user's historical violation behavior, violation frequency, violation severity, time factors, and the user's compliance repair ability, etc., will affect the user's risk assessment results. Therefore, the violation record data of the user's historical published content can be combined to more accurately assess the user's risk level.

[0037] In actual applications, since the amount of data such as the historical published content of some users and the interaction data of the user group on the historical published content is large, in order to ensure that the risk assessment model constructed based on the machine learning algorithm can efficiently and accurately complete the analysis and processing of the user's historical data, so as to improve the accuracy of the risk assessment results output by the risk assessment model to reflect the user's risk level, according to the preset data analysis strategy, data analysis and processing can be performed on at least one of the user's historical published content, the user group's interaction data on the historical published content, and the violation record data of the historical published content, so that the preset types of user-related information obtained by the data analysis can be used as the user historical data required to be input into the risk assessment model.

[0038] For ease of understanding, an example is given to illustrate this. For example, the publishing time of the user's historical published content can be analyzed to obtain the frequency data and active time period data of the user's published content. Alternatively, a collaborative analysis can be performed on the user's historical published content and the user group's interaction data on the historical published content to determine whether the user has emotional fluctuations or sudden high-frequency content releases for hot events. Alternatively, the violation record data of the user's historical published content can be analyzed to determine the review pass rate or violation rate of the user's published content. Alternatively, the user group that interacts with the user's historical published content can be analyzed to identify the user's influence and potential negative impact in the social circle, and whether the social circle he belongs to is an extreme or controversial circle, etc.; there is no specific limitation on this.

[0039] By performing data analysis and processing on the user's historical published content, the user group's interaction data on the historical published content, and the violation record data of the historical published content according to the preset data analysis strategy, a richer variety of user historical data can be obtained, which is conducive to a more comprehensive understanding of the risk status of individual users, and further improves the accuracy and reliability of the user's risk level generated by the risk identification model based on the user's historical data.

[0040] In the embodiment of the specification, performing risk detection processing on the target content according to the risk detection strategy having a corresponding relationship with the risk level of the user to obtain the risk detection result of the target content may include: Determine a risk detection strategy that corresponds to the risk level of the user; wherein the risk detection strategy is used to control the preset number ratio of content that needs to be risk detected in the target content of the user, and at least one of the preset risk types that need to be detected for the target content, and the preset number ratio and the number of the preset risk types are positively correlated with the risk level of the user.

[0041] According to the risk detection strategy, risk detection processing is performed on the specified content with the preset proportion contained in the target content to obtain the risk detection result of the target content. Or, According to the risk detection strategy, a risk detection process is performed on whether the target content contains a risk of the preset risk type, and a risk detection result of the target content is obtained. Or, According to the risk detection strategy, risk detection processing is performed on whether there is a risk of the preset risk type in the specified content with the preset proportion contained in the target content, so as to obtain a risk detection result of the target content.

[0042] In the embodiments of this specification, the strictness of the risk detection strategy can be reflected in the preset ratio of the content that needs to be risk-detected in the target content of the user, and the number of preset risk types that need to be detected for the target content. Generally, the larger the ratio of the preset ratio, the more content that actually needs to be risk-detected in the target content, so that a more comprehensive risk detection can be performed on the target content to effectively identify the risks carried in the target content. Therefore, the larger the ratio of the preset ratio, the higher the strictness of the risk detection strategy, and the smaller the ratio of the preset ratio, the lower the strictness of the risk detection strategy.

[0043] And, generally speaking, a greater number of preset risk types indicates whether a greater number of types of preset risks exist in the target content to be detected, thereby enabling a more comprehensive risk detection of the target content to effectively identify the risks that may be carried in the target content. Therefore, a greater number of preset risk types indicates a higher level of strictness in the risk detection strategy, and a smaller number of preset risk types indicates a lower level of strictness in the risk detection strategy.

[0044] It can be understood that when the risk detection strategy is only used to control the preset number ratio, the risk types that need to be identified for the content posted by users with different risk levels can be consistent; and when the risk detection strategy is only used to control the preset risk type, the number ratio of the content that needs to be risk detected in the content posted by users with different risk levels can be consistent. When the risk detection strategy is used to control the preset number ratio and the preset risk type, the risk types that need to be identified for the content posted by users with different risk levels can be different, and the number ratio of the content that needs to be risk detected can also be different, which has good flexibility and is not specifically limited.

