A Method for Analyzing the Effects of Internet Information Dissemination Based on Multi-Dimensional Data

Abstract

This invention provides a method for analyzing the effects of internet information dissemination based on multi-dimensional data, belonging to the field of information technology. It solves the problem of the inability to comprehensively assess the effects and impacts of information dissemination. Specifically, the method includes the following steps: S1: Collect internet dissemination information; S2: Classify disseminated documents according to the dissemination region and time, calculate the regional dissemination power, and construct a regional dissemination power map; analyze the types of dissemination nodes; and construct a dissemination node type map; S3: Analyze the content of disseminated documents, determine their expressive tendencies, and obtain an expressive tendency map; S4: Calculate the impact value of disseminated documents and construct an influential figures map; S5: Based on the regional dissemination power map, dissemination node type map, expressive tendency map, and influential figures map, construct a multi-dimensional analysis map. This invention, by displaying multiple dissemination dimensions, allows users to intuitively understand and analyze the effects of information dissemination.

Description

Technical Field

[0001] This invention relates to a method for analyzing the effects of internet information dissemination based on multi-dimensional data, and pertains to the field of information technology. Background Technology

[0002] Existing methods for analyzing the effects of internet information dissemination have the following shortcomings:

[0003] Limited analytical dimensions: Existing information dissemination analysis tools cannot comprehensively assess the effectiveness and impact of information dissemination.

[0004] Poor readability: Traditional information dissemination analysis tools usually only display one or a few information dimensions through charts, resulting in an incomplete information presentation. When attempting to display multiple information dimensions simultaneously, although the aim is to provide a more comprehensive analytical perspective, the actual display effect is poor due to complex chart layouts, crowded visual elements, or lack of effective integration, making it difficult to quickly understand the information and seriously affecting usability and user interpretation efficiency.

[0005] User limitations: Traditional information dissemination analysis tools (such as Gephi, UCINET, and Python-NLP libraries) have complex user interfaces, cumbersome parameter configurations, require programming basics (such as Python scripting) and have strong code dependencies, and have steep learning curves that require long-term training to master. As a result, non-professional users often hesitate to use these tools due to the large time investment and high technical threshold, which affects their widespread application in daily analysis tasks. Summary of the Invention

[0006] In view of the shortcomings of existing technologies, the purpose of this invention is to provide a method for analyzing the effects of internet information dissemination based on multi-dimensional data, aiming to solve the problem of difficulty in finding talent information.

[0007] To achieve the above objectives, the present invention employs the following technical solution: a method for analyzing the internet information dissemination effect based on multi-dimensional data, comprising:

[0008] Step S1: Collect information on internet dissemination, including the disseminated file, dissemination time, and dissemination location. The disseminated file includes information about the disseminator, the content of the disseminated file, the number of times the disseminated file has been forwarded, and the number of times the disseminated file has been commented on.

[0009] Step S2: Classify the disseminated documents according to the dissemination region to obtain regional dissemination documents, calculate the regional dissemination power based on the regional dissemination documents, and construct a regional dissemination power map based on the regional dissemination power;

[0010] The files are categorized according to their propagation time to obtain files propagated by time period. The number of files propagated by time period is counted, and the types of propagation nodes are analyzed. A propagation node type graph is then constructed.

[0011] Step S3: Analyze the content of the disseminated files, determine the expression tendency of the disseminated files, count the number of files with different expression tendencies, and visualize the statistical results; thus obtaining an expression tendency graph.

[0012] Step S4: Calculate the impact value of the disseminated file based on the number of forwards and comments on the disseminated file, and display the corresponding disseminator information based on the impact value to construct an influencer map;

[0013] Step S5: Integrate the regional dissemination force map, dissemination node type map, expression tendency map, and influential figure map to construct a multi-dimensional analysis map.

[0014] Furthermore, the specific steps of step S2 are as follows:

[0015] Step S21: Count the number of disseminated files (a) and the types of disseminated regions (b); classify the disseminated files according to the types of disseminated regions to obtain regional disseminated files; record the number of regional disseminated files; normalize the number of regional disseminated files to obtain the regional dissemination power of each type of disseminated region; and construct a regional dissemination power map based on the regional dissemination power.

[0016] Step S22: Obtain the propagation time of information spread on the Internet, divide the propagation time into stages to obtain the time periods, analyze the time periods to obtain the changes in the number of propagated files in the time periods, obtain the propagation node types based on the changes in the number of propagated files, perform statistics on the propagation node types, and construct a propagation node graph.

[0017] Furthermore, the specific steps of step S21 are as follows:

[0018] Step S211: Based on the number of files a and the types of regions to be spread, classify the files to be spread, loop through the files to obtain the regions to be spread for each file, count the number of files in the same region to obtain the number of files to be spread in each region, denoted as cb(1), cb(2), ..., cb(b);

[0019] Step S212: Obtain the maximum value among the number of files propagated in a region, denoted as zdc; obtain the minimum value among the number of files propagated in a region, denoted as zxc; normalize the number of files propagated in a region based on the maximum number of files propagated in a region zbc and the minimum number of files propagated in a region zxc to obtain the regional propagation force cbl(i).

