Information processing method, device and equipment and computer readable medium

Abstract

The invention relates to an information processing method, device and equipment and a computer readable medium. According to the method, the AI large model is utilized to identify malicious comment data, comment environments before and after filtering are fed to the AI large model, problems discussed before and after filtering and comments with similar comment tendencies before and after filtering are summarized and output, real public opinions after filtering are reflected to a user, and the user experience is improved. The technical problem that a user is easily trapped in an information cocoon house due to the fact that problem comments cannot be recognized is solved.

Description

Technical Field

[0001] The present application relates to the field of Internet technology, and in particular to an information processing method, device, equipment and computer-readable medium. Background Art

[0002] With the rapid development of the Internet, people's online social forms and consumption patterns have also undergone earth-shaking changes. From the relatively closed Internet forums in the early days, each forum was relatively independent, to the more comprehensive Baidu Tieba, Douban Group, etc., and now the inclusive Weibo, people's online social forms have become more and more open and more inclusive. Online e-commerce has also developed rapidly with the Internet trend, and a large part of people's consumption patterns have also shifted from offline to online. However, the open online community environment is not only good. The open environment has also caused a large number of robots to post comments in the community, as well as hired "water army" and "reverse" speech. The personalized recommendation algorithm of the platform allows users to only see what the platform wants them to see, and users are easily trapped in the information cocoon.

[0003] At present, although there are plug-ins in the relevant technology that force users to browse their comments and dynamics, they are still unable to identify robot comments, "water army" comments or "reverse" comments, resulting in the comment information seen by users may not be the most authentic comment information, and users are easily trapped in an information cocoon.

[0004] There is currently no effective solution to the problem that users are easily trapped in information cocoons due to the inability to identify problematic comments. Summary of the invention

[0005] The present application provides an information processing method, apparatus, device and computer-readable medium to solve the technical problem that users are easily trapped in an information cocoon due to the inability to identify problematic comments.

[0006] According to one aspect of an embodiment of the present application, the present application provides an information processing method, including: obtaining initial comment data and initial comment environment information of a target topic, wherein the target topic is an issue discussed jointly by multiple accounts in a social network; inputting the initial comment data and the initial comment environment information into a target big model, so as to summarize the initial comment background of the target topic based on the initial comment environment information through the target big model, and identify malicious comment data in the initial comment data based on the initial comment background, wherein the malicious comment data is comment data that guides public opinion away from the target topic; filtering out the malicious comment data in the initial comment data to obtain target comment data, and filtering out the malicious comment environment information corresponding to the malicious comment data in the initial comment environment information to obtain target comment environment information; inputting the target comment data and the target comment environment information into the target big model, so as to summarize the real comment background of the target topic based on the target comment environment information through the target big model, and classify and summarize the target comment data based on the real comment background.

[0007] Optionally, the method of obtaining the initial comment data and initial comment environment information of the target topic includes: obtaining the comment data to be identified under the target topic as the initial comment data; determining the account that issued the initial comment data as the account to be identified; obtaining the account information and historical dynamic information of the account to be identified, wherein the account information includes the registration time, account level and account name of the account; and determining the account information and the historical dynamic information of each account to be identified as the initial comment environment information.

[0008] Optionally, before inputting the initial comment data and the initial comment environment information into the target big model, the method also includes obtaining the target big model in the following manner: obtaining malicious comment samples and account data samples corresponding to the malicious comment samples; analyzing and sorting the account data samples to obtain the account basic information, historical dynamic information and the problem information being discussed when each malicious comment sample was published; inputting the malicious comment samples, the account basic information, the historical dynamic information and the problem information into the initial multi-category classification model for deep learning, so that the initial multi-category classification model performs weighted scoring analysis on the classification results of the malicious comment samples based on the malicious comment samples, the account basic information, the historical dynamic information and the problem information to learn to identify various types of malicious comment samples; performing regression verification on the trained initial multi-category classification model, and when the verification result meets the target condition, determining the initial multi-category classification model as the target big model.

[0009] Optionally, the obtaining of malicious comment samples and account data samples corresponding to the malicious comment samples includes at least one of the following: obtaining comments whose account level is less than a first threshold and whose creation time is less than a second threshold, and whose comment content similarity is greater than a third threshold as robot comment samples, and obtaining account information and historical dynamic information of the robot comment samples as the account data samples corresponding to the robot comment samples, wherein the malicious comment samples include the robot comment samples; obtaining comments whose number of comments appearing at the same time is greater than a fourth threshold and whose comment content similarity is greater than a fifth threshold as water army comment samples, and obtaining account information and historical dynamic information of the water army comment samples as the account data samples corresponding to the water army comment samples, wherein the malicious comment samples include the water army comment samples; obtaining comments whose similarity between the comment content and the account user portrait is less than a sixth threshold and whose number of comments is greater than a seventh threshold as anti-play comment samples, and obtaining account information and historical dynamic information of the anti-play comment samples as the account data samples corresponding to the anti-play comment samples, wherein the malicious comment samples include the anti-play comment samples.

