A news dissemination optimization method and system based on new media technology

Abstract

The present invention discloses an optimization method and system for news dissemination based on new media technology, belonging to the field of news dissemination technology, including an application recognition module, an application analysis module, a news calibration module, and a release optimization module; the application recognition module is used to perform real-time analysis on the benchmark news method to obtain a news detail map, and the application analysis module is used to perform real-time analysis on the news detail map to obtain the recommended optimization method for the corresponding news nodes in the news detail map; the news calibration module is used to perform calibration analysis on the news content and perform corresponding processing on the corresponding news content according to the content calibration result; the release optimization module is used to perform optimization processing on the release of the news content, determine the target news according to the content calibration result; perform release retrieval on the target news to obtain several reference news, screen the reference news to obtain the release reference news, identify the release information of the release reference news; and perform supplementary release processing on the target news according to the release information.

Description

Technical Field

[0001] The present invention belongs to the technical field of news dissemination, and particularly relates to a news dissemination optimization method and system based on new media technology. Background Art

[0002] In recent years, new media technology has profoundly changed the technical pattern and application mode in the field of news dissemination. Technologies such as artificial intelligence, big data, and blockchain have gradually penetrated into the news production, distribution, and interaction links, significantly improving the dissemination efficiency and credibility.

[0003] Based on this, in order to solve the drawbacks of existing news dissemination, the present invention provides a news dissemination optimization method and system based on new media technology. Summary of the Invention

[0004] In order to solve the problems existing in the above solutions, the present invention provides a news dissemination optimization method and system based on new media technology.

[0005] The object of the present invention can be achieved by the following technical solutions:

[0006] A news dissemination optimization system based on new media technology includes an application recognition module, an application analysis module, a news calibration module, and a publishing optimization module;

[0007] The application recognition module is used to perform real-time analysis on the benchmark news method to obtain a news detail map of the benchmark news method, where the benchmark news method is the news process method currently in use; the news detail map is composed of news nodes and the node features of the corresponding news nodes.

[0008] Further, performing real-time analysis on the benchmark news method includes:

[0009] Identifying the corresponding news nodes according to the benchmark news method, determining the sequential relationship between each news node, and generating a node flow chart according to the sequential relationship between each news node;

[0010] Performing real-time feature recognition on the benchmark news method according to the news nodes to obtain the node features corresponding to the news nodes; supplementing the node features to the node flow chart, and marking the supplemented node flow chart as the news detail map.

[0011] The application analysis module is used to perform real-time analysis on the news detail map to obtain the recommended optimization methods for the corresponding news nodes in the news detail map, obtain the recommended optimization information of the recommended optimization methods, and display the recommended optimization information to the user.

[0012] Further, performing real-time analysis on the news detail map includes:

[0013] The platform party establishes a new media technology library and updates the new media technology library in real time; the new media technology library is used to store various intelligent technology information applied to the news process and the technical feature data of the corresponding intelligent technologies;

[0014] Identify the node features of each news node in the news detail map, perform technology matching in the new media technology library according to the node features, and obtain the intelligent technology information applicable to the news node; obtain the corresponding technical feature data according to the intelligent technology information, and determine the performance value and adaptation value of the intelligent technology according to the technical feature data; calculate the first optimization value of the intelligent technology according to the first optimization formula, and the first optimization formula is:

[0015] ;

[0016] In the formula: YA is the first optimization value; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1; XZ is the performance value; SZ is the adaptation value;

[0017] Judge whether the intelligent technology meets the recommended optimization requirements according to the first optimization value, and obtain the recommended judgment result of the intelligent technology; mark the intelligent technology with the recommended judgment result that meets the recommended optimization requirements as the recommended optimization method.

[0018] Furthermore, record the recommended judgment results of each intelligent technology in real time, and screen the matched intelligent technologies when performing technology matching according to the node features.

[0019] The news calibration module is used to calibrate and analyze the news content, obtain the content calibration result of the corresponding news content, and the content calibration result includes authenticity anomaly, content hollowing and normal content verification; perform corresponding processing on the corresponding news content according to the content calibration result.

