A Method and System for Evaluating the New Media Influence of Scientific and Technological Periodicals

Abstract

The present invention provides a method and system for evaluating the new media influence of scientific and technological periodicals, belonging to the technical field of data processing. The method includes: collecting the basic index data of the new media platforms of scientific and technological periodicals, and performing standardization processing on the basic index data to generate standardized index data; based on the exponential decay function, correcting the data weights of the standardized index data according to time nodes to generate final index data; constructing a hierarchical evaluation model for new media influence, determining the subjective weights of the new media platforms, and the combined weights of the indexes that integrate the subjective weights of the new media platforms and the objective weights of the indexes; based on the structure of the hierarchical evaluation model and the weight setting, calculating the single-platform influence and cross-platform influence of scientific and technological periodicals by using the final index data. The present invention solves the problems in the prior art that the correlation between the new media influence of scientific and technological periodicals and the academic influence of scientific and technological periodicals is weak, and the evaluation of the new media influence of scientific and technological periodicals is not comprehensive, etc.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular, to a method and system for evaluating the new media influence of scientific and technological periodicals. Background Art

[0002] As an innovative way of running periodicals that can effectively enhance the influence of scientific and technological periodicals, new media is increasingly widely applied to the practice of running periodicals and has become one of the important means for scientific and technological periodicals to achieve high-quality development in the new era. For academic periodicals, new media publishing means combining the content advantages of traditional academic publishing with the communication advantages of digital media, breaking the original single paper publishing mode, and spreading innovative scientific research results through various enhanced publishing methods in multiple channels and three-dimensionally, so as to achieve the purpose of expanding the influence of scientific research results, promoting the communication of scientific research personnel, and popularizing science for the public.

[0003] At present, there are many new media communication platforms adopted by scientific and technological periodicals, and there are large differences in the index data of each platform. Some domestic public opinion monitoring companies formulate corresponding new media communication indexes for different platforms. However, these indexes do not consider the influence of the academic nature of the periodicals in the design process, resulting in a weak correlation between the new media communication index and the academic influence of the periodicals. Moreover, there is currently no scientific and reasonable evaluation system to unify the data of each platform, and it is impossible to quantitatively judge the relationship between the new media influence and the academic influence of scientific and technological periodicals.

[0004] Therefore, there is an urgent need to design a technical solution for evaluating the new media influence of scientific and technological periodicals. Summary of the Invention

[0005] The present invention provides a method and system for evaluating the new media influence of scientific and technological periodicals to solve the defects in the prior art, such as the weak correlation between the new media influence and the academic influence of periodicals, and the incomplete evaluation of the new media influence.

[0006] In a first aspect, the present invention provides a method for evaluating the new media influence of scientific and technological periodicals, including: collecting the basic index data of the new media platforms of scientific and technological periodicals, and performing standardization processing on the basic index data to generate standardized index data; the new media platforms include multiple social media platforms opened by the periodical and the official website platform of the periodical;

[0007] Performing data weight correction on the standardized index data according to time nodes based on an exponential decay function to generate final index data;

[0008] Construct a hierarchical evaluation model for the influence of new media, determine the subjective weights of new media platforms, and the combined weights of indicators that integrate the subjective weights of new media platforms and the objective weights of indicators; the hierarchical evaluation model includes an objective layer and a platform layer, the objective layer is used to output the influence of new media of cross-platform scientific and technological periodicals, the platform layer includes multiple new media platforms, and each new media platform has multiple indicators;

[0009] Based on the structure and weight setting of the hierarchical evaluation model, calculate the influence of a single platform and the cross-platform influence of scientific and technological periodicals using the final indicator data.

[0010] According to a method for evaluating the influence of new media of scientific and technological periodicals provided by the present invention, after calculating the influence of a single platform of a scientific and technological periodical, it further includes: verifying the correlation of the cross-platform influence using a preset influence parameter, calculating the correlation coefficient; determining whether the calculation result of the cross-platform influence meets the requirements according to the range interval where the correlation coefficient is located; in the case of non-compliance, adjusting the indicators and / or the indicator weights.

[0011] According to a method for evaluating the influence of new media of scientific and technological periodicals provided by the present invention, collect the basic indicator data of the new media platforms of scientific and technological periodicals, and perform standardization processing on the basic indicator data to generate standardized indicator data, including: using a web crawler tool or a third-party data monitoring platform to obtain the basic indicator data of the new media platforms of scientific and technological periodicals; the basic indicator data of the social media platform includes: the number of fans, the number of reads, the number of likes, the number of forwards, and the number of comments, and the basic indicator data of the official website platform of the periodical includes the number of visits, the number of independent visitors, the number of pages per visit, and the number of page views; perform normalization processing on the basic indicator data to generate standardized indicator data.