[0010] Optionally, the initial multi-category classification model performs weighted scoring analysis on the classification results of the malicious comment samples based on the malicious comment samples, the account basic information, the historical dynamic information and the problem information to learn to identify various types of the malicious comment samples, including at least one of the following: when it is determined that the account corresponding to the malicious comment sample has an account level less than the first threshold, a creation time less than the second threshold, and an account name regularity less than a preset degree threshold, the classification weight of the robot comment is increased according to the set step size to improve the score of classifying the malicious comment sample as the robot comment; when it is determined that multiple malicious comment samples appear at the same time and the similarity of the comment content is greater than the fifth threshold, the classification weight of the water army comment is increased according to the set step size to improve the score of classifying the malicious comment sample as the water army comment; when it is determined that multiple malicious comment samples appear at the same time and the similarity of the comment content and the account user portrait is less than the sixth threshold, the classification weight of the anti-stringing comment is increased according to the set step size to improve the score of classifying the malicious comment sample as the anti-stringing comment.

[0011] Optionally, after classifying and summarizing the target comment data based on the real comment background, the method also includes: determining the proportion of the malicious comment data and the target comment data in the initial comment data, and drawing a diagram based on their respective proportion data; displaying the diagram, marking the malicious comment data, and comparing the marked malicious comment data with the target comment data, so as to prompt the user to identify the malicious comment data while reflecting the real public opinion of the target topic.

[0012] Optionally, the method further includes: opening a data interface to a target browser plug-in, so that the target browser plug-in processes comment information in a social network through the data interface, wherein the target browser plug-in is embedded in a browser for front-end display.

[0013] According to another aspect of an embodiment of the present application, the present application provides an information processing device, including: an acquisition module, used to acquire initial comment data and initial comment environment information of a target topic, wherein the target topic is an issue discussed jointly by multiple accounts in a social network; an identification module, used to input the initial comment data and the initial comment environment information into a target big model, so as to summarize the initial comment background of the target topic based on the initial comment environment information through the target big model, and identify malicious comment data in the initial comment data based on the initial comment background, wherein the malicious comment data is comment data that guides public opinion away from the target topic; a filtering module, used to filter out the malicious comment data in the initial comment data to obtain target comment data, and filter out the malicious comment environment information corresponding to the malicious comment data in the initial comment environment information to obtain target comment environment information; a summarizing module, used to input the target comment data and the target comment environment information into the target big model, so as to summarize the real comment background of the target topic based on the target comment environment information through the target big model, and classify and summarize the target comment data based on the real comment background.

[0014] According to another aspect of an embodiment of the present application, the present application provides an electronic device, including a memory, a processor, a communication interface and a communication bus, wherein the memory stores a computer program that can be run on the processor, the memory and the processor communicate through the communication bus and the communication interface, and the steps of the above method are implemented when the processor executes the computer program.

[0015] According to another aspect of an embodiment of the present application, the present application also provides a computer-readable medium having a non-volatile program code executable by a processor, and the program code enables the processor to execute the above method.

[0016] The above technical solution provided by the embodiment of the present application has the following advantages compared with the related art:

[0017] The present application provides an information processing method, comprising: obtaining initial comment data and initial comment environment information of a target topic, wherein the target topic is an issue discussed jointly by multiple accounts in a social network; inputting the initial comment data and the initial comment environment information into a target big model, so as to summarize the initial comment background of the target topic based on the initial comment environment information through the target big model, and identify malicious comment data in the initial comment data based on the initial comment background, wherein the malicious comment data is comment data that guides public opinion away from the target topic; filtering out the malicious comment data from the initial comment data to obtain target comment data, and filtering out the malicious comment environment information corresponding to the malicious comment data from the initial comment environment information to obtain target comment environment information; inputting the target comment data and the target comment environment information into the target big model, so as to summarize the real comment background of the target topic based on the target comment environment information through the target big model, and classifying and summarizing the target comment data based on the real comment background. This application uses an AI big model to identify malicious comment data, feeds the comment environment before and after filtering to the AI ​​big model, summarizes and outputs the issues discussed before and after filtering, as well as the comment tendencies before and after filtering, summarizes comments with similar opinions, and reflects the real public opinion after filtering to users, solving the technical problem that users are easily trapped in information cocoons due to the inability to identify problematic comments. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0020] Figure 1 A schematic diagram of a hardware environment of an optional information processing method provided according to an embodiment of the present application;

[0021] Figure 2 A schematic diagram of an optional information processing method flow according to an embodiment of the present application;

[0022] Figure 3 A block diagram of an optional information processing device provided according to an embodiment of the present application;

[0023] Figure 4 A schematic diagram of an optional electronic device structure provided in an embodiment of the present application. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0025] In the subsequent description, the suffixes such as "module", "component" or "unit" used to represent elements are only used to facilitate the description of the present application and have no specific meaning. Therefore, "module" and "component" can be used interchangeably.

[0026] In order to solve the problem mentioned in the background technology, according to one aspect of the embodiments of the present application, an embodiment of an information processing method is provided.

[0027] Optionally, in the embodiment of the present application, the above information processing method can be applied to Figure 1 In the hardware environment composed of the terminal 101 and the server 103 shown in FIG. Figure 1 As shown, the server 103 is connected to the terminal 101 via a network, and can be used to provide services for the terminal or a client installed on the terminal. A database 105 can be set on the server or independently of the server to provide data storage services for the server 103. The above-mentioned network includes but is not limited to: a wide area network, a metropolitan area network or a local area network, and the terminal 101 includes but is not limited to a PC, a mobile phone, a tablet computer, etc.