[0045] In practical applications, when the target content is subjected to risk detection processing according to a risk detection strategy having a corresponding relationship with the risk level of the user, if the risk detection strategy is used to control the preset number ratio, then when the target content is video content, the image frames of the preset number ratio can be randomly or evenly extracted from the image frames constituting the video content, so as to generate the risk detection result of the target content in combination with the extracted image frames. When the target content is audio content or text content, if multiple audio contents of short duration and not connected to each other are intercepted, or multiple short words not connected to each other are intercepted, it is often impossible to effectively identify the content expressed by the user. Therefore, a unit minimum data volume (for example, minimum audio duration, minimum number of words, etc.) can be set to extract audio segments or text segments greater than the unit minimum data volume from the target content until the number ratio of the extracted audio content or text content reaches the preset number ratio, which is conducive to improving the accuracy and effectiveness of the risk detection result of the target content generated based on the extracted audio / text content.

[0046] In actual applications, if the risk detection strategy is used to control the preset risk type, the preset risk type can be a pre-screened risk type with a higher detection priority, or, when the risk level of the user in each preset risk area can be determined, the risk type with a higher priority in the preset risk area with a higher risk level of the user can also be used as the preset risk type that needs to be detected as indicated by the risk detection strategy, which is beneficial to improving the effectiveness and accuracy of risk detection for the target content that needs to be published by the user.

[0047] In the embodiment of the specification, the risk detection process is performed on the specified content with the preset proportion contained in the target content to obtain the risk detection result of the target content, which may include: By using a risk detection model constructed based on a neural network algorithm, risk detection processing is performed on the designated content with the preset proportion contained in the target content to obtain an initial risk detection result of the target content.

[0048] Correspondingly, performing risk detection processing on whether the target content contains the risk of the preset risk type to obtain the risk detection result of the target content may include: By using a risk detection model constructed based on a neural network algorithm, risk detection processing is performed on whether there is a risk of the preset risk type in the target content, so as to obtain an initial risk detection result of the target content.

[0049] Correspondingly, the risk detection process for whether there is a risk of the preset risk type in the specified content with the preset proportion contained in the target content to obtain the risk detection result of the target content may include: By using a risk detection model constructed based on a neural network algorithm, risk detection processing is performed on whether there is a risk of the preset risk type in the specified content with the preset proportion contained in the target content, so as to obtain an initial risk detection result of the target content.

[0050] In the embodiments of this specification, by using training samples in advance to train and tune the risk detection model built based on the neural network algorithm, the risk detection model can be enabled to extract useful feature data and the correlation between feature data from the content that the user needs to publish, so as to accurately assess the various preset risks that may exist in the content that the user needs to publish. Based on this, user content samples can be set in advance according to actual needs to train the risk detection model, and then the target content that is currently required to be published can be input into the risk detection model to obtain the initial risk detection result output by the risk detection model to reflect whether the target content carries the preset risk, which is convenient and quick.

[0051] In actual applications, the types of preset risks that the risk detection model can detect can be set according to actual needs, for example, checking whether the user content has the risk of containing inappropriate content, checking whether there is a risk of using unauthorized images, videos, and music in the user content, and checking whether the user content has the risk of leaking other people's personal information or sensitive data; in addition, it is also possible to check whether the user content has the risk of grammatical / spelling errors and the risk of poor clarity of images, videos, audio, etc., without specific limitations.

[0052] In an embodiment of the present specification, the risk detection strategy can also be used to indicate the preset conditions that need to be met for manual review of the user's target content, so as to control the probability of manual review of the user's target content, and the manual review probability can be positively correlated with the risk level of the user.

[0053] Correspondingly, performing risk detection processing on the target content according to a risk detection strategy having a corresponding relationship with the risk level of the user to obtain a risk detection result of the target content may also include: According to the initial risk detection result, it is determined whether the preset condition is met to obtain a first determination result.

[0054] If the first judgment result indicates that the preset condition is met, a manual risk review result for the target content is obtained to obtain a risk detection result for the target content.

[0055] If the first judgment result indicates that the preset condition is not met, the initial risk detection result is determined as the risk detection result of the target content.