[0020] Furthermore, calibrating and analyzing the news content includes:

[0021] The platform party establishes a feature recognition library, and the feature recognition library is used to store abnormal feature groups and the abnormal probabilities corresponding to the abnormal feature groups, and the abnormal feature groups are news feature combinations corresponding to authenticity anomalies or content hollowing;

[0022] Establish a content calibration model according to the feature recognition library, and the expression of the content calibration model is:

[0023] ;

[0024] Where: NS is the news content, the output data is the content verification value NP(NS), and the content verification value is 1, 2, or 0; meeting the first verification standard indicates that the content calibration result of the corresponding news content is content hollowing; meeting the second verification standard indicates that the content calibration result of the corresponding news content is authenticity anomaly;

[0025] Analyze the news content through the content calibration model to obtain the content verification value of the news content, and determine the content calibration result of the news content according to the content verification value.

[0026] Further, when the content calibration result of the news content is authenticity anomaly, perform false proof conversion on the news content according to a preset conversion method to obtain news conversion content, and display the news conversion content, the news content, and the content calibration result to the user.

[0027] The release optimization module is used to optimize the release of news content, real-time identify the content calibration result of the news content, and mark the news content with a content calibration result that is not content verification normal as the target news;

[0028] Conduct a release search based on the target news to obtain several reference news related to the target news, screen the reference news to obtain release reference news, and identify the release information of the release reference news; perform supplementary release processing on the target news according to the release information.

[0029] Further, screening the reference news includes:

[0030] Deduplicate the reference news, analyze the deduplicated reference news through the content calibration model to obtain the content calibration result of the reference news; delete the reference news with a content calibration result of content verification normal; mark the remaining reference news as release reference news.

[0031] Further, optimize and adjust the release information of the release reference news.

[0032] Further, optimizing and adjusting the release information includes:

[0033] Identify the publisher corresponding to the release information, and obtain the news optimization characteristics of the publisher; establish a release optimization model, and the expression of the release optimization model is:

[0034] ;

[0035] Where: YT iIt represents the news optimization features corresponding to the publisher of the release information, where i = 1, 2, ……, n, and n is the number of publishers corresponding to the release information; the output data is the release optimization value PR(YT i ), and the release optimization value is 1 or 0;

[0036] Analyze the news optimization features according to the release optimization model to obtain the release optimization value of the publisher; optimize and adjust the release information according to the release optimization value.

[0037] A news dissemination optimization method based on new media technology, the method includes:

[0038] Conduct real-time analysis on the benchmark news method to obtain the news detail map of the benchmark news method;

[0039] Conduct real-time analysis on the news detail map to obtain the recommended optimization methods of the corresponding news nodes in the news detail map, obtain the recommended optimization information of the recommended optimization methods, and display the recommended optimization information to the user;

[0040] Conduct calibration analysis on the news content to obtain the content calibration result of the corresponding news content, and perform corresponding processing on the corresponding news content according to the content calibration result;

[0041] Mark the news content with normal content calibration results as target news; conduct release retrieval based on the target news to obtain several reference news related to the target news, screen the reference news to obtain the release reference news, identify the release information of the release reference news; perform supplementary release processing on the target news according to the release information.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] Through the mutual cooperation between the application recognition module, the application analysis module, the news calibration module and the release optimization module, the dynamic optimization of news dissemination based on new media technology is realized; the optimized news dissemination method can more accurately locate the target audience, thereby expanding the coverage of the news and enabling more potential readers to access relevant information; by solving the problems of news hollowing and reduced credibility; at the same time, based on the content calibration results, supplementary optimization processing of news release is carried out, and news dissemination is carried out more specifically. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0045] Figure 1 This is the principle block diagram of the present invention. Specific implementation manners

[0046] The technical solutions of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0047] As Figure 1 shown, a news dissemination optimization system based on new media technology includes an application recognition module, an application analysis module, a news calibration module, and a publishing optimization module;

[0048] The application recognition module is used to analyze the current news process mode, mark the current news process mode of the application as the reference news mode, and set it according to the news optimization needs of the user, such as the entire process from news generation to distribution; identify news nodes according to the reference news mode, such as news nodes such as information collection and topic selection, news production, content review, and content distribution, and specifically determine them in combination with the actual news process at the user's place; generate a node flow chart according to the reference news mode and news nodes, that is, determine the sequential relationship of each news node according to the reference news mode, and then generate a node flow chart according to the sequential relationship and news nodes;

[0049] Perform real-time feature recognition on the reference news mode according to the node flow chart to obtain the node features of the corresponding news nodes. The node features are recognized according to the actual application mode of the news node. For example, for information collection and topic selection, it can be through various methods such as on-site reporter interviews and network collection; supplement the node features to the node flow chart to form a news details map.