[0012] According to a method for evaluating the influence of new media of scientific and technological periodicals provided by the present invention, perform data weight correction on the standardized indicator data according to time nodes based on the exponential decay function to generate the final indicator data, including: using the exponential decay function to assign time weights to the data at different time nodes; based on the time weights, adjust the standardized indicator data to generate the final indicator data.

[0013] According to a method for evaluating the influence of new media of scientific and technological periodicals provided by the present invention, determine the subjective weights of new media platforms, and the combined weights of indicators that integrate the subjective weights of new media platforms and the objective weights of indicators, including: using the analytic hierarchy process to determine the subjective weight of the M th new media platform in the platform layer ; and, according to the information entropy of the M th indicator of the i this scientific and technological periodical under the j th new media platform, calculate the objective weight ; calculate the subjective weight The difference degree from the objective weight is calculated, and the average value of the difference degrees of all indicators is determined to set the power function exponent according to the average value of the difference degrees; based on the power function exponent, a preset power function is set for the subjective weight and the objective weight are non-linearly fused to generate a fused weight .

[0014] According to a method for evaluating the new media influence of scientific and technological periodicals provided by the present invention, the preset power function is:

[0015] ;

[0016] wherein, is the power function exponent, n is i the maximum value of

[0017] According to a method for evaluating the new media influence of scientific and technological periodicals provided by the present invention, based on the structure and weight setting of the hierarchical evaluation model, the single-platform influence and cross-platform influence of the scientific and technological periodical are calculated by using the final index data, including: calculating the i single-platform influence of this scientific and technological periodical on the M th new media platform:

[0018]

[0019] wherein, is the normalized index data of the final index data, and L is the total number of indicators;

[0020] According to the single-platform influence of this scientific and technological periodical on each new media platform and the subjective weight of the new media platform, the cross-platform influence is calculated. i

[0021] In a second aspect, the present invention also provides a system for evaluating the new media influence of scientific and technological periodicals, including:

[0022] A data acquisition module, configured to collect the basic index data of the new media platform of the scientific and technological periodical and perform standardization processing on the basic index data to generate standardized index data; the new media platform includes multiple social media platforms opened by the periodical and the official website platform of the periodical;

[0023] A data correction module, configured to perform data weight correction on the standardized index data according to time nodes based on an exponential decay function to generate final index data;

[0024] ​The hierarchical evaluation model construction module is used to construct a hierarchical evaluation model for the influence of new media, determine the subjective weights of new media platforms, and the combined weights of indicators that combine the subjective weights of new media platforms and the objective weights of indicators; the hierarchical evaluation model includes a target layer and a platform layer. The target layer is used to output the influence of cross-platform scientific and technological periodical new media. The platform layer includes multiple new media platforms, and each new media platform has multiple indicators.

[0025] The influence evaluation module is used to calculate the single-platform influence and cross-platform influence of scientific and technological periodicals by using the final index data based on the structure and weight setting of the hierarchical evaluation model.

[0026] Thirdly, the present invention provides an electronic device, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the steps of the scientific and technological periodical new media influence evaluation method as described in any one of the above are implemented.

[0027] Fourthly, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the scientific and technological periodical new media influence evaluation method as described in any one of the above are implemented.

[0028] The scientific and technological periodical new media influence evaluation method and system provided by the present invention achieve the purpose of formulating different new media influence evaluation methods for different types of scientific and technological periodicals, solve the problems of uncorrelated data between cross-new media platforms and weak matching between new media influence and periodical influence. At the same time, by non-linearly integrating subjective and objective weights (two types of weights: the analytic hierarchy process and the entropy method), it not only retains the professionalism of subjective judgment but also incorporates the objectivity of data, balances subjective and objective information, improves the scientificity of weights, reduces the bias of the calculated scientific and technological periodical new media influence, realizes the evaluation requirements of dynamics, adaptability, and high precision, solves the problems of incomplete new media influence evaluation, etc., enables it to evaluate the size of the cross-platform scientific and technological periodical new media influence and the influence degree of each numerical index from the perspective of the CNKI influence that scientific and technological periodical workers are more concerned about, and is used to guide periodicals to improve academic quality, expand the influence of the publication platform and academic influence. Description of the Drawings

[0029] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0030] Figure 1It is a schematic flow chart of the method for evaluating the new media influence of scientific and technological periodicals provided by the present invention;

[0031] Figure 2 It is a schematic structural diagram of the system for evaluating the new media influence of scientific and technological periodicals provided by the present invention;

[0032] Figure 3 It is a schematic structural diagram of the electronic device provided by the present invention. Detailed implementation manners

[0033] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions in the present invention will be clearly and completely described below with reference to the accompanying drawings in the present invention. Apparently, the described embodiments are some but not all of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present invention without making creative efforts shall fall within the protection scope of the present invention.