[0056] In the embodiments of this specification, in addition to using the risk detection model built based on the neural network algorithm to perform risk detection on the target content that the user needs to publish, manual review is usually an indispensable part of the user content risk review process. By having the reviewer conduct a comprehensive risk assessment of the target content that the user needs to publish, the spread of inappropriate content can be further effectively prevented, which helps to improve the quality and compliance of the content that the user successfully publishes.

[0057] Based on this, in order to save human resources and make full use of the advantages of manual review to control the risks of the target content that users need to publish, a risk detection strategy that can indicate the preset conditions that need to be met for manual review of the target content of the user can be pre-configured, and the probability that the content to be published by the user with a higher risk level is manually reviewed is greater, while the probability that the content to be published by the user with a lower risk level is manually reviewed is smaller, which is conducive to improving the efficiency and accuracy of risk detection and reducing the cost of risk detection. Among them, the preset conditions can be set according to actual needs. For example, the preset conditions can include: the probability that the target content generated by the risk detection model constructed based on the neural network algorithm has a risk reaches a preset threshold, or the target content has a specified risk, or the target content is content in a specified field, etc., without specific limitation.

[0058] Therefore, when the target content is subjected to risk detection processing according to the risk detection strategy that corresponds to the risk level of the user, after the initial risk detection result of the target content is generated by the risk detection model constructed based on the neural network algorithm, the initial risk detection result of the target content can be combined to determine whether the preset conditions required for manual review of the target content that the user needs to publish are met. If so, the target content can be submitted to the reviewer so that the obtained manual risk review result for the target content can be used as the risk detection result of the target content. If not, the initial risk detection result of the target content generated by the risk detection model can be directly used as the risk detection result of the target content, which is flexible.

[0059] In the embodiment of the specification, the risk detection process is performed on the target content according to the risk detection strategy having a corresponding relationship with the risk level of the user, and after obtaining the risk detection result of the target content, the method may further include: According to the risk detection result of the target content, it is judged whether the target content meets the preset content publishing condition to obtain a second judgment result.

[0060] If the second judgment result indicates that the target content meets the preset content publishing condition, a publishing instruction for the target content is generated.

[0061] If the second judgment result indicates that the target content does not meet the preset content publishing condition, a prohibition instruction for publishing the target content is generated.

[0062] In the embodiments of this specification, the preset content publishing conditions for user content can usually be pre-configured according to actual needs; for example, the preset content publishing conditions may include: the user content does not carry a preset risk, or the risk level of the user content is lower than a threshold, or the quality score of the user content reaches a threshold, etc., which are not specifically limited. By generating a publishing instruction for the target content whose preset content publishing conditions are met, the user content that meets the preset content publishing conditions is published and disseminated externally; and generating a prohibition of publishing instruction for the target content whose preset content publishing conditions are not met, so as to refuse to publish and disseminate the user content that does not meet the preset content publishing conditions, the quality and compliance of the user content published and disseminated externally can be effectively controlled.

[0063] In the embodiment of this specification, if the second judgment result indicates that the target content meets the preset content publishing condition, the method may further include: Determine a content publishing strategy that has a corresponding relationship with the risk level of the user; wherein the content publishing strategy is used to control the propagation rate of the target content in the user group, and the propagation rate is negatively correlated with the risk level of the user.

[0064] Correspondingly, the generating the publishing instruction for the target content may include: generating the publishing instruction for the target content according to the content publishing policy.

[0065] In the embodiment of this specification, although risk detection is performed on the target content that the user needs to publish, and the decision is made whether to publish the target content externally based on the risk detection result of the target content, the risk brought by the target content can be more effectively controlled. However, there may still be some new risks or more hidden risks in the process of dissemination of the target content, thereby affecting the security and compliance of the user content dissemination process. Based on this, it is also possible to preset the content release strategy according to actual needs, so that the target content published by users with a higher risk level has a lower dissemination rate in the user group, and the target content published by users with a lower risk level has a higher dissemination rate in the user group. So that after successfully publishing and disseminating the target content externally, once it is detected that the dissemination of the target content has caused negative public opinion or social repercussions, it can quickly take measures such as banning, deleting or reducing the visibility of the content to prevent the spread of risky content. It is not only conducive to reducing the immediate impact of the dissemination of risky content, but also to buy time for subsequent corrective measures to effectively reduce the possibility of risk diffusion.