[0020] ;

[0021] Where: cb(i) refers to the number of files distributed in the region, 1≤i≤b;

[0022] Step S213: Construct a semi-circular fan-shaped region, obtain the maximum radian Π of the semi-circular fan-shaped region, set the radian hd of the region based on the maximum radian of the semi-circular fan-shaped region and the regional propagation force, sort the radians in descending order to obtain a descending radian list, fill the semi-circular fan-shaped region according to the descending radian list, and mark the propagation region type at the corresponding radian position; obtain the image of each propagation region, and display the images of each propagation region around the outside of the semi-circular fan-shaped region in the order of the descending radian list; obtain the regional propagation force map.

[0023] Furthermore, the specific steps of step S22 are as follows:

[0024] Step S221: Obtain the start propagation time, denoted as qsj; obtain the end propagation time, denoted as zsj; calculate the duration interval qj based on the start and end propagation times, qj = zsj - qsj; divide the duration interval into three equal parts to divide the propagation time into the early stage, middle stage, and late stage of information propagation.

[0025] Let the early stage of information propagation be denoted as xcq, and the value range of xcq is [qsj, qsj + qj / 3];

[0026] The intermediate stage of information dissemination is denoted as xcz, and the value range of xcz is [qsj + qj / 3, zsj - qj / 3].

[0027] The later stage of information propagation is denoted as xch, and the value range of xch is [zsj-qj / 3,zsj];

[0028] The information dissemination process was analyzed in three stages: the early stage, the middle stage, and the late stage, to identify information nodes at different stages of the dissemination.

[0029] Step S222: Obtain node types, highlight different node types with different colors, sort the nodes in the order of early information propagation, middle information propagation, and late information propagation, and construct a propagation node graph.

[0030] Furthermore, the specific steps of step S221 are as follows:

[0031] Analyze the early stage of information dissemination, extract the dissemination files at time xcq to obtain the dissemination files in the early stage of information dissemination, and obtain the number of files at different times based on the dissemination files in the early stage of information dissemination, denoted as qws(qt), where: qws(qt) represents the number of files at time qt is qws; qt∈[qsj, qsj+qj / 3];

[0032] The image is plotted using qws(qt), resulting in a curve showing the change in the number of files propagated over time. qs nodes are then selected and evenly distributed across the curve.

[0033] Based on the change curve, the rate of change at the node's front end is calculated to obtain the first judgment value ypd(j) for the previous node:

[0034] ;

[0035] Where: Δqt represents the small change in qt; qsj and qj are the parameters for the value of qt, qt∈[qsj, qsj+qj / 3];

[0036] Based on the change curve, the rate of change at the back end of the node is calculated to obtain the second judgment value epd(j) of the previous node:

[0037] ;

[0038] The node type is determined based on the first judgment value ypd(j) and the second judgment value epd(j) of the previous node:

[0039] If ypd(j) = 0, then the node is the starting point;

[0040] If epd(j) = 0, then the node is the endpoint;

[0041] If epd(j) > ypd(j) > 0, then the node is an ascending node;

[0042] If ypd(j) ≥ epd(j) > 0, then the node is a blockage point;

[0043] If ypd(j) > 0 > epd(j), then the node is a burst point;

[0044] If ypd(j) < 0 and epd(j) < 0, then the node is a descending node.

[0045] Furthermore, the specific steps of step S221 also include:

[0046] The mid-term of information dissemination is analyzed, and the dissemination files at time xcz are extracted to obtain the dissemination files in the mid-term of information dissemination. Based on the dissemination files in the mid-term of information dissemination, the number of files at different times is obtained, denoted as zws(zt), where: zws(zt) represents the number of files at time zt as zws; zt∈[qsj+qj / 3,zsj-qj / 3];

[0047] The image is plotted using zws(zt), resulting in a curve showing the change in the number of files propagated over time. zs nodes are then selected and evenly distributed across the curve.

[0048] Based on the change curve, the rate of change at the node front end is calculated to obtain the first judgment value yzd(k) for the mid-term node:

[0049] ;

[0050] Where: Δzt represents the small change in zt; qsj and qj are the parameters for the value of zt, zt∈[qsj+qj / 3, zsj-qj / 3];

[0051] Based on the change curve, the rate of change at the back end of the node is calculated to obtain the second judgment value ezd(k) for the intermediate node:

[0052] ;

[0053] The intermediate node type is determined by judging the first judgment value yzd(k) and the second judgment value ezd(k) of the intermediate node.

[0054] Furthermore, the specific steps of step S221 also include:

[0055] The later stage of information propagation is analyzed, and the propagation files at the xch time point are extracted to obtain the propagation files in the later stage of information propagation. Based on the propagation files in the later stage of information propagation, the number of files at different times is obtained, denoted as hws(ht), where: hws(ht) represents the number of files at time ht as hws; ht∈[zsj-qj / 3,zsj];

[0056] The image is plotted using hws(ht), resulting in a curve showing the change in the number of files transmitted over time. hs nodes are then selected and evenly distributed across the curve.