[0028] An information processing method in the embodiment of the present application can be executed by the server 103, or can be executed by the server 103 and the terminal 101 together, such as Figure 2 As shown, the method may include the following steps:

[0029] Step S202, obtaining initial comment data and initial comment environment information of a target topic, wherein the target topic is a problem discussed by multiple accounts in a social network;

[0030] Step S204, inputting the initial comment data and the initial comment environment information into the target macro model, so as to summarize the initial comment background of the target topic based on the initial comment environment information through the target macro model, and identify malicious comment data in the initial comment data based on the initial comment background, wherein the malicious comment data is comment data that guides public opinion away from the target topic;

[0031] Step S206, filtering out the malicious comment data from the initial comment data to obtain target comment data, and filtering out the malicious comment environment information corresponding to the malicious comment data from the initial comment environment information to obtain target comment environment information;

[0032] Step S208 inputs the target comment data and the target comment environment information into the target big model, so as to summarize the real comment background of the target topic based on the target comment environment information through the target big model, and classify and summarize the target comment data based on the real comment background.

[0033] In the embodiment of the present application, the target topic refers to a specific issue or topic discussed by multiple accounts in a social network. Initial comment data refers to all original comment content posted by users on the social network for the target topic. Initial comment environment information refers to environmental factors associated with the initial comment data, including the posting time, location, publisher information, emotional tendency, etc. of the comment, as well as the association between comments (such as replies, likes, etc.). The target large model is a pre-trained model based on deep learning technology, which can process natural language text and related information, and perform tasks such as comment background summary, malicious comment identification, comment classification and summary.

[0034] With the popularity of social networks, users are more inclined to express their opinions and comments on the platform. However, the existence of malicious comments will interfere with normal discussions, mislead public opinion, and may even have adverse effects on individuals or society. In this regard, this application uses AI big models to identify malicious comment data, feed the comment environment before and after filtering to the AI ​​big model, summarize and output the issues discussed before and after filtering, as well as the comment tendencies before and after filtering, remind users to pay attention to identifying problematic comments, and reflect the real public opinion after filtering to users. Specifically:

[0035] In an embodiment of the present application, initial comment data and initial comment environment information about the target topic are collected from social networks through web crawlers or other data acquisition technologies. The initial data is input into the target big model, and the big model first summarizes the initial comment background of the target topic based on the initial comment environment information. Then, the big model uses the malicious comment recognition ability learned in advance, combined with the initial comment background, to identify the malicious comment data in the initial comment data. Remove the identified malicious comment data from the initial comment data to obtain the target comment data that does not contain malicious comments. At the same time, remove the information corresponding to the malicious comment data from the initial comment environment information to obtain the target comment environment information. The target comment data and the target comment environment information are input into the target big model again. The big model summarizes the real comment background of the target topic based on more real and effective target comment environment information. Based on the real comment background, the big model classifies the target comment data (such as positive, negative, neutral, etc.) and generates a summary report that summarizes the core views, trends and emotional attitudes discussed in the target topic.

[0036] In the embodiment of the present application, in addition to text-type comment data, multimodal comment data (such as pictures, videos, audio, etc.) can be collected by extending the multimodal data interface to more comprehensively analyze and understand the user's comment intentions and emotions.

[0037] In the embodiments of the present application, when processing user data, the relevant laws and regulations on privacy protection and data security are strictly observed to ensure the security and legal use of user data, and sensitive information is desensitized to prevent user privacy leakage.

[0038] This application uses an AI big model to identify malicious comment data, feeds the comment environment before and after filtering to the AI ​​big model, summarizes and outputs the issues discussed before and after filtering, as well as the comment tendencies before and after filtering, summarizes comments with similar opinions, and reflects the real public opinion after filtering to users, solving the technical problem that users are easily trapped in information cocoons due to the inability to identify problematic comments.

[0039] In an optional embodiment, the step of obtaining the initial comment data and initial comment environment information of the target topic includes:

[0040] Step 1, obtaining the comment data to be identified under the target topic as the initial comment data;

[0041] Step 2, determining the account that sent the initial comment data as the account to be identified;

[0042] Step 3, obtaining the account information and historical dynamic information of the account to be identified, wherein the account information includes the registration time, account level and account name of the account;

[0043] Step 4: Determine the account information and the historical dynamic information of each account to be identified as the initial comment environment information.

[0044] In an embodiment of the present application, all comment data under the target topic is captured from the social networking platform through a web crawler or other data acquisition technology as the comment data to be identified. According to the publisher information of the comment data to be identified, the account of the issuer of each comment, that is, the account to be identified, is determined. Basic information such as the registration time, account level and account name of the account to be identified is obtained from the account management system of the social networking platform. Through the API interface or data capture technology, the historical content, likes, comments, sharing and other interactions posted by the account to be identified on the social networking platform are obtained. The account information and historical dynamic information of each account to be identified are integrated and associated to form initial comment environment information for subsequent data processing and analysis.