[0050] The application analysis module is used to perform real-time analysis on the news details map, determine the recommended optimization methods for the corresponding news nodes, obtain the recommended optimization information of the recommended optimization methods, and display the recommended optimization information to the user. Subsequently, the user decides whether to perform optimization and adjustment according to the recommended optimization methods.

[0051] In one embodiment, performing real-time analysis on the news details map includes:

[0052] The platform party establishes a new media technology library according to the development of new media technologies. The new media technology library is used to store various intelligent technology information that can be applied to the news process, such as various relevant intelligent technologies corresponding to artificial intelligence, big data, blockchain, 5G communication technology, virtual reality, etc.; Exemplarily, big data technology can analyze user behavior, build portraits, and achieve precise push; Artificial intelligence helps with automated production and personalized recommendation; Blockchain technology ensures the authenticity of news and the traceability of sources; 5G technology accelerates content transmission and supports high-definition multimedia live broadcast; Virtual reality and augmented reality can create immersive news experiences; The intelligent technology information is the introduction data of the corresponding intelligent technology, such as relevant data like technology name, advantages, application directions, etc. For the same intelligent technology, different technologies will be formed according to different application methods. For example, artificial intelligence is just a general concept, and the actual intelligent technologies formed are diverse.

[0053] Analyze the intelligent technologies corresponding to each intelligent technology information in the new media technology library, determine the performance values corresponding to each news node within the applicable range of the corresponding intelligent technology and the adaptation values with other various news node application technologies, and integrate them into the technical feature data of the intelligent technology. Both the performance value and the adaptation value adopt the same percentage value or decimal value, etc. Specify the performance values and adaptation values corresponding to the best and worst cases of the corresponding news nodes respectively. Subsequently, set several reference standards and corresponding performance values and adaptation values in the middle. Subsequently, match the performance values and adaptation values according to actual applications. The performance value is set according to the application effect of the intelligent technology, and the adaptation value is set according to the overall adaptation situation of the intelligent technology; Generally, when determining the intelligent technology, the staff set the technical feature data, which can be set by means of actual simulation verification, experimental data summary, etc.; It is also possible to establish an intelligent model based on intelligent technologies such as deep learning algorithms to intelligently extract technical adjustment data; Store the technical feature data in the new media technology library; And dynamically update the new media technology library according to technological development.

[0054] Identify the node features of each news node in the news details map, perform technology matching in the new media technology library according to the node features, and obtain the intelligent technology information applicable to the news node; Obtain the corresponding technical feature data according to the intelligent technology information, and determine the performance value and adaptation value corresponding to the intelligent technology according to the technical feature data; Calculate the first optimization value of the corresponding intelligent technology according to the first optimization formula. The first optimization formula is:

[0055] YA = b1×XZ + b2×SZ;

[0056] In the formula: YA is the first optimization value; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1; XZ is the performance value; SZ is the adaptation value;

[0057] Determine the first optimization value of the technologies applied to each news node in the news details map according to the calculation method of the first optimization value, which is marked as the node application value. The node application values of each news node can be synchronously determined by the platform when building the news details map, or can be determined by other means; compare the first optimization value with the node application value of the corresponding news node to judge whether the corresponding intelligent technology meets the optimization recommendation requirements, such as only being greater than the node application value, being greater than a preset difference compared with the node application value, etc., to obtain the corresponding recommendation judgment result; mark the corresponding intelligent technology as the recommended optimization method according to the recommendation judgment result, and set the recommended optimization information according to the corresponding intelligent technology information and the first optimization value, which is used to display the corresponding recommended optimization method to the user in detail.

[0058] In one embodiment, the recommendation judgment results of each intelligent technology are recorded in real time, and the matched intelligent technologies are screened when matching technologies according to node characteristics based on the recommendation judgment results; that is, the intelligent technologies that have been recorded and will not be used as the current news node are screened to improve the data analysis efficiency.

[0059] The news calibration module is used to perform calibration analysis on the news content to obtain the content calibration result of the corresponding news content. The content calibration result includes authenticity anomaly, content hollowing, and normal content verification; perform corresponding processing on the corresponding news content according to the content calibration result; for example, for content hollowing, the news content can be readjusted, and for authenticity anomaly, a warning can be issued. After determining false information, news is generated in reverse to inform the public of the false information, and specific processing is carried out according to the processing methods of different preset content calibration results.