[0057] Based on the change curve, the rate of change at the node's leading edge is calculated to obtain the first judgment value yhd(h) for the later node:

[0058] ;

[0059] Where: Δht represents the small change in ht; zsj and qj are the parameters for the value of ht, ht∈[zsj-qj / 3, zsj];

[0060] Based on the change curve, the rate of change at the rear end of the node is calculated to obtain the second judgment value ehd(h) for the later node:

[0061] ;

[0062] The type of the later node is determined by judging the first judgment value yhd(h) and the second judgment value ehd(h) of the later node.

[0063] Furthermore, the specific steps of step S3 are as follows:

[0064] Step S31: Obtain the content of the dissemination document, analyze the expressive tendency of the dissemination document content, and obtain the emotional polarity of the dissemination document content; based on the emotional polarity of the dissemination document content, define the dissemination document as a positive dissemination document, a negative dissemination document, or a neutral dissemination document;

[0065] Step S32: Extract the propagation files with the same propagation time and propagation area, and denote them as local propagation files. Obtain the number of local propagation files jsl; obtain the number of positive propagation files in the local propagation files, and denote it as zsl; obtain the number of negative propagation files in the local propagation files, and denote it as fsl; obtain the number of neutral propagation files in the local propagation files, and denote it as xsl.

[0066] zsl + fsl + xsl = jsl;

[0067] Define three nodes to store propagation files with different expression tendencies:

[0068] Get the maximum value among zsl, fsl, and xsl, and denote it as mx1. Denote the remaining two values ​​as mx2 and mx3.

[0069] Set the first node as the expression tendency corresponding to mx1, and highlight it with a unique color; reduce mx1 to get the update count of mx1;

[0070] ;

[0071] Where: jsl represents the number of locally propagated files;

[0072] Compare the updated mx1 with mx2 and mx3, obtain the expression tendency corresponding to the maximum value, set it as the second node, and highlight it with a unique color;

[0073] Similarly, configure the third node;

[0074] Step S33: Set nodes for the expression tendency of the propagated files at different propagation times, arrange the nodes in chronological order, count the number of nodes of each type, and record the number of nodes of each type after the node to obtain the expression tendency map.

[0075] Furthermore, the specific steps of step S4 are as follows:

[0076] Step S41: Based on the number of files spread, a, obtain the number of forwards and comments on the files spread. Record the number of forwards as zf(1), zf(2), ..., zf(a), and record the number of comments on the files spread as pl(1), pl(2), ..., pl(a).

[0077] Weights qz1 and qz2 are assigned to the number of forwarded and commented-on files of the disseminated document; the influence value yxz(v) is calculated based on the number of forwarded and commented-on files of the disseminated document and their corresponding weights.

[0078] ;

[0079] Where: zf(v) represents the number of forwards of the v-th propagated file, and pl(v) represents the number of comments on the v-th propagated file;

[0080] Step S42: Combine the dissemination regions, statistically analyze the impact values ​​of disseminated documents in the same region, extract the largest impact value to obtain the regional impact extreme value, obtain the dissemination document corresponding to the regional impact extreme value, obtain the disseminator information based on the dissemination document, display the disseminator's avatar and nickname to obtain a regional representative figure display image; integrate the regional representative figure display images to obtain an influential figure image.

[0081] Furthermore, the specific steps of step S5 are as follows:

[0082] Step S51: Integrate the regional dissemination power map, dissemination node type map, expression tendency map, and influential figure map to display the regional dissemination power, the dissemination status of information at different time points, the expression tendency of information, and the most influential figures in the information dissemination process through images.

[0083] Step S52: Set the regional propagation force map in the center of the image, obtain the propagation region type b, set b equal division lines on the regional propagation force map according to the propagation region type, fill the propagation node type map along the division lines, fill the expression tendency map on the side of the propagation node type map, and add the influential person map at the end of the node type map according to the division lines; thus obtaining the multi-dimensional analysis map.

[0084] Compared with the prior art, the beneficial effects of the present invention are:

[0085] Multi-dimensional analysis: This invention provides a comprehensive perspective on information dissemination by integrating multiple dimensions; enabling users not only to observe the dissemination effect of a single event or media, but also to analyze the overall dissemination trend of information from multiple angles, thereby making more precise strategic adjustments.

[0086] Improved readability: The fan-shaped matrix display method adopted in this invention represents a propagation dimension by each sector. The size and color intensity of the sector are adjusted according to the propagation strength of that dimension. This intuitive visual presentation is not only aesthetically pleasing, but also improves the readability of information and the efficiency of information parsing for users. Users can see at a glance which dimensions have the strongest propagation power and which need to be strengthened, thus enabling them to react quickly.

[0087] Expanding the scope of application: By optimizing the layout and visual presentation of information, it ensures that users will not feel overwhelmed even when there is a large amount of information; this design is not only suitable for professional data analysts, but also for non-professional users, making the application scope of this tool wider. Attached Figure Description

[0088] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0089] Figure 1 This is a schematic diagram of the method of the present invention;

[0090] Figure 2 This is a schematic diagram of the node types of the present invention;

[0091] Figure 3 This is a diagram illustrating the multi-dimensional analysis of the present invention. Detailed Implementation

[0092] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0093] Example 1

[0094] Please see Figure 1 Methods for analyzing the effects of internet information dissemination based on multi-dimensional data include:

[0095] Step S1: Collect information on internet dissemination, including the disseminated file, dissemination time, and dissemination location. The disseminated file includes information about the disseminator, the content of the disseminated file, the number of times the disseminated file has been forwarded, and the number of times the disseminated file has been commented on.