[0045] This application obtains the comment data to be identified and directly analyzes the discussion content under the target topic, obtains the account information and historical dynamic information of the account to be identified as the initial comment environment information, so as to fully understand the identity characteristics, behavior patterns, possible positions and biases of the commentator, thereby improving the accuracy and reliability of malicious comment identification.

[0046] In an optional embodiment, before inputting the initial review data and the initial review environment information into the target macro model, the method further includes obtaining the target macro model in the following manner:

[0047] Step 1, obtaining malicious comment samples and account data samples corresponding to the malicious comment samples;

[0048] Step 2: Analyze and sort the account data samples to obtain basic account information, historical dynamic information, and information about the issues being discussed when each malicious comment sample was published;

[0049] Step 3, inputting the malicious comment samples, the basic account information, the historical dynamic information and the problem information into an initial multi-category classification model for deep learning, so that the initial multi-category classification model performs weighted scoring analysis on the classification results of the malicious comment samples based on the malicious comment samples, the basic account information, the historical dynamic information and the problem information to learn to identify the malicious comment samples of various types;

[0050] Step 4: perform regression verification on the trained initial multi-category classification model, and when the verification result meets the target condition, determine the initial multi-category classification model as the target large model.

[0051] In the embodiments of the present application, malicious comment samples refer to comment data that has been confirmed to be malicious in social networks, such as robot comments, "water army" comments, or "reverse" comments. Account data samples are information about social network accounts corresponding to malicious comment samples, including basic account information, historical dynamic information, etc. The initial multi-category classification model is a deep learning model used for multi-category classification.

[0052] In an embodiment of the present application, the training process of the target large model is specifically as follows: a certain number of malicious comment samples and corresponding account data samples are collected. The collected account data samples are analyzed and sorted in detail to extract the basic information of the account (such as registration time, account level, etc.), historical dynamic information, and the problem information being discussed when each malicious comment is posted. The malicious comment samples, basic account information, historical dynamic information, and problem information are used as input data and input into the initial multi-category classification model. Through deep learning technology, the model learns how to classify malicious comment samples based on input data, and optimizes the accuracy of the classification results through weighted scoring analysis. Use an independent verification data set to perform regression verification on the trained initial multi-category classification model. If the verification result meets the preset target conditions (such as accuracy, recall rate, etc. reach a certain level), the model is determined as the final target large model; otherwise, it is necessary to adjust the model parameters or collect samples again for training.

[0053] By collecting and analyzing real malicious comment samples and their corresponding account data samples, this application can ensure that the model learns the characteristics and patterns of malicious comments and improve the accuracy and reliability of recognition. At the same time, through multi-category classification and weighted scoring analysis, the model can better distinguish different types of malicious comments and adapt to recognition needs in different environments and scenarios.

[0054] In an optional embodiment, the obtaining of malicious comment samples and account data samples corresponding to the malicious comment samples includes at least one of the following:

[0055] Acquire comments whose account level is less than a first threshold and whose creation time is less than a second threshold, and whose comment content similarity is greater than a third threshold as robot comment samples, and acquire account information and historical dynamic information of the robot comment samples as the account data samples corresponding to the robot comment samples, wherein the malicious comment samples include the robot comment samples;

[0056] Obtaining comments whose number of comments appearing in the same time is greater than a fourth threshold and whose comment content similarity is greater than a fifth threshold as water army comment samples, and obtaining account information and historical dynamic information of the water army comment samples as the account data samples corresponding to the water army comment samples, wherein the malicious comment samples include the water army comment samples;

[0057] Obtain comments whose content similarity to the account user portrait is less than a sixth threshold and whose number of comments is greater than a seventh threshold as anti-stringing comment samples, and obtain the account information and historical dynamic information of the anti-stringing comment samples as the account data samples corresponding to the anti-stringing comment samples, wherein the malicious comment samples include the anti-stringing comment samples.

[0058] In the embodiment of the present application, a newly created, low-level account that posts a large number of similar comments in a short period of time is considered to be a robot comment. The specific process of obtaining robot comment samples is as follows: set a first threshold and a second threshold to filter accounts with a small account level and creation time, respectively, check the comment content of these accounts, calculate the similarity, and when the similarity is greater than the third threshold, use these comments as robot comment samples, and collect the account information and historical dynamic information of these comment samples as account data samples.

[0059] In the embodiment of the present application, water army comments usually publish a large number of similar comments in a short period of time to create public opinion or mislead the public. Comments with this feature are considered water army comments. The specific process of obtaining water army comment samples is as follows: monitor the comment platform, find accounts with a number of comments greater than the fourth threshold at the same time, check the comment content of these accounts, calculate the similarity, and when the similarity is greater than the fifth threshold, use these comments as water army comment samples, and collect the account information and historical dynamic information of these comment samples as account data samples.

[0060] In the embodiment of the present application, in order to intentionally cause controversy or confuse the public, comments that are inconsistent with the normal behavior or user profile of the account can be considered as reverse comments. The specific process of obtaining reverse comment samples is as follows: analyze the user profile of the account, including normal behavior, interest preferences, etc., check the comment content of the account, calculate the similarity with the user profile, and when the similarity is less than the sixth threshold and the number of comments is greater than the seventh threshold, use these comments as reverse comment samples, and collect the account information and historical dynamic information of these comment samples as account data samples.