[0034] It should be noted that in the description of the embodiments of the present invention, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitations, an element defined by the phrase "including one..." does not exclude the existence of additional identical elements in the process, method, article or device including the said element. For those of ordinary skill in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0035] In addition, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates an "or" relationship between the associated objects before and after.

[0036] The following combines Figures 1 - 3 Describe the method and system for evaluating the new media influence of scientific and technological periodicals provided by the embodiments of the present invention.

[0037] Figure 1 It is a schematic flow chart of the method for evaluating the new media influence of scientific and technological periodicals provided by the present invention. As Figure 1 shown, it includes but is not limited to the following steps:

[0038] Step 101: Collect the basic index data of the new media platforms of scientific and technological periodicals, and perform standardization processing on the basic index data to generate standardized index data; the new media platforms include multiple social media platforms opened by the periodical and the official website platform of the periodical.

[0039] (1) Data collection: Use web crawler tools or third-party data monitoring platforms to obtain the basic indicator data of the new media platforms of scientific and technological journals.

[0040] (2) Basic indicator data of new media platforms: The basic indicator data of new media platforms are divided into two categories. The first category is social media platforms such as WeChat official accounts, Douyin, and Xiaohongshu opened by the journal, and the corresponding basic indicator data are the number of fans, reading volume, like volume, repost volume, comment volume, etc. The second category is the official website platform of the journal, and the basic indicator data are the number of visits, number of independent visitors, number of pages per visit, page view volume, etc.

[0041] (3) Normalize the basic indicator data to generate standardized indicator data: Adopt the maximum-minimum normalization data preprocessing method to map the basic indicator data to the specified interval [0,1] to eliminate the dimensional differences between different features. The formula is:

[0042]

[0043] Where: x is the original data ，x min and x max are the minimum and maximum values of this indicator respectively, is the standardized indicator data.

[0044] Step 102: Based on the exponential decay function, correct the data weights of the standardized indicator data according to time nodes to generate the final indicator data.

[0045] Specifically, according to the characteristics of information timeliness in communication, use the exponential decay function to assign time weights to data at different time nodes. The formula is:

[0046] ;

[0047] Where, is the time weight, The larger it is, the smaller the weight; is the time interval between the data generation time and the current time, and the unit can be month; is the decay coefficient, which can be adjusted according to data characteristics to determine the optimal value.

[0048] Based on the time weight, adjust the standardized indicator data to generate the final indicator data. The formula is:

[0049] =

[0050] Where, is the final indicator data.

[0051] Step 103: Construct a hierarchical evaluation model for new media influence, determine the subjective weights of new media platforms, and the combined weights of indicators that integrate the subjective weights of new media platforms and the objective weights of indicators.

[0052] (1) Structure of the hierarchical evaluation model for new media influence:

[0053] The hierarchical evaluation model includes a target layer and a platform layer. The target layer is used to output the new media influence of cross-platform scientific and technological periodicals. The platform layer includes multiple new media platforms, and each new media platform has multiple indicators.

[0054] (2) Use the analytic hierarchy process to determine the subjective weight of the M th new media platform in the platform layer :

[0055] Considering the importance of different new media platforms, the analytic hierarchy process (AHP) is adopted. Since the communication values of different platforms (such as the official website of the periodical, WeChat official account, Douyin, Xiaohongshu) vary greatly, it is necessary to rely on experts to make subjective judgments on the platform weights. For example, the official website focuses more on the dissemination of academic papers (depth), and the WeChat official account focuses on the dissemination of popular science knowledge (breadth).

[0056] Construct a judgment matrix. Through pairwise comparison of the platform importance by experts (1-9 scale method), construct the judgment matrix .

[0057]

[0058] Among them: is the influence of the M th new media platform and the N th new media platform judged by experts on the target (new media influence). M The value range of N; is 1 to M = N When ， it means the two platforms are equally important; , it means M is slightly more important than N ; , it means M is extremely more important than N ; The value of

[0059] is 1 to 9.

[0060]

[0061] Calculate the average by row, and calculate the mean value of each row to obtain the initial weight vector:

[0062]

[0063] Calculate the weight vector and calculate the maximum eigenvalue of the matrix λ max , and calculate the subjective weight of the normalized eigenvector:

[0064] ;

[0065] Among them, is the subjective weight of the M th new media platform.

[0066] Consistency test, calculate the consistency index CI * =(λ max - n ) / ( n -1), calculate CR = CI * / RI , where RI is the random consistency index, which can be obtained by looking up the table. If CR < 0.1 , then the test is passed, otherwise the judgment matrix needs to be adjusted.