[0066] In actual applications, the spread rate of user content may be affected by many factors. Therefore, there may be many types of content publishing strategies. For example, the content publishing strategy may be used to control the weights in the content recommendation algorithm to control the exposure rate, exposure time period, etc. of the user content, or the content publishing strategy may be used to control the number of people to whom the user content is pushed, the characteristics of the people to whom the content is pushed, the frequency of pushes, the number of times the content is pushed, etc., without specific limitation. By generating a publishing instruction for the target content in accordance with the content publishing strategy, when the target content is published and spread in response to the publishing instruction, the spread rate of the target content may be indirectly controlled by controlling the exposure rate, exposure time period, number of people to whom the content is pushed, the characteristics of the people to whom the content is pushed, the frequency of pushes, the number of times the content is pushed, etc., with good flexibility.

[0067] In the embodiments of this specification, Figure 2 The method may further include: The target content is stored in a preset database to obtain the updated historical published content of the user. Or, The interactive data of the user group for the target content is stored in a preset database to obtain the updated interactive data of the user group for the historically published content of the user. Or, The violation record data of the target content is stored in a preset database or a preset blockchain network to obtain the updated violation record data of the user's historical published content.

[0068] In the embodiments of this specification, regardless of whether the user's target content is successfully released, the target content can be used to update the user's historical release content, which is conducive to ensuring the comprehensiveness and integrity of the user's updated historical release content; and the violation record data of the target content can also be used to update the violation record data of the user's historical release content, which is conducive to ensuring the comprehensiveness and integrity of the violation record data of the user's historical release content after the update. After the user's target content is successfully released, if the user group's interactive data for the target content is generated, the user group's interactive data for the target content can also be used to update the interactive data of the user's historical release content, so as to ensure the comprehensiveness and integrity of the updated interactive data of the user's historical release content.

[0069] In practical applications, the data stored in the blockchain network cannot usually be modified or deleted, and the decentralized nature of the blockchain network makes its data management process independent of a single manager or platform, thereby reducing the probability of data stored in the blockchain network being unilaterally controlled or manipulated. Based on this, the service provider can store the violation record data of the user's target content (and historical published content) in the preset blockchain network to ensure the integrity and credibility of the user's related violation record data, which is conducive to enhancing the trust between the user and the service provider. Of course, other types of user historical data of the user can also be stored in the preset blockchain network, and there is no specific limitation on this.

[0070] In the embodiments of this specification, Figure 2 The method may further include: It is determined whether a risk level update condition for the user is satisfied to obtain a third determination result.

[0071] If the third judgment result indicates that the risk level update condition for the user is met, the latest user history data generated based on the user's historical content publishing behavior is obtained; wherein the latest user history data includes: at least one of the updated historical publishing content, the updated interaction data and the updated violation record data.

[0072] The latest user historical data is processed by using a risk assessment model constructed based on a machine learning algorithm to obtain a risk assessment result output by the risk assessment model that reflects the updated risk level of the user; wherein the updated risk level of the user is used to determine the risk detection strategy required for risk detection and control of content that the user needs to publish within a preset time period in the future, or to determine the content publishing strategy required for content publishing control of content that the user needs to publish within a preset time period in the future.

[0073] In the embodiments of the present specification, risk level update conditions for users can be pre-configured according to actual needs. For example, the risk level update condition can be that the interval time since the last update of the user's risk level reaches a threshold, or the number of contents posted by the user in the most recent preset time period reaches a threshold, or the number of violation records generated by the user in the most recent preset time period reaches a threshold, etc., and no specific limitation is made to this.

[0074] When the risk level update condition for the user is met, the user's current risk level can be evaluated in combination with the latest user history data generated based on the user's historical content publishing behavior, as the user's updated risk level. Subsequently, when the user needs to publish content, the corresponding risk detection strategy and / or content publishing strategy can be determined in combination with the user's updated risk level, so as to perform risk detection on the content that the user needs to publish in combination with the risk detection strategy, and control the dissemination rate of the content in combination with the content publishing strategy.

[0075] In the embodiments of the present specification, the risk level of the user is updated by combining the user's latest historical data, and the risk detection control and content publishing control of the content that the user needs to publish subsequently can be performed in combination with the user's updated risk level. This is conducive to motivating users to comply with content publishing regulations and promoting users to participate in content risk management, thereby reducing the risks brought by user-published content at the source, helping to create a healthy and safe content ecological environment, and more effectively ensuring the quality and compliance of user content.