[0096] It should be noted that collecting information disseminated on the Internet refers to obtaining publicly available information published by users on various platforms through application programming interfaces.

[0097] Step S2: Classify the disseminated documents according to the dissemination region to obtain regional dissemination documents, calculate the regional dissemination power based on the regional dissemination documents, and construct a regional dissemination power map based on the regional dissemination power;

[0098] The files are categorized according to their propagation time to obtain files propagated by time period. The number of files propagated by time period is counted, and the types of propagation nodes are analyzed. A propagation node type graph is then constructed.

[0099] Step S21: Count the number of disseminated files (a) and the types of disseminated regions (b); classify the disseminated files according to the types of disseminated regions to obtain regional disseminated files; record the number of regional disseminated files; normalize the number of regional disseminated files to obtain the regional dissemination power of each type of disseminated region; and construct a regional dissemination power map based on the regional dissemination power.

[0100] Step S211: Based on the number of files a and the types of regions b, classify the files to be spread, loop through the files to obtain the regions of each file, count the number of files in the same region, and obtain the number of files spread by region, denoted as cb(1), cb(2), ..., cb(b); where cb(1) + cb(2) + ... + cb(b) = a;

[0101] Step S212: Obtain the maximum value among the number of files propagated in a region, denoted as zdc; obtain the minimum value among the number of files propagated in a region, denoted as zxc; normalize the number of files propagated in a region based on the maximum number of files propagated in a region zbc and the minimum number of files propagated in a region zxc to obtain the regional propagation force cbl(i).

[0102] ;

[0103] Where: cb(i) refers to the number of files distributed in the region, 1≤i≤b;

[0104] Step S213: Construct a semi-circular fan-shaped region, obtain the maximum radian Π of the semi-circular fan-shaped region, set the radian hd of the region based on the maximum radian of the semi-circular fan-shaped region and the regional propagation force, sort the radians in descending order to obtain a descending radian list, fill the semi-circular fan-shaped region according to the descending radian list, and mark the propagation region type at the corresponding radian position; obtain the image of each propagation region, and display the images of each propagation region around the outside of the semi-circular fan-shaped region in the order of the descending radian list; obtain the regional propagation force map.

[0105] Step S22: Obtain the propagation time of information spread on the Internet, divide the propagation time into stages to obtain the time periods, analyze the time periods to obtain the changes in the number of propagated files in the time periods, obtain the propagation node types based on the changes in the number of propagated files, perform statistics on the propagation node types, and construct a propagation node graph;

[0106] Step S221: Obtain the start propagation time, denoted as qsj; obtain the end propagation time, denoted as zsj; calculate the duration interval qj based on the start and end propagation times, qj = zsj - qsj; divide the duration interval into three equal parts to divide the propagation time into the early stage, middle stage, and late stage of information propagation.

[0107] The early stage of information propagation is denoted as xcq, and the value range of xcq is [qsj, qsj + qj / 3].

[0108] The middle stage of information propagation is denoted as xcz, and the value range of xcz is [qsj + qj / 3, zsj - qj / 3].

[0109] The later stage of information propagation is denoted as xch, and the value range of xch is [zsj-qj / 3,zsj];

[0110] The information dissemination process was analyzed in three stages: the early stage, the middle stage, and the late stage, to identify information nodes at different stages of the dissemination.

[0111] Step S2211: Analyze the early stage of information propagation, extract the propagation files with propagation time in xcq, obtain the propagation files in the early stage of information propagation, and obtain the number of files at different times based on the propagation files in the early stage of information propagation, denoted as qws(qt), where: qws(qt) represents the number of files at time qt as qws; qt∈[qsj, qsj+qj / 3];

[0112] Please see Figure 2 The image is plotted using qws(qt) to obtain the curve showing the change in the number of files being spread over time; qs nodes are selected and evenly distributed on the curve.

[0113] Based on the change curve, the rate of change at the node's front end is calculated to obtain the first judgment value ypd(j) for the previous node:

[0114] ;

[0115] Where: Δqt represents the small change in qt; qsj and qj are the parameters for the value of qt, qt∈[qsj, qsj+qj / 3];

[0116] Based on the change curve, the rate of change at the back end of the node is calculated to obtain the second judgment value epd(j) of the previous node:

[0117] ;

[0118] The node type is determined based on the first judgment value ypd(j) and the second judgment value epd(j) of the previous node:

[0119] If ypd(j) = 0, then the node is the starting point;

[0120] If epd(j) = 0, then the node is the endpoint;

[0121] If epd(j) > ypd(j) > 0, then the node is an ascending node;

[0122] If ypd(j) ≥ epd(j) > 0, then the node is a blockage point;

[0123] If ypd(j) > 0 > epd(j), then the node is a burst point;

[0124] If ypd(j) < 0 and epd(j) < 0, then the node is a descending node;

[0125] It should be noted that: the starting point represents the initial moment when the curve begins to rise, marking the transition of quantity from a static or low level to a growth phase; the rising point represents the node where the growth rate begins to accelerate, and the slope of the curve becomes steeper; the bursting point represents the point where the quantity reaches its peak or critical threshold, which is the highest point of the growth phase; the bottleneck point represents the node where growth is hindered, and the growth rate of quantity decreases significantly or stagnates; the falling point represents the node where the quantity begins to decline continuously, marking the entry into the decline phase; and the ending point represents the endpoint where the quantity tends to stabilize or return to zero, and the process is completely over.