[0067] (3)According to the information entropy of the M th new media platform for the i th index of the j th science and technology journal, calculate the objective weight : Use the entropy method for objective weighting. According to the data dispersion degree of the indicators within the platform (such as the number of fans and the number of forwards), the value of the indicators can be objectively reflected. For example, if the data of the "number of likes" of a WeChat public account varies greatly, a higher weight is assigned.

[0068] Standardize each indicator within the platform and convert it into a probability distribution to obtain , where i represents the i rd journal, i ranges from 1 to n; j represents the i th index of the j rd journal, j ranges from 1 to m , represents the value of the i th journal's j th index after time weight correction.

[0069] Calculate the information entropy, , if = 0, define = 0.

[0070] Calculate the objective weight, .

[0071] (4)Calculate the subjective weight and the objective weight of the difference, and determine the average value of the difference of all indicators, in order to set the power function exponent according to the average value of the difference.

[0072] In the process of assigning weights to the new media communication influence indicators of scientific and technological journals, introducing a non-linear combination strategy to fuse subjective and objective weights can solve the problem that the linear superposition of subjective and objective weights cannot reflect the complex relationships between indicators (such as threshold effect, synergy effect). Using a dynamic adaptive power function to amplify the influence of key indicators (such as high entropy value forwarding volume) and suppress low-value indicators (such as the number of fans) to fuse subjective and objective weights.

[0073] For each indicator, calculate the difference between the subjective and objective weights , take the average value of the differences of all indicators , introduce the power function exponent , and dynamically adjust the fusion ratio of subjective and objective weights according to the difference:[[]]

[0074]

[0075] Among them: is the sensitivity coefficient (usually taken as 10), is the reference difference (can be set to 0.1), when >[[]] when, tends to 0, more dependent on the objective weight; when <[[]] when, tends to 1, more dependent on the subjective weight.

[0076] (5)Based on the power function exponent, set the preset power function for the subjective weight and the objective weight to perform non-linear fusion to generate the fusion weight .

[0077] The specific preset power function is:[[]]

[0078]

[0079] Among them, is the power function exponent, n is i the maximum value of.

[0080] Step 104: Calculate the single-platform influence and cross-platform influence of the scientific and technological periodical by using the final index data based on the structure and weight setting of the hierarchical evaluation model.

[0081] Calculate the i single-platform influence of this scientific and technological periodical on the M th new media platform :

[0082]

[0083] where is the normalized index data of the final index data, and L is the total number of indexes;

[0084] According to the single-platform influence of this scientific and technological periodical on each new media platform and the subjective weight of the new media platform, calculate the cross-platform influence i : :

[0085] ;

[0086] where K represents the number of platform layers.

[0087] Based on the content of the above embodiments, after calculating the single-platform influence of the scientific and technological periodical, it further includes: verifying the correlation of the cross-platform influence by using a preset influence parameter (CNKI CI, i.e., the CNKI influence index), calculating the correlation coefficient; determining whether the calculation result of the cross-platform influence meets the requirements according to the range interval where the correlation coefficient is located; and adjusting the indexes and / or index weights in case of non-compliance.

[0088] Specifically, calculate the Pearson correlation coefficient between the cross-platform influence of the scientific and technological periodical and CNKI CI r :

[0089]

[0090] where: is the cross-platform influence of the i th scientific and technological periodical; is the CNKI influence index of the i th scientific and technological periodical; is the average value of the cross-platform influences of all scientific and technological periodicals, ; is the average value of the CNKI influence indexes of all periodicals, .

[0091] When the Pearson correlation coefficient r > 0.7, it indicates a strong correlation, indicating that the selected indicators are relatively reasonable, and the new media influence of the scientific and technological periodicalS is highly correlated with academic influence; when 0.3 ≤ r ≤ 0.7, it indicates a moderate correlation; when r <0.3, it indicates a weak correlation, which may be due to unreasonable selection of index parameters. At this time, the parameters need to be adjusted again and then recalculated. Significance P ≤ 0.05 , indicates that the correlation has statistical significance, P> > 0.05 indicates that the correlation is not significant and cannot prove the existence of a linear correlation between variables.

[0092] To more clearly illustrate the above technical solution, taking the scientific and technological periodicals in the water conservancy industry as an example, according to the ranking of the influence index (CloutIndex, CI) announced in the Annual Report of the Impact Factor of Chinese Academic Journals (Natural Science and Engineering Technology · 2024 Edition), after screening and removing some periodicals that do not use new media platforms or have not been updated for a long time, 18 representative water conservancy science and technology periodicals in Q1 and Q2 areas are selected for analysis, and the relationship between their new media influence and the influence index CI is calculated.