[0061] In the embodiment of the present application, the specific settings of the above-mentioned first threshold to the seventh threshold can be reasonably set according to the specific situation and data characteristics.

[0062] In an optional embodiment, the initial multi-category classification model performs weighted scoring analysis on the classification results of the malicious comment samples based on the malicious comment samples, the account basic information, the historical dynamic information, and the problem information to learn to identify each type of the malicious comment samples, including at least one of the following:

[0063] When it is determined that the account corresponding to the malicious comment sample has an account level less than the first threshold, a creation time less than the second threshold, and an account name regularity less than a preset degree threshold, the classification weight of the robot comment is increased according to the set step size to improve the score of classifying the malicious comment sample as the robot comment;

[0064] When it is determined that a plurality of the malicious comment samples appear at the same time and the similarity of the comment contents is greater than the fifth threshold, increasing the classification weight of the water army comments according to the set step length to improve the score of classifying the malicious comment samples as the water army comments;

[0065] When it is determined that a plurality of the malicious comment samples appear at the same time and the similarity between the comment content and the account user portrait is less than the sixth threshold, the classification weight of the anti-character comment is increased according to the set step size to improve the score of classifying the malicious comment samples as the anti-character comment.

[0066] In the embodiment of the present application, when training the malicious comment recognition model, different types of malicious comments often have different characteristics and patterns. Therefore, the present application performs weighted analysis on different classifications. Specifically, during the training process, feature extraction and analysis are performed on the input malicious comment samples and their corresponding account data samples. Features include account level, creation time, regularity of account name, historical dynamics, etc. According to the results of the feature analysis, the classification weights of the initial multi-category classification model for different types of malicious comments are adjusted. For robot comments, when the account level is less than the first threshold, the creation time is less than the second threshold, or the regularity of the account name is less than the preset threshold (such as the account name is a random combination of letters and numbers), the classification weight of the robot comments is increased. For water army comments, when multiple malicious comment samples appear at the same time and the similarity of the comment content is greater than the fifth threshold, the classification weight of the water army comments is increased. For reverse comments, when multiple malicious comment samples appear at the same time and the similarity of the comment content and the account user portrait is less than the sixth threshold, the classification weight of the reverse comments is increased. The weight increases, the higher the score of the corresponding category, and finally the model identifies the malicious comment data to be classified as the type with the highest score.

[0067] This application adjusts the classification weights so that the model can pay more attention to the characteristics and patterns of different types of malicious comments, thereby improving the recognition accuracy of different types of malicious comments. This helps to improve the generalization and robustness of the model, so that it can better adapt to the malicious comment recognition task in different environments and scenarios.

[0068] In an optional embodiment, after classifying and summarizing the target review data based on the real review background, the method further includes:

[0069] Step 1, determining the proportion of the malicious comment data and the target comment data in the initial comment data, and drawing a diagram according to the respective proportion data;

[0070] Step 2, displaying the diagram, marking the malicious comment data, and comparing the marked malicious comment data with the target comment data, so as to prompt the user to identify the malicious comment data while reflecting the real public opinion of the target topic.

[0071] In the embodiment of the present application, after the target comment data is classified and summarized by the target large model, the relationship between the malicious comment data and the target comment data can be further processed and analyzed, and these data can be displayed in a visual way, so that the user can more intuitively understand the real public opinion of the target theme and identify the malicious comments therein. Specifically, first, the initial comment data is collected, and the target comment data is classified and summarized using a multi-category classification model, and then the number or frequency of the malicious comment data and the target comment data is counted, and their proportion in the initial comment data is calculated. According to the proportion data, a suitable graphic type (such as a pie chart, a bar chart, etc.) is selected, and a drawing tool or library (such as Matplotlib, Plotly, etc.) is used to draw a diagram. In addition, the malicious comment data can also be directly marked on the social network using colors, text or other visual elements so that the user can identify it. The marked malicious comment data is compared with the target comment data and displayed, which can be a side-by-side diagram, or it can be distinguished by adjusting the color, size, etc. of the visual elements.

[0072] This application displays the comments before and after filtering in a visual way so that users can more intuitively understand the real public opinion of the target topic and identify malicious comments.

[0073] In an optional embodiment, the method further includes:

[0074] A data interface is opened to a target browser plug-in, so that the target browser plug-in processes the comment information in the social network through the data interface, wherein the target browser plug-in is embedded in a browser for front-end display.

[0075] In the embodiments of the present application, first, it is necessary to define the standards and specifications of the data interface, including the name, parameters, return value, etc. of the interface. These interfaces should be able to support operations such as obtaining, processing, and analyzing comment information in social networks. According to the defined data interface specifications, a corresponding back-end service or API is developed to process requests from browser plug-ins and return corresponding data or results. The plug-in developer develops a target browser plug-in based on demand. The plug-in needs to include the function of communicating with the data interface in order to obtain data or send requests from the back-end service. The developed browser plug-in is embedded in the browser so that the target browser plug-in processes the comment information in the social network through the data interface.