[0126] Step S2212: Analyze the mid-term of information propagation, extract the propagation files at time xcz to obtain the propagation files in the mid-term of information propagation, and obtain the number of files at different times based on the propagation files in the mid-term of information propagation, denoted as zws(zt), where: zws(zt) represents the number of files at time zt as zws; zt∈[qsj+qj / 3,zsj-qj / 3];

[0127] The image is plotted using zws(zt), resulting in a curve showing the change in the number of files propagated over time. zs nodes are then selected and evenly distributed across the curve.

[0128] Based on the change curve, the rate of change at the node front end is calculated to obtain the first judgment value yzd(k) for the mid-term node:

[0129] ;

[0130] Where: Δzt represents the small change in zt; qsj and qj are the parameters for the value of zt, zt∈[qsj+qj / 3, zsj-qj / 3];

[0131] Based on the change curve, the rate of change at the back end of the node is calculated to obtain the second judgment value ezd(k) for the intermediate node:

[0132] ;

[0133] Based on the first judgment value yzd(k) and the second judgment value ezd(k) of the intermediate node, the intermediate node type is determined in conjunction with the judgment process in step S2211 to obtain the intermediate node type.

[0134] Step S2213: Analyze the later stage of information propagation, extract the propagation files with propagation time in xch, and obtain the propagation files in the later stage of information propagation. Based on the propagation files in the later stage of information propagation, obtain the number of files at different times, denoted as hws(ht), where: hws(ht) represents the number of files at time ht as hws; ht∈[zsj-qj / 3,zsj];

[0135] The image is plotted using hws(ht), resulting in a curve showing the change in the number of files transmitted over time. hs nodes are then selected and evenly distributed across the curve.

[0136] Based on the change curve, the rate of change at the node's leading edge is calculated to obtain the first judgment value yhd(h) for the later node:

[0137] ;

[0138] Where: Δht represents the small change in ht; zsj and qj are the parameters for the value of ht, ht∈[zsj-qj / 3, zsj];

[0139] Based on the change curve, the rate of change at the rear end of the node is calculated to obtain the second judgment value ehd(h) for the later node:

[0140] ;

[0141] Based on the first judgment value yhd(h) and the second judgment value ehd(h) of the later node, the type of the later node is judged in conjunction with the judgment process in step S2211 to obtain the type of the later node.

[0142] Step S222: Obtain node types, highlight different node types with different colors, sort the nodes in the order of early information propagation, middle information propagation, and late information propagation, and construct a propagation node graph;

[0143] Step S3: Analyze the content of the disseminated files, determine the expression tendency of the disseminated files, count the number of files with different expression tendencies, and visualize the statistical results; thus obtaining an expression tendency graph.

[0144] Step S31: Obtain the content of the file to be disseminated (txt), and analyze the expressive tendency of the content using TextBlob to obtain the sentiment polarity of the content; specifically as follows:

[0145] from textblob import TextBlob

[0146] text = txt

[0147] blob = TextBlob(text)

[0148] print(blob.sentiment) # Output: polarity=-0.3

[0149] It should be noted that polarity=-0.3 represents the sentiment polarity of the transmitted file content (txt), with the sentiment polarity ranging from -1 (negative) to 1 (positive).

[0150] Based on the emotional polarity of the content of the disseminated document, the disseminated document can be defined as a positive disseminated document, a negative disseminated document, or a neutral disseminated document;

[0151] Among them, the sentiment polarity of positive dissemination documents is [0.33, 1], the sentiment polarity of negative dissemination documents is [-1, -0.33], and the sentiment polarity of neutral dissemination documents is [-0.33, 0.33].

[0152] Step S32: Extract the propagation files with the same propagation time and propagation area, and denote them as local propagation files. Obtain the number of local propagation files jsl; obtain the number of positive propagation files in the local propagation files, and denote it as zsl; obtain the number of negative propagation files in the local propagation files, and denote it as fsl; obtain the number of neutral propagation files in the local propagation files, and denote it as xsl.

[0153] zsl + fsl + xsl = jsl;

[0154] Define three nodes to store propagation files with different expression tendencies:

[0155] Get the maximum value among zsl, fsl, and xsl, and denote it as mx1. Denote the remaining two values ​​as mx2 and mx3.