[0076] Based on the same idea, the embodiments of this specification also provide a device corresponding to the above method. Figure 3 , is a schematic diagram of the structure of a risk detection device provided in an embodiment of this specification. Figure 3 As shown, the risk detection device 3 can be implemented as all or part of the electronic device through software, hardware or a combination of both. According to some embodiments, the risk detection device 3 may include a first acquisition module 31, a second acquisition module 32, and a risk detection module 33, wherein: The first acquisition module 31 is used to acquire target content that the user needs to publish.

[0077] The second acquisition module 32 is used to acquire the risk level of the user determined according to the user history data; wherein the user history data includes data generated based on the user's historical content publishing behavior.

[0078] The risk detection module 33 is used to perform risk detection processing on the target content according to a risk detection strategy that corresponds to the risk level of the user, and obtain a risk detection result of the target content; wherein the strictness of the risk detection strategy is positively correlated with the risk level of the user.

[0079] Optionally, the second acquisition module 32 may include: The first acquisition unit is used to acquire user history data of a preset type generated based on the user's historical content publishing behavior.

[0080] The risk level assessment unit is used to process the user's historical data using a risk assessment model constructed based on a machine learning algorithm to obtain a risk assessment result output by the risk assessment model that reflects the risk level of the user.

[0081] Optionally, the first acquisition unit may be specifically used for: Obtain at least one of the following data: historical published content of the user, interaction data of user groups on the historical published content, and violation record data of the historical published content, to obtain the user historical data; or, According to a preset data analysis strategy, data analysis is performed on at least one of the historical published content of the user, the interaction data of the user group on the historical published content, and the violation record data of the historical published content to obtain the user historical data.

[0082] Optionally, the risk detection module 33 may include: A first determination unit is used to determine a risk detection strategy that corresponds to the risk level of the user; wherein the risk detection strategy is used to control the preset number ratio of the content that needs to be risk detected in the target content of the user, and at least one of the preset risk types that need to be detected for the target content, and the preset number ratio and the number of the preset risk types are positively correlated with the risk level of the user.

[0083] A first risk detection unit is configured to perform risk detection processing on the specified content with the preset proportion contained in the target content according to the risk detection strategy to obtain a risk detection result of the target content; or A second risk detection unit is configured to perform risk detection processing on whether the target content has a risk of the preset risk type according to the risk detection strategy, and obtain a risk detection result of the target content; or The third risk detection unit is used to perform risk detection processing according to the risk detection strategy to determine whether there is a risk of the preset risk type in the specified content with the preset proportion contained in the target content, so as to obtain a risk detection result of the target content.

[0084] Optionally, the first risk detection unit can be specifically used to: utilize a risk detection model constructed based on a neural network algorithm to perform risk detection processing on designated content that accounts for the preset proportion of the target content, and obtain an initial risk detection result of the target content.

[0085] Optionally, the second risk detection unit may be specifically configured to: utilize a risk detection model constructed based on a neural network algorithm to perform risk detection processing on whether a risk of the preset risk type exists in the target content, and obtain an initial risk detection result of the target content.

[0086] Optionally, the third risk detection unit can be specifically used to: utilize a risk detection model constructed based on a neural network algorithm to perform risk detection processing on whether there is a risk of the preset risk type in the specified content with the preset proportion contained in the target content, and obtain an initial risk detection result of the target content.

[0087] Optionally, the risk detection strategy can also be used to indicate preset conditions that need to be met for manual review of the user's target content, so as to control the probability of manual review of the user's target content, and the manual review probability is positively correlated with the risk level of the user.

[0088] Optionally, the risk detection module 33 may also include: The judgment unit is used to judge whether the preset condition is met according to the initial risk detection result to obtain a first judgment result.

[0089] The second acquisition unit is used to acquire a manual risk review result for the target content to obtain a risk detection result for the target content if the first judgment result indicates that the preset condition is met.

[0090] The second determining unit is configured to determine the initial risk detection result as the risk detection result of the target content if the first judgment result indicates that the preset condition is not satisfied.

[0091] Optionally, the target content may include: at least one of video content, audio content, image content and text content.