[0060] In one embodiment, to perform calibration analysis on the news content, calibration can be carried out based on existing calibration methods. For example, an intelligent calibration model is established using a deep learning algorithm, and a corresponding training set is established and trained manually. The training set includes input data and output data. The input data is news content-related information, including news content, content source, and other information related to the generation of this news content, which is mainly used to analyze its authenticity; the output data is the content calibration result, and calibration analysis is carried out according to the intelligent calibration model after successful training to obtain the corresponding content calibration result.

[0061] In one embodiment, the calibration analysis of the news content includes:

[0062] Statistically analyze the abnormal feature groups and abnormal probabilities corresponding to news hollowing and false news content respectively based on technologies such as big data, that is, which feature combinations may be news hollowing or false news content, and form the probability that such an abnormal feature combination is corresponding news hollowing or false news content, and conduct statistics according to the share ratio; mark the corresponding authenticity abnormal label or content hollowing label for the abnormal feature group; integrate the obtained abnormal feature group and abnormal probability to establish a feature recognition library, and dynamically update the feature recognition library;

[0063] Establish a content calibration model based on the feature recognition library. The content calibration model is used to analyze the news content based on the feature recognition library, perform feature matching on the news content according to the feature recognition library, and determine whether the news content has a matching abnormal feature group. If there is no matching abnormal feature group, the content calibration result is that the content check is normal. If there is an abnormal feature group, identify the corresponding abnormal probability, and judge whether the content check is normal, authenticity abnormal or content hollowing according to the abnormal probability; use the set training set for training, and at the same time combine the subsequent manual check results according to the content calibration result for learning and adjustment. The main adjustment is the corresponding check standard, which is divided into the first check standard and the second check standard. Meeting the first check standard indicates that it is content hollowing; meeting the second check standard indicates that it is authenticity abnormal; the expression of the content calibration model is:

[0064] ;

[0065] In the formula: NS is the input data, that is, the news content, and the output data is the content check value NP(NS). The content check value is 1, 2 or 0; because the news content can meet the first check standard and the second check standard at the same time, so a news content can have two content check values;

[0066] Analyze the news content through the content calibration model to obtain the content check value of the corresponding news content, and determine the content calibration result of the news content according to the content check value.

[0067] In one embodiment, when the content calibration result of the news content is authenticity abnormal, perform false proof conversion on the corresponding news content according to the preset conversion method to obtain a new news content, mark the new news content as news conversion content, and display the news conversion content, news content and content calibration result to the user, and then the user will perform manual determination and calibration.

[0068] The conversion method is to convert the news content in the direction of indicating that it is false information. For the method of news intelligent generation, it can be converted according to the corresponding news generation method; it can also establish an intelligent model for news content conversion based on existing intelligent algorithms, etc., such as establishing an intelligent model based on a CNN network or a DNN network, and training it through an artificial method to establish a corresponding training set. The training set includes input data and output data. The input data is news content-related data, such as news content, content source, and other relevant data, and the output data is news conversion content; through the trained intelligent model for conversion analysis, a preset conversion method is formed.

[0069] The said release optimization module is used to optimize the release of news content, which refers to the news content waiting to be released after undergoing multiple processes such as verification; the news content processed due to abnormal authenticity or content hollowness according to the content calibration result is marked as the target news, that is, the target news includes the news content processed due to content hollowness or abnormal authenticity, including news content with processing measures such as deletion and adjustment. For the deletion situation, the original news content is used as the target news; for other news content, it is released according to the preset release method, mainly optimizing the release of the target news because the target news has a more targeted release background.

[0070] Conduct a release search based on the target news to obtain several reference news related to the target news, such as related to the original news content, related to the processed news content, etc., that is, including relevant news about the falsity and hollowness of the target news, as well as relevant news equivalent to the target news; de-duplicate the reference news, that is, only include one of the same reference news, but record the respective release information corresponding to the reference news, that is, which release entities it is released by, to form the release information of each reference news.

[0071] Analyze the reference news according to the content calibration model to obtain the content calibration result of the corresponding reference news, and delete the reference news with a normal content calibration result; mark the remaining reference news as the release reference news.

[0072] Identify the release information of the release reference news and conduct supplementary release processing on the target news according to the release information; specifically, conduct supplementary release processing according to the user's release requirements, such as conducting associated supplementary release on the corresponding release channels of the corresponding publisher, directly releasing under the news of the publisher, identifying the viewers of the corresponding release reference news, and releasing according to the composition of the corresponding viewers, etc. Specifically, there are various ways to conduct supplementary release processing based on the clear publisher and release reference news.