[0093] WeChat official accounts, Douyin, Xiaohongshu, and Weibo are classified as WeChat-based platforms, and official websites are classified as website platforms.

[0094] Taking the period from May to October 2024 as the research period, through means such as web crawlers and Qingbo platforms, the index data of WeChat-based platforms (belonging to basic index data) are searched to obtain the total number of tweets, total reading volume, total number of likes, total number of reposts, and estimated number of fans of WeChat-based platforms. Since most of the 18 selected water conservancy science and technology periodicals do not have Douyin, Xiaohongshu, and Weibo accounts, the calculation process is simplified, and only the index data of their WeChat official accounts are statistically analyzed, as shown in Table 1.

[0095] Taking the period from May to October 2024 as the research period, through means such as web crawlers and similarweb, the index data of website platforms are searched to obtain index data such as monthly website traffic, monthly unique visitors, number of pages per visit, and page view times.

[0096] The Min-Max normalization data preprocessing method is adopted to linearly map the new media index data to the specified interval [0,1] to eliminate the dimensional differences between different features and form standardized index data.

[0097] Due to space limitations, the following calculations take "Yangtze River" and "Express Water Resources & Hydropower" as examples to calculate the total reading volume and monthly website traffic indicators respectively.

[0098] Normalize the "total reading volume" index in the WeChat official account:

[0099] Maximum value = 46888, minimum value = 725, normalized value of the total reading volume of "Yangtze River" = (19985 - 725) / (46888 - 725) = 0.4172, normalized value of the total reading volume of "Express Water Resources & Hydropower" = (26969 - 725) / (46888 - 725) = 0.5685.

[0100] Normalize the "monthly visit volume" indicator in the website metrics:

[0101] Maximum value = 33714, minimum value = 224, normalized value of the monthly visit volume of "Yangtze River" = (1723 - 224) / (33714 - 224) = 0.0448, normalized value of the monthly visit volume of "Express Water Resources & Hydropower" = (511 - 224) / (33714 - 224) = 0.0086.

[0102] Table 1 Evaluation index table of the academic influence of water resources science and technology journals

[0103]

[0104] According to the characteristics of information timeliness in communication, an exponential decay function is used to assign weights to the data at different time nodes. , because the time dimension selected for each index in this example is the same, so in this calculation = 0, = 1.

[0105] For the normalized data Multiply by the time weight , to obtain the adjusted index value, that is, the final index data = , the total reading volume of "Yangtze River" = 0.417, the total reading volume of "Express Water Resources & Hydropower" = 0.569; the monthly visit volume of "Yangtze River" = 0.0448, the monthly visit volume of "Express Water Resources & Hydropower" .

[0106] Considering the importance of different new media platforms, the analytic hierarchy process (AHP) is used for weight assignment. Five industry experts are invited to judge the platform strategic positioning, and the content of the expert judgment matrix (1 - 9 degrees) is shown in Table 2:

[0107] Table 2 Content table of the expert judgment matrix

[0108]

[0109] Among them: "1" indicates that the impacts of two elements on the target are completely the same; "3" indicates that the former is slightly more important than the latter, but the difference is small. The result shows that experts believe that the data of the official website is slightly more important than the data of the WeChat official account.

[0110] The weight of the eigenvector is calculated as T (Passing the consistency test, CR = 0 < 0.1), where the characteristic weight of the official website , and the characteristic weight of the WeChat official account .

[0111] The entropy method is used for objective weight assignment because the data dispersion degree of the indicators within the platform (such as the number of fans and the number of forwards) can objectively reflect the value of the indicators. According to the data dispersion degree for weight assignment, the greater the degree of variation (the smaller the entropy value), the higher the discrimination degree of the indicator and the greater the weight.

[0112] Each indicator within the platform is standardized and transformed into a probability distribution to obtain :

[0113] Total reading volume of "Yangtze River" = 0.0982, total reading volume of "Express Water Resources & Hydropower" = 0.1338; monthly visit volume of "Yangtze River" = 0.0126, monthly visit volume of "Express Water Resources & Hydropower" 0.0024.

[0114] Calculate the information entropy of each internal indicator of the journals, , as calculated, the information entropy of the access volume, number of independent visitors, number of pages per visit, and page view volume of the official website indicators of the science and technology journals are 0.7822, 0.7669, 0.9096, and 0.8498 respectively; the information entropy of the total number of tweets, total reading volume, total number of likes, total forwards, and estimated number of fans in the WeChat official account are 0.8362, 0.7865, 0.7666, 0.7535, and 0.7459 respectively. Among them, the entropy value of the estimated number of fans is the smallest, and its degree of variation is the greatest.