[0076] This application uses a browser plug-in, which allows users to operate and process comment information in social networks directly in the browser environment without jumping to other applications or platforms, greatly improving the convenience and consistency of the user experience. As an extension of browser functions, browser plug-ins can provide users with more customized services. Through open data interfaces, plug-in developers can develop various functions based on these data interfaces, such as comment screening, sentiment analysis, content recommendations, etc. Browser plug-ins usually run on the user's local device and have stronger data processing capabilities. Through the data interface, some data processing tasks can be transferred to the plug-in side to reduce the burden on the server side.

[0077] The present application provides an information processing method, comprising: obtaining initial comment data and initial comment environment information of a target topic, wherein the target topic is an issue discussed jointly by multiple accounts in a social network; inputting the initial comment data and the initial comment environment information into a target big model, so as to summarize the initial comment background of the target topic based on the initial comment environment information through the target big model, and identify malicious comment data in the initial comment data based on the initial comment background, wherein the malicious comment data is comment data that guides public opinion away from the target topic; filtering out the malicious comment data from the initial comment data to obtain target comment data, and filtering out the malicious comment environment information corresponding to the malicious comment data from the initial comment environment information to obtain target comment environment information; inputting the target comment data and the target comment environment information into the target big model, so as to summarize the real comment background of the target topic based on the target comment environment information through the target big model, and classifying and summarizing the target comment data based on the real comment background. This application uses an AI big model to identify malicious comment data, feeds the comment environment before and after filtering to the AI ​​big model, summarizes and outputs the issues discussed before and after filtering, as well as the comment tendencies before and after filtering, summarizes comments with similar opinions, and reflects the real public opinion after filtering to users, solving the technical problem that users are easily trapped in information cocoons due to the inability to identify problematic comments.

[0078] According to another aspect of the embodiment of the present application, Figure 3 As shown, an information processing device is provided, comprising:

[0079] An acquisition module 301 is used to acquire initial comment data and initial comment environment information of a target topic, wherein the target topic is a problem discussed by multiple accounts in a social network;

[0080] The identification module 303 is used to input the initial comment data and the initial comment environment information into the target macro model, so as to summarize the initial comment background of the target topic based on the initial comment environment information through the target macro model, and identify malicious comment data in the initial comment data based on the initial comment background, wherein the malicious comment data is comment data that guides public opinion away from the target topic;

[0081] The filtering module 305 is used to filter out the malicious comment data in the initial comment data to obtain target comment data, and filter out the malicious comment environment information corresponding to the malicious comment data in the initial comment environment information to obtain target comment environment information;

[0082] The summarizing module 307 is used to input the target comment data and the target comment environment information into the target big model, so as to summarize the real comment background of the target subject based on the target comment environment information through the target big model, and classify and summarize the target comment data based on the real comment background.

[0083] It should be noted that the acquisition module 301 in this embodiment can be used to execute step S202 in the embodiment of the present application, the identification module 303 in this embodiment can be used to execute step S204 in the embodiment of the present application, the filtering module 305 in this embodiment can be used to execute step S206 in the embodiment of the present application, and the summary module 307 in this embodiment can be used to execute step S208 in the embodiment of the present application.

[0084] It should be noted that the examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the contents disclosed in the above embodiments. It should be noted that the above modules as part of the device can be run in Figure 1 In the hardware environment shown, it can be implemented by software or by hardware.

[0085] Optionally, the acquisition module is specifically used to: acquire the comment data to be identified under the target topic as the initial comment data; determine the account that issued the initial comment data as the account to be identified; acquire the account information and historical dynamic information of the account to be identified, wherein the account information includes the account registration time, account level and account name; determine the account information and the historical dynamic information of each account to be identified as the initial comment environment information.

[0086] Optionally, the information system processing device also includes a model training module, which is specifically used to: obtain malicious comment samples and account data samples corresponding to the malicious comment samples; analyze and organize the account data samples to obtain the account basic information, historical dynamic information and the problem information being discussed when each malicious comment sample is published; input the malicious comment samples, the account basic information, the historical dynamic information and the problem information into the initial multi-category classification model for deep learning, so that the initial multi-category classification model performs weighted scoring analysis on the classification results of the malicious comment samples based on the malicious comment samples, the account basic information, the historical dynamic information and the problem information to learn to identify various types of malicious comment samples; perform regression verification on the trained initial multi-category classification model, and when the verification result meets the target condition, determine the initial multi-category classification model as the target large model.

[0087] Optionally, the model training module is also used to: obtain comments whose account level is less than a first threshold and whose creation time is less than a second threshold, and whose comment content similarity is greater than a third threshold as robot comment samples, and obtain the account information and historical dynamic information of the robot comment samples as the account data samples corresponding to the robot comment samples, wherein the malicious comment samples include the robot comment samples; obtain comments whose number of comments appearing at the same time is greater than a fourth threshold and whose comment content similarity is greater than a fifth threshold as water army comment samples, and obtain the account information and historical dynamic information of the water army comment samples as the account data samples corresponding to the water army comment samples, wherein the malicious comment samples include the water army comment samples; obtain comments whose comment content similarity to the account user portrait is less than a sixth threshold and whose number of comments is greater than a seventh threshold as anti-play comment samples, and obtain the account information and historical dynamic information of the anti-play comment samples as the account data samples corresponding to the anti-play comment samples, wherein the malicious comment samples include the anti-play comment samples.