[0073] In one embodiment, in order to improve resource utilization, the release information corresponding to the release reference information is analyzed to determine whether it meets the requirements for supplementary release processing. The judgment is mainly carried out on the news view count, the number of comments, the news popularity, etc. The publishers who do not meet the requirements are removed from the release information to optimize the release information.

[0074] In one embodiment, the release information can be optimized and adjusted based on the existing method. For example, an intelligent model is established based on a neural network and trained by manually establishing a corresponding training set. The training set includes input data and output data. The input data is the news optimization features corresponding to the publishers in the release information, mainly the features related to the news view count, the number of comments, the news popularity, etc. The output data is whether the corresponding publisher meets the requirements for supplementary release processing. After successful training, the intelligent model is used for analysis, and then the release information is optimized and adjusted.

[0075] In one embodiment, optimizing and adjusting the release information includes:

[0076] Identifying the publisher corresponding to the release information and obtaining the news optimization features corresponding to the publisher; establishing a release optimization model and training it by setting a training set according to relevant historical data or a manually set method. The training set includes input data and output data. The input data is the news optimization features, and the output data is whether the corresponding publisher meets the requirements for supplementary release processing; the expression of the release optimization model is:

[0077] ;

[0078] In the formula: YT i represents the news optimization features of the publisher corresponding to the release information, i = 1, 2,..., n, where n is the number of publishers corresponding to the release information; the output data is the release optimization value PR(YT i ), and the release optimization value is 1 or 0;

[0079] Analyzing the news optimization features according to the release optimization model to obtain the release optimization value of the corresponding publisher; optimizing and adjusting the release information according to the release optimization value, and removing the publisher information with a release optimization value of 0.

[0080] A news dissemination optimization method based on new media technology, the method includes:

[0081] Performing real-time analysis on the benchmark news method to obtain the news details map of the benchmark news method;

[0082] Performing real-time analysis on the news details map to obtain the recommended optimization method of the corresponding news node in the news details map, obtaining the recommended optimization information of the recommended optimization method, and displaying the recommended optimization information to the user;

[0083] Calibrate and analyze the news content to obtain the content calibration result of the corresponding news content, and perform corresponding processing on the corresponding news content according to the content calibration result;

[0084] Mark the news content with normal content calibration result as the target news; perform a release search based on the target news to obtain a number of reference news related to the target news, screen the reference news to obtain the released reference news, and identify the release information of the released reference news; perform supplementary release processing on the target news according to the release information.

[0085] The above formulas are all calculated by removing the dimension and taking the numerical value. The formula is obtained by collecting a large amount of data for software simulation to get a formula closest to the actual situation. The preset parameters and preset thresholds in the formula are set by those skilled in the art according to the actual situation or obtained by a large amount of data simulation.

[0086] The above embodiments are only used to illustrate the technical method of the present invention and not to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical method of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A news dissemination optimization system based on new media technology, characterized in that, It includes an application recognition module, an application analysis module, a news calibration module, and a release optimization module; The application recognition module is used to perform real-time analysis on the benchmark news mode to obtain a news detail map of the benchmark news mode, where the benchmark news mode is the news process mode of the current application; the news detail map consists of news nodes and the node features of the corresponding news nodes; The application analysis module is used to perform real-time analysis on the news detail map to obtain the recommended optimization method for the corresponding news nodes in the news detail map, obtain the recommended optimization information of the recommended optimization method, and display the recommended optimization information to the user; The news calibration module is used to perform calibration analysis on the news content to obtain the content calibration result of the corresponding news content. The content calibration result includes authenticity anomaly, content hollowing, and normal content verification; Perform corresponding processing on the corresponding news content according to the content calibration result; The release optimization module is used to perform optimization processing on the release of the news content, real-time identify the content calibration result of the news content, and mark the news content with the content calibration result not being normal content verification as the target news; Perform a release search based on the target news to obtain several reference news related to the target news, screen the reference news to obtain the release reference news, and identify the release information of the release reference news; Perform supplementary release processing on the target news according to the release information; Performing real-time analysis on the news detail map includes: The platform party establishes a new media technology library and updates it in real time; the new media technology library is used to store various intelligent technology information applied to the news process and the technical feature data of the corresponding intelligent technologies; the technical feature data is integrated from the performance values corresponding to each news node within the applicable range of the intelligent technology and the adaptation values with other various news node application technologies; the performance value is set according to the application effect of the intelligent technology, and the adaptation value is set according to the overall adaptation situation of the intelligent technology; Identify the node features of each news node in the news detail map, perform technology matching in the new media technology library according to the node features to obtain the intelligent technology information applicable to the news node; obtain the corresponding technical feature data according to the intelligent technology information, and determine the performance value and adaptation value of the intelligent technology according to the technical feature data; calculate the first optimization value of the intelligent technology according to the first optimization formula, and the first optimization formula is: YA = b1×XZ + b2×SZ; In the formula: YA is the first optimization value; b1 and b2 are both proportionality coefficients, and the value range is 0 < b1 ≤ 1, 0 < b2 ≤ 1; XZ is the performance value; SZ is the adaptation value; Judge whether the intelligent technology meets the recommended optimization requirements according to the first optimization value to obtain the recommended judgment result of the intelligent technology; mark the intelligent technology with the recommended judgment result meeting the recommended optimization requirements as the recommended optimization method.