[0115] Calculate the objective weights of each indicator, , the objective weights of the total number of tweets, total reading volume, total number of likes, total forwards, and estimated number of fans in the WeChat official account are 0.4339, 0.4254, 0.5046, 0.4714 respectively; the objective weights of the total number of tweets, total reading volume, total number of likes, total forwards, and estimated number of fans in the WeChat official account are 0.4638, 0.4363, 0.4253, 0.4180, and 0.4138 respectively.

[0116] In the process of assigning weights to the new media communication influence indicators of scientific and technological periodicals, a non-linear combination strategy is introduced to fuse subjective and objective weights. For each indicator, calculate the difference degree between subjective and objective weights:

[0117]

[0118] Take the average value of the difference degrees of all indicators = 0.1753.

[0119] Introduce the power function exponent , and dynamically adjust the fusion ratio of subjective and objective weights according to the difference degree

[0120] = 0.6798

[0121] Among them: is the sensitivity coefficient (usually taken as 10), is the reference difference degree (which can be set to 0.1). When > , tends to 0, and it depends more on the objective weight; when < , tends to 1, and it depends more on the subjective weight.

[0122] Adopt a preset power function to perform non-linear fusion on subjective and objective weights:

[0123]

[0124] It is calculated that:

[0125]

[0126] Integrate the platform weight, indicator weight, and time decay factor to generate a dynamic evaluation result, and calculate the single-platform influence and cross-platform influence (i.e., new media influence) of the i-th scientific and technological periodical.

[0127] The calculation results in this embodiment are shown in Table 3:

[0128] Table 3 is a display table of the calculation results of new media influence

[0129]

[0130] Use SPSS 27 software to conduct a correlation analysis on the new media influence and academic influence CI values of scientific and technological periodicals, and obtain a Pearson correlation of 0.540 * ("*" indicates that the correlation is significant at the 0.05 level), and the significance P = 0.021 < 0.05, indicating that the correlation has statistical significance.

[0131] Table 4 is the table of the linear regression analysis results of the new media influence and the CI value

[0132]

[0133] Taking the new media influence of cross-platform scientific and technological periodicals as the independent variable and the CI value as the dependent variable, observe their correlation, as shown in Table 4. The results of the linear regression analysis show that there is a strong positive correlation between the new media influence and the CI value (p<0.05).

[0134] The new media influence of the water conservancy scientific and technological periodicals calculated according to the above process meets the design requirements, is significantly correlated with the academic influence and passes the linear regression test, indicating that it can be used for the evaluation of the new media influence of scientific and technological periodicals, as well as the quantitative analysis of the relationship between various new media indicators and the academic influence, and can be used to guide the running of periodicals and adjust the periodical operation strategy, ultimately enhancing the academic influence of the periodicals.

[0135] Compared with the prior art, the present invention provides a method for calculating the new media influence of scientific and technological periodicals. Using web crawlers or third-party data monitoring platforms to obtain the basic index data of specific new media, adopting the Min-Max normalization data preprocessing method to normalize the derived basic index data, and at the same time considering the information timeliness characteristics in communication studies, introducing an exponential decay function to weight the data at different time nodes, and performing time decay correction on the normalized data. Using the non-linear combined weighting method (subjective weighting + objective weighting) to determine the weights of each media platform and index data, and finally obtaining the cross-platform scientific and technological periodical new media influence calculation formula. Through the correlation analysis of the new media influence calculation formula and the journal's CNKI composite influence CI, verify the rationality of the calculation formula. At the same time, according to the different types of scientific and technological periodicals (such as popular science periodicals, academic periodicals), adjust the corresponding index weights, so that the cross-platform scientific and technological periodical new media influence calculation formula is always correlated with the CNKI composite influence CI to achieve the purpose of evaluation. This patent realizes the purpose of formulating different new media influence evaluation methods for different types of scientific and technological periodicals, solves the problems of uncorrelated data between cross-new media platforms and weak matching between new media influence and periodical influence. At the same time, through the non-linear fusion of the analytic hierarchy process (AHP) and the entropy method for two types of weights, it not only retains the professionalism of subjective judgment but also incorporates the objectivity of data, balances subjective and objective information, improves the scientificity of weights, reduces the bias of the calculated new media influence of scientific and technological periodicals, realizes the evaluation requirements of dynamics, adaptability and high precision, solves the problems of incomplete evaluation of new media influence, etc., enabling it to evaluate the size of the cross-platform scientific and technological periodical new media influence and the influence degree of each numerical index from the perspective of the CNKI influence that scientific and technological periodical workers are more concerned about, and is used to guide periodicals to improve academic quality, expand the influence of the publication platform and academic influence.