[0156] Set the first node as the expression tendency corresponding to mx1, and highlight it with a unique color; reduce mx1 to get the update count of mx1;

[0157] ;

[0158] Where: jsl represents the number of locally propagated files;

[0159] Compare the updated mx1 with mx2 and mx3, obtain the expression tendency corresponding to the maximum value, set it as the second node, and highlight it with a unique color;

[0160] Similarly, configure the third node.

[0161] For example, if zsl=60, fsl=50, xsl=10, then the maximum value is the number of files corresponding to positive propagation. In this case, the first node is set to positive orientation and represented in blue. Then the value of zal is reduced, zsl=60-(60+50+10) / 3=20.

[0162] The values ​​for the second comparison are zsl=20, fsl=50, and xsl=10. The maximum value corresponds to the number of negatively propagated files. Therefore, the second node is set to negative tendency and is indicated in red. Then, the value of fsl is reduced to fal=50-(60+50+10) / 3=10.

[0163] The values ​​for the third comparison are zsl=20, fsl=10, and xsl=10. The maximum value corresponds to the number of files propagated in the positive direction. Therefore, the third node is set to the positive orientation and is represented by blue.

[0164] Step S33: Set nodes for the expression tendency of the propagated files at different propagation times, arrange the nodes in chronological order, count the number of nodes of each type, and record the number of nodes of each type after the node to obtain the expression tendency map.

[0165] Step S4: Calculate the impact value of the disseminated file based on the number of forwards and comments on the disseminated file, and display the corresponding disseminator information based on the impact value to construct an influencer map;

[0166] Step S41: Based on the number of files spread, a, obtain the number of forwards and comments on the files spread. Record the number of forwards as zf(1), zf(2), ..., zf(a), and record the number of comments on the files spread as pl(1), pl(2), ..., pl(a).

[0167] Based on the forwarding volumes of the propagated files zf(1), zf(2), ..., zf(a), the average forwarding volume of the propagated files is calculated to obtain the forwarding average zfp;

[0168] The weight qz1 is set based on the average forwarding value and the number of forwarded files.

[0169] ;

[0170] Where: zf(z) represents the number of times the z-th propagation file is forwarded;

[0171] Based on the number of comments on the disseminated document pl(1), pl(2), ..., pl(a), the average number of comments on the disseminated document is calculated to obtain the average comment value plp;

[0172] The weights qz2 are set based on the average number of comments and the number of comments on the disseminated file.

[0173] ;

[0174] Where: pl(z) represents the number of comments on the z-th propagated file;

[0175] The influence value yxz(v) is calculated based on the number of times the document is forwarded and the number of comments on the document, and their corresponding weights:

[0176] ;

[0177] Where: zf(v) represents the number of forwards of the v-th propagated file, and pl(v) represents the number of comments on the v-th propagated file;

[0178] Step S42: Combine the dissemination regions, statistically analyze the impact values ​​of disseminated documents in the same region, extract the largest impact value to obtain the regional impact extreme value, obtain the dissemination document corresponding to the regional impact extreme value, obtain the disseminator information based on the dissemination document, display the disseminator's avatar and nickname to obtain a regional representative figure display image; integrate the regional representative figure display images to obtain an influential figure image.

[0179] Step S5: Integrate the regional dissemination power map, dissemination node type map, expression tendency map, and influential figure map to construct a multi-dimensional analysis map;

[0180] Please see Figure 3Step S51: Integrate the regional dissemination power map, dissemination node type map, expression tendency map, and influential figure map to display the regional dissemination power, the dissemination status of information at different time points, the expression tendency of information, and the most influential figures in the information dissemination process through images.

[0181] Step S52: Set the regional propagation force map in the center of the image, obtain the propagation region type b, set b equal division lines on the regional propagation force map according to the propagation region type, fill the propagation node type map along the division lines, fill the expression tendency map on the side of the propagation node type map, and add the influential person map at the end of the node type map according to the division lines; thus obtaining the multi-dimensional analysis map.

[0182] The above formulas are all dimensionless calculations. The formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation. For example, there are weighting coefficients and proportional coefficients. The values ​​set are to quantify each parameter to obtain a specific value, which is convenient for subsequent comparison. The values ​​of the weighting coefficients and proportional coefficients are only required to not affect the proportional relationship between the parameters and the quantified values.