[0088] Optionally, the model training module is also used to: when it is determined that the account corresponding to the malicious comment sample has any of the following situations: the account level is less than the first threshold, the creation time is less than the second threshold, and the regularity of the account name is less than a preset threshold, increase the classification weight of the robot comment according to the set step size to improve the score of classifying the malicious comment sample as the robot comment; when it is determined that multiple malicious comment samples appear at the same time and the similarity of the comment content is greater than the fifth threshold, increase the classification weight of the water army comment according to the set step size to improve the score of classifying the malicious comment sample as the water army comment; when it is determined that multiple malicious comment samples appear at the same time and the similarity of the comment content and the account user portrait is less than the sixth threshold, increase the classification weight of the anti-stringing comment according to the set step size to improve the score of classifying the malicious comment sample as the anti-stringing comment.

[0089] Optionally, the information system processing device also includes a data visualization module, which is specifically used to: determine the proportion of the malicious comment data and the target comment data in the initial comment data, and draw a diagram based on their respective proportion data; display the diagram, mark the malicious comment data, and compare and display the marked malicious comment data with the target comment data, so as to prompt the user to identify the malicious comment data while reflecting the true public opinion of the target topic.

[0090] Optionally, the information system processing device also includes an interface module, which is specifically used to: open a data interface to a target browser plug-in so that the target browser plug-in processes comment information in the social network through the data interface, wherein the target browser plug-in is embedded in the browser for front-end display.

[0091] According to another aspect of the embodiments of the present application, the present application provides an electronic device, such as Figure 4 As shown, it includes a memory 401, a processor 403, a communication interface 405 and a communication bus 407. The memory 401 stores a computer program that can be run on the processor 403. The memory 401 and the processor 403 communicate through the communication interface 405 and the communication bus 407. When the processor 403 executes the computer program, the steps of the above method are implemented.

[0092] The memory and processor in the above electronic device communicate via a communication bus and a communication interface. The communication bus may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus may be divided into an address bus, a data bus, a control bus, etc.

[0093] The memory may include a random access memory (RAM) or a non-volatile memory, such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0094] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0095] According to another aspect of the embodiments of the present application, a computer program product or a computer program is provided, the computer program product or the computer program includes computer instructions, and the computer instructions are stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device performs the steps of any of the above embodiments.

[0096] Optionally, in an embodiment of the present application, the computer-readable medium is configured to store program codes for the processor to perform the following steps:

[0097] Acquire initial comment data and initial comment environment information of a target topic, wherein the target topic is a problem discussed by multiple accounts in a social network;

[0098] The initial comment data and the initial comment environment information are input into the target macro model, so as to summarize the initial comment background of the target topic based on the initial comment environment information through the target macro model, and identify malicious comment data in the initial comment data based on the initial comment background, wherein the malicious comment data is comment data that guides public opinion away from the target topic;

[0099] Filtering out the malicious comment data from the initial comment data to obtain target comment data, and filtering out malicious comment environment information corresponding to the malicious comment data from the initial comment environment information to obtain target comment environment information;

[0100] The target review data and the target review environment information are input into the target big model, so as to summarize the real review background of the target subject based on the target review environment information through the target big model, and classify and summarize the target review data based on the real review background.

[0101] Optionally, the specific examples in this embodiment may refer to the examples described in the above embodiments, and this embodiment will not be described in detail here.

[0102] When the embodiments of the present application are specifically implemented, reference may be made to the above-mentioned embodiments, which have corresponding technical effects.

[0103] It is understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For hardware implementation, the processing unit may be implemented in one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), general purpose processors, controllers, microcontrollers, microprocessors, other electronic units for performing the functions described in the present application, or a combination thereof.

[0104] For software implementation, the technology described herein can be implemented by a unit that performs the functions described herein. The software code can be stored in a memory and executed by a processor. The memory can be implemented in the processor or outside the processor.

[0105] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0106] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0107] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0108] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0109] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0110] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the embodiment of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium, including several instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a ROM, a RAM, a magnetic disk or an optical disk. It should be noted that, in this article, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. Moreover, the term "include", "include" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such a process, method, article or device. Without more constraints, an element defined by the phrase "comprising a..." does not exclude the existence of other identical elements in the process, method, article or apparatus comprising the element.

[0111] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest range consistent with the principles and novel features applied for herein.

Claims

1. An information processing method, characterized in that: include: Acquire initial comment data and initial comment environment information of a target topic, wherein the target topic is a problem discussed by multiple accounts in a social network; The initial comment data and the initial comment environment information are input into the target macro model, so as to summarize the initial comment background of the target topic based on the initial comment environment information through the target macro model, and identify malicious comment data in the initial comment data based on the initial comment background, wherein the malicious comment data is comment data that guides public opinion away from the target topic; Filtering out the malicious comment data from the initial comment data to obtain target comment data, and filtering out malicious comment environment information corresponding to the malicious comment data from the initial comment environment information to obtain target comment environment information; The target review data and the target review environment information are input into the target big model, so as to summarize the real review background of the target subject based on the target review environment information through the target big model, and classify and summarize the target review data based on the real review background.