[0136] In summary, the present invention has the advantages of comprehensive information collection, balanced subjective and objective information, good adaptability, high accuracy, strong relevance, etc. It can comprehensively and truly reflect the size of the new media influence of scientific and technological periodicals, and can be used as a reference to guide the operation of periodicals and improve the academic influence of periodicals.

[0137] On the other hand, the present invention also provides a new media influence evaluation system for scientific and technological periodicals, Figure 2 which is a schematic structural diagram of the new media influence evaluation system provided by the present invention, as Figure 2 shown. The system includes: a data acquisition module 210, a data correction module 220, a hierarchical evaluation model construction module 230, and an influence evaluation module 240.

[0138] Among them, the data acquisition module 210 is used to collect the basic index data of the new media platforms of scientific and technological periodicals, and perform standardization processing on the basic index data to generate standardized index data; the new media platforms include multiple social media platforms opened by the periodical, as well as the official website platform of the periodical;

[0139] The data correction module 220 is used to perform data weight correction on the standardized index data according to time nodes based on the exponential decay function to generate final index data;

[0140] The hierarchical evaluation model construction module 230 is used to construct a hierarchical evaluation model of new media influence, determine the subjective weights of new media platforms, and the fusion weights of indicators that integrate the subjective weights of new media platforms and the objective weights of indicators; the hierarchical evaluation model includes a target layer and a platform layer. The target layer is used to output the new media influence of scientific and technological periodicals across platforms, and the platform layer includes multiple new media platforms, and each new media platform has multiple indicators;

[0141] The influence evaluation module 240 is used to calculate the single-platform influence and cross-platform influence of scientific and technological periodicals by using the final index data based on the structure and weight settings of the hierarchical evaluation model.

[0142] It should be noted that the new media influence evaluation system for scientific and technological periodicals provided by the embodiments of the present invention, when specifically operating, can execute the new media influence evaluation method described in any of the above embodiments, and this embodiment will not be elaborated here.

[0143] Figure 3 which is a schematic structural diagram of the electronic device provided by the present invention, as Figure 3As shown, the electronic device may include: a processor 310, a communications interface 320, a memory 330, and a communication bus 340. Among them, the processor 310, the communications interface 320, and the memory 330 complete communication with each other through the communication bus 340. The processor 310 may call the logical instructions in the memory 330 to execute the method for evaluating the influence of new media of scientific and technological periodicals.

[0144] On the other hand, the present invention also provides a computer program product. The computer program product includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions. When the program instructions are executed by a computer, the computer can execute the method for evaluating the influence of new media of scientific and technological periodicals provided in the above-mentioned embodiments.

[0145] On another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it is implemented to execute the method for evaluating the influence of new media of scientific and technological periodicals provided in the above-mentioned embodiments.

[0146] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for evaluating the new media influence of a scientific and technological journal, characterized in that Including: Collecting the basic index data of the new media platforms of scientific and technological periodicals, and performing standardized processing on the basic index data to generate standardized index data; the new media platforms include multiple social media platforms opened by the periodical and the official website platform of the periodical; Based on the exponential decay function, correcting the data weights of the standardized index data according to time nodes to generate final index data; Constructing a hierarchical evaluation model for new media influence, and determining the subjective weights of the new media platforms, as well as the combined weights of the indexes that integrate the subjective weights of the new media platforms and the objective weights of the indexes; the hierarchical evaluation model includes a target layer and a platform layer, the target layer is used to output the new media influence of cross-platform scientific and technological periodicals, the platform layer includes multiple new media platforms, and each new media platform has multiple indexes; Based on the structure and weight settings of the hierarchical evaluation model, calculating the single-platform influence and cross-platform influence of scientific and technological periodicals using the final index data; Among them, based on the exponential decay function, correcting the data weights of the standardized index data according to time nodes to generate final index data, including: Using the exponential decay function to assign time weights to the data at different time nodes, specifically: ； Among them, is the time weight, is the time interval between the data generation time and the current time, is the attenuation coefficient; Based on the time weights, adjusting the standardized index data to generate final index data; Among them, determining the subjective weights of the new media platforms, as well as the combined weights of the indexes that integrate the subjective weights of the new media platforms and the objective weights of the indexes, including: Use the analytic hierarchy process to determine the subjective weight of the M th new media platform in the platform layer ; and, according to the M th new media platform, calculate the information entropy of the i th index of this scientific and technological journal to calculate the objective weight j ; ; Calculate the subjective weight and the objective weight to determine the difference degree, and determine the average value of the difference degrees of all indicators, so as to set the power function exponent according to the average value of the difference degrees; Set a preset power function for the subjective weight based on the power function exponent and the objective weight to perform non-linear fusion to generate a fusion weight ; Among them, the preset power function is: ； is the exponent of the power function, n is i the maximum value of; Among them, setting the power function exponent according to the average value of the difference degree, specifically: is the sensitivity coefficient, is the reference difference degree, is the average value of the difference degrees of all indicators; Based on the structure and weight settings of the hierarchical evaluation model, calculating the single-platform influence and cross-platform influence of scientific and technological periodicals using the final index data, including: Calculate the i The single-platform influence of this scientific and technological periodical under the M th new media platform: Among them, is the normalized index data of the final index data, and L is the total number of indexes; According to Article i Calculate the cross-platform influence based on the single-platform influence of this scientific and technological periodical under each new media platform and the subjective weight of the new media platform; specifically: ; Among them, K represents the number of platform layers.