[0183] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for analyzing the effects of internet information dissemination based on multi-dimensional data, characterized in that, The analytical method includes: Step S1: Collect information on internet dissemination, including the disseminated file, dissemination time, and dissemination location. The disseminated file includes information about the disseminator, the content of the disseminated file, the number of times the disseminated file has been forwarded, and the number of times the disseminated file has been commented on. Step S2: Classify the disseminated documents according to the dissemination region to obtain regional dissemination documents, calculate the regional dissemination power based on the regional dissemination documents, and construct a regional dissemination power map based on the regional dissemination power; The files are categorized according to their propagation time to obtain files propagated by time period. The number of files propagated by time period is counted, and the types of propagation nodes are analyzed. A propagation node type graph is then constructed. Step S3: Analyze the content of the disseminated files, determine the expression tendency of the disseminated files, count the number of files with different expression tendencies, and visualize the statistical results; thus obtaining an expression tendency graph. Step S4: Calculate the impact value of the disseminated file based on the number of forwards and comments on the disseminated file, and display the corresponding disseminator information based on the impact value to construct an influencer map; Step S5: Integrate the regional dissemination power map, dissemination node type map, expression tendency map, and influential figure map to construct a multi-dimensional analysis map; The specific steps of step S2 are as follows: Step S21: Count the number of disseminated files (a) and the types of disseminated regions (b); classify the disseminated files according to the types of disseminated regions to obtain regional disseminated files; record the number of regional disseminated files; normalize the number of regional disseminated files to obtain the regional dissemination power of each type of disseminated region; and construct a regional dissemination power map based on the regional dissemination power. Step S22: Obtain the propagation time of information spread on the Internet, divide the propagation time into stages to obtain the time periods, analyze the time periods to obtain the changes in the number of propagated files in the time periods, obtain the propagation node types based on the changes in the number of propagated files, perform statistics on the propagation node types, and construct a propagation node graph; The specific steps of step S22 are as follows: Step S221: Obtain the start propagation time, denoted as qsj; obtain the end propagation time, denoted as zsj; calculate the duration interval qj based on the start and end propagation times, qj = zsj - qsj; divide the duration interval into three equal parts to divide the propagation time into the early stage, middle stage, and late stage of information propagation. Let the early stage of information propagation be denoted as xcq, and the value range of xcq is [qsj, qsj + qj / 3]; The intermediate stage of information dissemination is denoted as xcz, and the value range of xcz is [qsj + qj / 3, zsj - qj / 3]. The later stage of information propagation is denoted as xch, and the value range of xch is [zsj-qj / 3,zsj]; The information dissemination process was analyzed in three stages: the early stage, the middle stage, and the late stage, to identify information nodes at different stages of the dissemination. Step S222: Obtain node types, highlight different node types with different colors, sort the nodes in the order of early information propagation, middle information propagation, and late information propagation, and construct a propagation node graph; The specific steps of step S221 are as follows: Analyze the early stage of information dissemination, extract the dissemination files at time xcq to obtain the dissemination files in the early stage of information dissemination, and obtain the number of files at different times based on the dissemination files in the early stage of information dissemination, denoted as qws(qt), where: qws(qt) represents the number of files at time qt is qws; qt∈[qsj, qsj+qj / 3]; The image is plotted using qws(qt), resulting in a curve showing the change in the number of files transmitted over time. qs nodes are then selected and evenly distributed across the curve. Based on the change curve, the rate of change at the node's front end is calculated to obtain the first judgment value ypd(j) for the previous node: ； Where: Δqt represents the small change in qt; qsj and qj are the parameters for the value of qt, qt∈[qsj, qsj+qj / 3]; Based on the change curve, the rate of change at the back end of the node is calculated to obtain the second judgment value epd(j) of the previous node: ； The node type is determined based on the first judgment value ypd(j) and the second judgment value epd(j) of the previous node: If ypd(j) = 0, then the node is the starting point; If epd(j) = 0, then the node is the endpoint; If epd(j) > ypd(j) > 0, then the node is an ascending node; If ypd(j) ≥ epd(j) > 0, then the node is a blockage point; If ypd(j) > 0 > epd(j), then the node is a burst point; If ypd(j) < 0 and epd(j) < 0, then the node is a descending node.

2. The method for analyzing the internet information dissemination effect based on multi-dimensional data according to claim 1, characterized in that, The specific steps of step S21 are as follows: Step S211: Based on the number of files a and the types of regions to be spread, classify the files to be spread, loop through the files to obtain the regions to be spread for each file, count the number of files in the same region to obtain the number of files to be spread in each region, denoted as cb(1), cb(2), ..., cb(b); Step S212: Obtain the maximum value among the number of files propagated in each region, and denote the maximum number of files propagated in each region as zdc; Find the minimum number of files distributed in each region, and denote the minimum number of files distributed in each region as zxc; Based on the maximum number of regionally propagated files zbc and the minimum number of regionally propagated files zxc, the number of regionally propagated files is normalized to obtain the regional propagation force cbl(i). ； Where: cb(i) refers to the number of files distributed in the region, 1≤i≤b; Step S213: Construct a semi-circular fan-shaped region, obtain the maximum radian Π of the semi-circular fan-shaped region, set the radian hd of the region based on the maximum radian of the semi-circular fan-shaped region and the regional propagation force, sort the radians in descending order to obtain a descending radian list, fill the semi-circular fan-shaped region according to the descending radian list, and mark the propagation region type at the corresponding radian position; obtain the image of each propagation region, and display the images of each propagation region around the outside of the semi-circular fan-shaped region in the order of the descending radian list; obtain the regional propagation force map.