2. The news dissemination optimization system based on new media technology according to claim 1, characterized in that Performing real-time analysis on the benchmark news mode includes: Identify the corresponding news nodes according to the reference news method, determine the sequential relationship between each news node, and generate a node flow chart based on the sequential relationship between each news node; Perform real-time feature recognition on the reference news method according to the news nodes to obtain the node features corresponding to the news nodes; supplement the node features to the node flow chart, and mark the supplemented node flow chart as the news details map.

3. An optimized news dissemination system based on new media technology according to claim 1, characterized in that, Record the recommendation judgment results of each intelligent technology in real time, and screen the matching intelligent technologies when performing technology matching according to the node features according to the recommendation judgment results.

4. An optimized news dissemination system based on new media technology according to claim 1, characterized in that Perform calibration analysis on the news content, including: The platform party establishes a feature recognition library, which is used to store abnormal feature groups and the corresponding abnormal probabilities of the abnormal feature groups. The abnormal feature groups are news feature combinations corresponding to authenticity anomalies or content hollowing; Establish a content calibration model according to the feature recognition library. The expression of the content calibration model is: ； In the formula: NS is the news content, the output data is the content verification value NP(NS), and the content verification value is 1, 2 or 0; meeting the first verification standard indicates that the content calibration result of the corresponding news content is content hollowing; meeting the second verification standard indicates that the content calibration result of the corresponding news content is an authenticity anomaly; Analyze the news content through the content calibration model to obtain the content verification value of the news content, and determine the content calibration result of the news content according to the content verification value.

5. The news dissemination optimization system based on new media technology according to claim 4, characterized in that When the content calibration result of the news content is an authenticity anomaly, perform false proof conversion on the news content according to a preset conversion method to obtain news conversion content, and display the news conversion content, the news content and the content calibration result to the user.

6. The news dissemination optimization system based on new media technology according to claim 1, wherein Screen the reference news, including: Deduplicate the reference news, analyze the deduplicated reference news through the content calibration model to obtain the content calibration result of the reference news; delete the reference news with the content calibration result of normal content verification; mark the remaining reference news as the published reference news.

7. The news dissemination optimization system based on new media technology according to claim 1, characterized in that, Optimize and adjust the publishing information of the published reference news.

8. An optimized news dissemination system based on new media technology according to claim 7, characterized in that, Optimize and adjust the publishing information, including: Identify the publisher corresponding to the publishing information, and obtain the news optimization features of the publisher; establish a publishing optimization model, and the expression of the publishing optimization model is: ； Where: YT i represents the news optimization feature of the publisher corresponding to the published information, i = 1, 2,..., n, where n is the number of publishers corresponding to the published information; the output data is the publication optimization value PR(YT i ), and the publication optimization value is 1 or 0; Analyze the news optimization features according to the publishing optimization model to obtain the publishing optimization value of the publisher; optimize and adjust the publishing information according to the publishing optimization value.

9. An optimization method for news dissemination based on new media technology, characterized in that, Applied to a news dissemination optimization system based on new media technology as described in any one of claims 1 to 8, the method includes: Perform real-time analysis on the reference news method to obtain a news details map of the reference news method; Perform real-time analysis on the news details map to obtain the recommended optimization method of the corresponding news node in the news details map, obtain the recommended optimization information of the recommended optimization method, and display the recommended optimization information to the user; Perform calibration analysis on the news content to obtain the content calibration result of the corresponding news content, and perform corresponding processing on the corresponding news content according to the content calibration result; Mark the news content with normal content calibration results as target news; perform a release search based on the target news to obtain several reference news related to the target news, screen the reference news to obtain the released reference news, identify the release information of the released reference news; perform supplementary release processing on the target news according to the release information.

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