2. The evaluation method for the new media influence of scientific and technological periodicals according to claim 1, characterized in that After calculating the single-platform influence and cross-platform influence of scientific and technological periodicals using the final index data, it also includes: Using the preset influence parameter to verify the correlation of the cross-platform influence and calculating the correlation coefficient; According to the range interval where the correlation coefficient is located, determining whether the calculation result of the cross-platform influence meets the requirements; In the case of not meeting the requirements, adjusting the indexes and / or index weights.

3. The method for evaluating the new media influence of a scientific and technological periodical according to claim 1, wherein Collecting the basic index data of the new media platforms of scientific and technological periodicals, and performing standardized processing on the basic index data to generate standardized index data, including: Using a web crawler tool or a third-party data monitoring platform to obtain the basic index data of the new media platforms of scientific and technological periodicals; the basic index data of the social media platforms include: the number of fans, the number of reads, the number of likes, the number of forwards, and the number of comments, and the basic index data of the official website platform of the periodical include the number of visits, the number of independent visitors, the number of pages per visit, and the number of page views; Performing normalization processing on the basic index data to generate standardized index data.

4. A new media influence evaluation system for scientific and technological periodicals, characterized in that, Including: A data collection module, used to collect the basic index data of the new media platforms of scientific and technological periodicals, and perform standardized processing on the basic index data to generate standardized index data; the new media platforms include multiple social media platforms opened by the periodical and the official website platform of the periodical; A data correction module, used to correct the data weights of the standardized index data according to time nodes based on the exponential decay function to generate final index data; Among them, the data weight of the standardized index data is corrected according to time nodes based on the exponential decay function to generate the final index data, including: The time weights of data at different time nodes are given by using the exponential decay function, specifically: ； Among them, is the time weight, is the time interval between the data generation time and the current time, is the attenuation coefficient; Based on the time weights, the standardized index data is adjusted to generate the final index data; The hierarchical evaluation model construction module is used to construct a hierarchical evaluation model of new media influence, determine the subjective weights of new media platforms, and the combined weights of indicators that integrate the subjective weights of new media platforms and the objective weights of indicators; the hierarchical evaluation model includes a target layer and a platform layer, the target layer is used to output the cross-platform scientific journal new media influence, the platform layer includes multiple new media platforms, and each new media platform has multiple indicators; Among them, determining the subjective weights of new media platforms and the combined weights of indicators that integrate the subjective weights of new media platforms and the objective weights of indicators includes: Determine the subjective weight of the M th new media platform in the platform layer ; and, according to the M th new media platform, calculate the information entropy of the i th index of this scientific and technological journal to calculate the objective weight j ; ; Calculate the subjective weight and the objective weight to determine the difference degree, and calculate the average difference degree of all indicators, so as to set the power function exponent according to the average difference degree; Based on the power function exponent, a preset power function is set for the subjective weight and the objective weight are non-linearly fused to generate a fused weight ; Among them, the preset power function is: ； is the exponent of the power function, n is i the maximum value of; Among them, the power function exponent is set according to the average difference degree, specifically: is the sensitivity coefficient, is the reference difference degree, is the average value of the difference degrees of all indicators; The influence evaluation module is used to calculate the single-platform influence and cross-platform influence of scientific journals by using the final index data based on the structure of the hierarchical evaluation model and the weight setting; Calculating the single-platform influence and cross-platform influence of scientific journals by using the final index data based on the structure of the hierarchical evaluation model and the weight setting includes: Calculate the i The single-platform influence of this scientific and technological periodical under the M new media platform: Among them, is the normalized index data of the final index data, and L is the total number of indexes; According to Article i Calculate the cross-platform influence based on the single-platform influence of this scientific and technological periodical under each new media platform and the subjective weight of the new media platform; specifically: ; Among them, K represents the number of platform layers.

5. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the computer program, the steps of the scientific journal new media influence evaluation method according to any one of claims 1 to 3 are implemented.

6. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, the steps of the scientific journal new media influence evaluation method according to any one of claims 1 to 3 are implemented.

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