3. The method for analyzing the internet information dissemination effect based on multi-dimensional data according to claim 1, characterized in that, The specific steps of step S221 also include: The mid-term of information dissemination is analyzed, and the dissemination files at time xcz are extracted to obtain the dissemination files in the mid-term of information dissemination. Based on the dissemination files in the mid-term of information dissemination, the number of files at different times is obtained, denoted as zws(zt), where: zws(zt) represents the number of files at time zt as zws; zt∈[qsj+qj / 3,zsj-qj / 3]; The image is plotted using zws(zt), resulting in a curve showing the change in the number of files propagated over time. zs nodes are then selected and evenly distributed across the curve. Based on the change curve, the rate of change at the node front end is calculated to obtain the first judgment value yzd(k) for the mid-term node: ； Where: Δzt represents the small change in zt; qsj and qj are the parameters for the value of zt, zt∈[qsj+qj / 3, zsj-qj / 3]; Based on the change curve, the rate of change at the back end of the node is calculated to obtain the second judgment value ezd(k) for the intermediate node: ； The intermediate node type is determined by judging the first judgment value yzd(k) and the second judgment value ezd(k) of the intermediate node.

4. The method for analyzing the internet information dissemination effect based on multi-dimensional data according to claim 1, characterized in that, The specific steps of step S221 also include: The later stage of information propagation is analyzed, and the propagation files at the xch time point are extracted to obtain the propagation files in the later stage of information propagation. Based on the propagation files in the later stage of information propagation, the number of files at different times is obtained, denoted as hws(ht), where: hws(ht) represents the number of files at time ht as hws; ht∈[zsj-qj / 3,zsj]; The image is plotted using hws(ht), resulting in a curve showing the change in the number of files transmitted over time. hs nodes are then selected and evenly distributed across the curve. Based on the change curve, the rate of change at the node's leading edge is calculated to obtain the first judgment value yhd(h) for the later node: ； Where: Δht represents the small change in ht; zsj and qj are the parameters for the value of ht, ht∈[zsj-qj / 3, zsj]; Based on the change curve, the rate of change at the rear end of the node is calculated to obtain the second judgment value ehd(h) for the later node: ； The type of the later node is determined by judging the first judgment value yhd(h) and the second judgment value ehd(h) of the later node.

5. The method for analyzing the internet information dissemination effect based on multi-dimensional data according to claim 1, characterized in that, The specific steps of step S3 are as follows: Step S31: Obtain the content of the dissemination document, analyze the expressive tendency of the dissemination document content, and obtain the emotional polarity of the dissemination document content; based on the emotional polarity of the dissemination document content, define the dissemination document as a positive dissemination document, a negative dissemination document, or a neutral dissemination document; Step S32: Extract the propagation files with the same propagation time and propagation area, and denote them as local propagation files. Obtain the number of local propagation files jsl; obtain the number of positive propagation files in the local propagation files, and denote it as zsl; obtain the number of negative propagation files in the local propagation files, and denote it as fsl; obtain the number of neutral propagation files in the local propagation files, and denote it as xsl. zsl + fsl + xsl = jsl; Define three nodes to store propagation files with different expression tendencies: Get the maximum value among zsl, fsl, and xsl, and denote it as mx1. Denote the remaining two values ​​as mx2 and mx3. Set the first node as the expression tendency corresponding to mx1, and highlight it with a unique color; reduce mx1 to get the update count of mx1; ； Where: jsl represents the number of locally propagated files; Compare the updated mx1 with mx2 and mx3, obtain the expression tendency corresponding to the maximum value, set it as the second node, and highlight it with a unique color; Similarly, configure the third node; Step S33: Set nodes for the expression tendency of the propagated files at different propagation times, arrange the nodes in chronological order, count the number of nodes of each type, and record the number of nodes of each type after the node to obtain the expression tendency map.

6. The method for analyzing the internet information dissemination effect based on multi-dimensional data according to claim 1, characterized in that, The specific steps of step S4 are as follows: Step S41: Based on the number of files spread, a, obtain the number of forwards and comments on the files spread. Record the number of forwards as zf(1), zf(2), ..., zf(a), and record the number of comments on the files spread as pl(1), pl(2), ..., pl(a). Weights qz1 and qz2 are assigned to the number of forwarded and commented-on files of the disseminated document; the influence value yxz(v) is calculated based on the number of forwarded and commented-on files of the disseminated document and their corresponding weights. ； Where: zf(v) represents the number of forwards of the v-th propagated file, and pl(v) represents the number of comments on the v-th propagated file; Step S42: Combine the dissemination regions, statistically analyze the impact values ​​of disseminated documents in the same region, extract the largest impact value to obtain the regional impact extreme value, obtain the dissemination document corresponding to the regional impact extreme value, obtain the disseminator information based on the dissemination document, display the disseminator's avatar and nickname to obtain a regional representative figure display image; integrate the regional representative figure display images to obtain an influential figure image.

7. The method for analyzing the internet information dissemination effect based on multi-dimensional data according to claim 1, characterized in that, The specific steps of step S5 are as follows: Step S51: Integrate the regional dissemination power map, dissemination node type map, expression tendency map, and influential figure map to display the regional dissemination power, the dissemination status of information at different time points, the expression tendency of information, and the most influential figures in the information dissemination process through images. Step S52: Set the regional propagation force map in the center of the image, obtain the propagation region type b, set b equal division lines on the regional propagation force map according to the propagation region type, fill the propagation node type map along the division lines, fill the expression tendency map on the side of the propagation node type map, and add the influential person map at the end of the node type map according to the division lines; thus obtaining the multi-dimensional analysis map.

Images