[0092] Optional, Figure 3 The device may further include: The first condition judgment module is used to judge whether the target content meets the preset content publishing condition according to the risk detection result of the target content, and obtain a second judgment result.

[0093] The first generating module is configured to generate a publishing instruction for the target content if the second judgment result indicates that the target content meets a preset content publishing condition.

[0094] The second generating module is configured to generate a prohibition instruction for publishing the target content if the second judgment result indicates that the target content does not meet a preset content publishing condition.

[0095] Optional, Figure 3 The device may further include: A determination module is used to determine a content publishing strategy that corresponds to the risk level of the user if the second judgment result indicates that the target content meets the preset content publishing conditions; wherein the content publishing strategy is used to control the propagation rate of the target content in the user group, and the propagation rate is negatively correlated with the risk level of the user.

[0096] The first generating module may be specifically configured to generate a publishing instruction for the target content according to the content publishing strategy.

[0097] Optional, Figure 3 The device may further include: A first storage module is used to store the target content in a preset database to obtain the updated historical published content of the user; or A second storage module is used to store the interaction data of the user group for the target content in a preset database to obtain updated interaction data of the user group for the historical content published by the user; or The third storage module is used to store the violation record data of the target content in a preset database or a preset blockchain network, and obtain the updated violation record data of the historical published content of the user.

[0098] Optional, Figure 3 The device may further include: The second condition judgment module is used to judge whether the risk level update condition for the user is met to obtain a third judgment result.

[0099] A third acquisition module is used to obtain the latest user historical data generated based on the user's historical content publishing behavior if the third judgment result indicates that the risk level update condition for the user is met; wherein the latest user historical data includes: at least one of the updated historical publishing content, the updated interaction data and the updated violation record data.

[0100] An updating module is used to process the latest user historical data using a risk assessment model constructed based on a machine learning algorithm to obtain a risk assessment result output by the risk assessment model that reflects the updated risk level of the user; wherein the updated risk level of the user is used to determine a risk detection strategy required for risk detection and control of content that the user needs to publish within a preset time period in the future, or to determine a content publishing strategy required for content publishing control of content that the user needs to publish within a preset time period in the future.

[0101] The above device embodiments correspond to the method embodiments. For specific descriptions, please refer to the description of the method embodiments, which will not be repeated here. The device embodiments are obtained based on the corresponding method embodiments and have the same technical effects as the corresponding method embodiments. For specific descriptions, please refer to the corresponding method embodiments.

[0102] The present specification also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the above Figure 2 The specific execution process of the risk detection method shown can refer to the specific description of the relevant embodiments of the risk detection method, which will not be repeated here.

[0103] The present specification also provides a computer program product, which stores at least one instruction, and the at least one instruction is loaded and executed by the processor as described above. Figure 2 The specific execution process of the risk detection method shown can refer to the specific description of the relevant embodiments of the risk detection method, which will not be repeated here.

[0104] The embodiments of this specification also provide Figure 4 The structural diagram of the electronic device shown in FIG. Figure 4 At the hardware level, the electronic device may include a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include other hardware required for the business. The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to achieve the above Figure 2 The specific execution process of the risk detection method shown can refer to the specific description of the relevant embodiments of the risk detection method, which will not be repeated here.

[0105] Of course, in addition to software implementation, this specification does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is to say, the executor of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0106] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for computer-readable storage media, computer program products and Figure 4 As for the electronic device shown, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0107] In the 1990s, it was very clear whether the improvement of a technology was hardware improvement (for example, improvement of the circuit structure of diodes, transistors, switches, etc.) or software improvement (improvement of the method flow). However, with the development of technology, many improvements of the method flow today can be regarded as direct improvements of the hardware circuit structure. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that the improvement of a method flow cannot be implemented with hardware entity modules. For example, a programmable logic device (PLD) (such as a field programmable gate array (FPGA)) is such an integrated circuit whose logical function is determined by the user's programming of the device. Designers can "integrate" a digital system on a PLD by programming it themselves, without having to ask chip manufacturers to design and make dedicated integrated circuit chips. Moreover, nowadays, instead of manually making integrated circuit chips, this kind of programming is mostly implemented by "logic compiler" software, which is similar to the software compiler used when developing and writing programs, and the original code before compilation must also be written in a specific programming language, which is called hardware description language (HDL). There is not only one kind of HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed ​​Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also know that it is only necessary to program the method flow slightly in the above-mentioned hardware description languages ​​and program it into the integrated circuit, and then it is easy to obtain the hardware circuit that implements the logic method flow.