2. The method according to claim 1, characterized in that: The acquisition of the initial comment data and initial comment environment information of the target topic includes: Acquire the comment data to be identified under the target topic as the initial comment data; Determine the account that sent the initial comment data as the account to be identified; Acquire the account information and historical dynamic information of the account to be identified, wherein the account information includes the registration time, account level and account name of the account; The account information and the historical dynamic information of each account to be identified are determined as the initial comment environment information.

3. The method according to claim 1, characterized in that Before inputting the initial review data and the initial review environment information into the target macro model, the method further includes obtaining the target macro model in the following manner: Obtain malicious comment samples and account data samples corresponding to the malicious comment samples; Analyze and organize the account data samples to obtain basic account information, historical dynamic information, and information about the issues being discussed when each malicious comment sample was published; Inputting the malicious comment samples, the basic account information, the historical dynamic information and the problem information into an initial multi-category classification model for deep learning, so that the initial multi-category classification model performs weighted scoring analysis on the classification results of the malicious comment samples based on the malicious comment samples, the basic account information, the historical dynamic information and the problem information to learn to identify various types of the malicious comment samples; The initial multi-category classification model that has been trained is subjected to regression verification, and when the verification result reaches the target condition, the initial multi-category classification model is determined as the target large model.

4. The method according to claim 3, characterized in that The obtaining of malicious comment samples and account data samples corresponding to the malicious comment samples comprises at least one of the following: Acquire comments whose account level is less than a first threshold and whose creation time is less than a second threshold, and whose comment content similarity is greater than a third threshold as robot comment samples, and acquire account information and historical dynamic information of the robot comment samples as the account data samples corresponding to the robot comment samples, wherein the malicious comment samples include the robot comment samples; Obtaining comments whose number of comments appearing in the same time is greater than a fourth threshold and whose comment content similarity is greater than a fifth threshold as water army comment samples, and obtaining account information and historical dynamic information of the water army comment samples as the account data samples corresponding to the water army comment samples, wherein the malicious comment samples include the water army comment samples; Obtain comments whose content similarity to the account user portrait is less than a sixth threshold and whose number of comments is greater than a seventh threshold as anti-stringing comment samples, and obtain the account information and historical dynamic information of the anti-stringing comment samples as the account data samples corresponding to the anti-stringing comment samples, wherein the malicious comment samples include the anti-stringing comment samples.

5. The method according to claim 4, characterized in that The step of enabling the initial multi-category classification model to perform weighted scoring analysis on the classification results of the malicious comment samples based on the malicious comment samples, the basic account information, the historical dynamic information, and the problem information to learn to identify each type of the malicious comment samples includes at least one of the following: When it is determined that the account corresponding to the malicious comment sample has an account level less than the first threshold, a creation time less than the second threshold, and an account name regularity less than a preset degree threshold, the classification weight of the robot comment is increased according to the set step size to improve the score of classifying the malicious comment sample as the robot comment; When it is determined that a plurality of the malicious comment samples appear at the same time and the similarity of the comment contents is greater than the fifth threshold, increasing the classification weight of the water army comments according to the set step length to improve the score of classifying the malicious comment samples as the water army comments; When it is determined that a plurality of the malicious comment samples appear at the same time and the similarity between the comment content and the account user portrait is less than the sixth threshold, the classification weight of the anti-character comment is increased according to the set step size to improve the score of classifying the malicious comment samples as the anti-character comment.

6. The method according to claim 1, characterized in that After classifying and summarizing the target review data based on the real review background, the method further includes: Determine the proportion of the malicious comment data and the target comment data in the initial comment data, and draw a diagram according to the respective proportion data; The diagram is displayed, and the malicious comment data is marked, and the marked malicious comment data is compared with the target comment data for display, so as to reflect the real public opinion of the target topic and prompt the user to identify the malicious comment data.

7. The method according to any one of claims 1 to 6, characterized in that: The method further comprises: A data interface is opened to a target browser plug-in, so that the target browser plug-in processes the comment information in the social network through the data interface, wherein the target browser plug-in is embedded in a browser for front-end display.

8. An information processing device, characterized in that: include: An acquisition module, used to acquire initial comment data and initial comment environment information of a target topic, wherein the target topic is a problem discussed by multiple accounts in a social network; an identification module, for inputting the initial comment data and the initial comment environment information into a target macro model, so as to summarize the initial comment background of the target topic based on the initial comment environment information through the target macro model, and identify malicious comment data in the initial comment data based on the initial comment background, wherein the malicious comment data is comment data that guides public opinion away from the target topic; A filtering module, configured to filter out the malicious comment data from the initial comment data to obtain target comment data, and to filter out the malicious comment environment information corresponding to the malicious comment data from the initial comment environment information to obtain target comment environment information; A summarizing module is used to input the target comment data and the target comment environment information into the target big model, so as to summarize the real comment background of the target subject based on the target comment environment information through the target big model, and classify and summarize the target comment data based on the real comment background.

9. An electronic device, comprising a memory, a processor, a communication interface and a communication bus, wherein the memory stores a computer program that can be run on the processor, and the memory and the processor communicate through the communication bus and the communication interface, characterized in that: When the processor executes the computer program, the information processing method according to any one of claims 1 to 7 is implemented.

10. A computer readable medium having a non-volatile program code executable by a processor, characterized in that: The program code enables the processor to execute the information processing method according to any one of claims 1 to 7.

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