[0108] The controller may be implemented in any suitable manner, for example, the controller may take the form of a microprocessor or processor and a computer-readable medium storing a computer-readable program code (such as software or firmware) executable by the (micro)processor, a logic gate, a switch, an application-specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller may also be implemented as part of the control logic of the memory. It is also known to those skilled in the art that, in addition to implementing the controller in a purely computer-readable program code manner, the controller may be implemented in the form of a logic gate, a switch, an application-specific integrated circuit, a programmable logic controller, and an embedded microcontroller by logically programming the method steps. Therefore, such a controller may be considered as a hardware component, and the devices for implementing various functions included therein may also be considered as structures within the hardware component. Or even, the devices for implementing various functions may be considered as both software modules for implementing the method and structures within the hardware component.

[0109] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0110] For the convenience of description, the above device is described in various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0111] Those skilled in the art will appreciate that the embodiments of this specification may be provided as methods, systems, or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0112] This specification is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of this specification. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0113] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0114] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0115] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0116] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0117] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0118] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0119] It should be understood by those skilled in the art that the embodiments of this specification may be provided as methods, systems or computer program products. Therefore, this specification may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, this specification may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] This specification may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media, including storage devices.

[0121] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0122] The above description is only an embodiment of the present specification and is not intended to limit the present specification. For those skilled in the art, the present specification may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification shall be included in the scope of the claims of the present specification.

Claims

1. A risk detection method, the method comprising: Obtain the target content that the user needs to publish; Obtaining the risk level of the user determined based on the user's historical data; wherein the user's historical data includes data generated based on the user's historical content publishing behavior; According to a risk detection strategy having a corresponding relationship with the risk level of the user, the target content is subjected to risk detection processing to obtain a risk detection result of the target content; wherein the strictness of the risk detection strategy is positively correlated with the risk level of the user.

2. According to the method of claim 1, the step of obtaining the risk level of the user determined based on the user's historical data comprises: Acquire user history data of a preset type generated based on the user's historical content publishing behavior; The user's historical data is processed using a risk assessment model constructed based on a machine learning algorithm to obtain a risk assessment result output by the risk assessment model that reflects the risk level of the user.

3. According to the method of claim 2, the step of obtaining the preset type of user history data generated based on the user's historical content publishing behavior comprises: Obtain at least one of the following data: historical published content of the user, interaction data of user groups on the historical published content, and violation record data of the historical published content, to obtain the user historical data; or, According to a preset data analysis strategy, data analysis is performed on at least one of the historical published content of the user, the interaction data of the user group on the historical published content, and the violation record data of the historical published content to obtain the user historical data.

4. The method according to claim 1, wherein the risk detection process is performed on the target content according to the risk detection strategy having a corresponding relationship with the risk level of the user to obtain the risk detection result of the target content, comprising: Determine a risk detection strategy that corresponds to the risk level of the user; wherein the risk detection strategy is used to control a preset number ratio of content that needs to be risk detected in the target content of the user, and at least one of the preset risk types that need to be detected for the target content, and the preset number ratio and the number of the preset risk types are both positively correlated with the risk level of the user; According to the risk detection strategy, risk detection processing is performed on the specified content with the preset proportion contained in the target content to obtain the risk detection result of the target content; or According to the risk detection strategy, a risk detection process is performed on whether the target content has a risk of the preset risk type, and a risk detection result of the target content is obtained; or According to the risk detection strategy, risk detection processing is performed on whether there is a risk of the preset risk type in the specified content with the preset proportion contained in the target content, so as to obtain a risk detection result of the target content.

5. The method according to claim 4, wherein the step of performing risk detection processing on the designated content with the preset proportion contained in the target content to obtain the risk detection result of the target content comprises: Using a risk detection model built based on a neural network algorithm, risk detection processing is performed on the specified content with the preset proportion contained in the target content to obtain an initial risk detection result of the target content; The performing risk detection processing on whether there is a risk of the preset risk type in the target content to obtain a risk detection result of the target content includes: Using a risk detection model built based on a neural network algorithm, a risk detection process is performed on whether the target content contains a risk of the preset risk type, thereby obtaining an initial risk detection result of the target content; The performing risk detection processing on whether the specified content with the preset proportion contained in the target content contains the risk of the preset risk type to obtain the risk detection result of the target content includes: By using a risk detection model constructed based on a neural network algorithm, risk detection processing is performed on whether there is a risk of the preset risk type in the specified content with the preset proportion contained in the target content, so as to obtain an initial risk detection result of the target content.

6. According to the method of claim 5, the risk detection strategy is further used to indicate the preset conditions that need to be met for manual review of the target content of the user, so as to control the probability of manual review of the target content of the user, and the manual review probability is positively correlated with the risk level of the user; The step of performing risk detection processing on the target content according to the risk detection strategy having a corresponding relationship with the risk level of the user to obtain a risk detection result of the target content also includes: According to the initial risk detection result, determining whether the preset condition is met to obtain a first determination result; If the first judgment result indicates that the preset condition is met, obtaining a manual risk review result for the target content to obtain a risk detection result for the target content; If the first judgment result indicates that the preset condition is not met, the initial risk detection result is determined as the risk detection result of the target content.

7. The method according to claim 6, wherein the target content comprises: At least one of video content, audio content, image content and text content.

8. The method according to any one of claims 1 to 7, wherein after performing risk detection processing on the target content according to a risk detection strategy having a corresponding relationship with the risk level of the user and obtaining the risk detection result of the target content, the method further comprises: According to the risk detection result of the target content, determining whether the target content meets the preset content publishing condition, and obtaining a second determination result; If the second judgment result indicates that the target content meets the preset content publishing condition, generating a publishing instruction for the target content; If the second judgment result indicates that the target content does not meet the preset content publishing condition, a prohibition instruction for publishing the target content is generated.

9. The method according to claim 8, if the second judgment result indicates that the target content meets the preset content publishing condition, the method further comprises: Determining a content publishing strategy that has a corresponding relationship with the risk level of the user; wherein the content publishing strategy is used to control the propagation rate of the target content in the user group, and the propagation rate is negatively correlated with the risk level of the user; The generating of the publishing instruction for the target content includes: According to the content publishing strategy, a publishing instruction for the target content is generated.

10. The method according to claim 9, further comprising: Storing the target content in a preset database to obtain the updated historical published content of the user; or, storing the interaction data of the user group for the target content in a preset database, and obtaining the updated interaction data of the user group for the historically published content of the user; or, The violation record data of the target content is stored in a preset database or a preset blockchain network to obtain the updated violation record data of the user's historical published content.

11. The method according to claim 10, further comprising: Determining whether a risk level update condition for the user is satisfied, and obtaining a third determination result; If the third judgment result indicates that the risk level update condition for the user is met, then the latest user history data generated based on the user's historical content publishing behavior is obtained; wherein the latest user history data includes: at least one of the updated historical publishing content, the updated interaction data, and the updated violation record data; The latest user historical data is processed by using a risk assessment model constructed based on a machine learning algorithm to obtain a risk assessment result output by the risk assessment model that reflects the updated risk level of the user; wherein the updated risk level of the user is used to determine the risk detection strategy required for risk detection and control of content that the user needs to publish within a preset time period in the future, or to determine the content publishing strategy required for content publishing control of content that the user needs to publish within a preset time period in the future.

12. A risk detection device, comprising: The first acquisition module is used to acquire the target content that the user needs to publish; A second acquisition module is used to acquire the risk level of the user determined based on the user's historical data; wherein the user's historical data includes data generated based on the user's historical content publishing behavior; The risk detection module is used to perform risk detection processing on the target content according to a risk detection strategy that corresponds to the risk level of the user, and obtain a risk detection result of the target content; wherein the strictness of the risk detection strategy is positively correlated with the risk level of the user.

13. A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the method according to any one of claims 1 to 11.

14. An electronic device comprising: A processor and a memory; wherein the memory stores a computer program, and the computer program is suitable for being loaded by the processor and executing the steps of the method as claimed in any one of claims 1 to 11.

15. A computer program product having at least one instruction stored thereon, wherein when the at least one instruction is executed by a processor, the steps of the method according to any one of claims 1 to 11 are implemented.

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