CN112559679B - Political new media propagation force detection method, device, equipment and storage medium - Google Patents
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Abstract
The invention discloses a method for detecting political new media transmission force, which comprises the following steps: acquiring new media data of a government department to be detected; inputting the new media data into a pre-trained political news classification model to obtain political new media data; and calculating the propagation force of the political new media according to the political new media data. According to the method for detecting the government law new media transmission force, which is provided by the embodiment of the invention, objective data such as manuscripts, audience behavior data and the like which are issued by a plurality of new media channels are collected, and the new media transmission force of government departments is quantitatively analyzed, so that the real-time performance, objectivity and accuracy of detection results are greatly improved.
Description
Technical Field
The invention relates to the technical field of new media, in particular to a method, a device, equipment and a storage medium for detecting political new media transmission force.
Background
In recent years, with the rapid development of internet technology and 5G technology, various media propagation channels are continuously emerging, such as new media of WeChat public numbers, microblogs, tremble sounds, today's top, and the like, and compared with traditional media, the propagation of the new media is more focused on interaction with audiences, such as praise, comments, forwarding, and the like.
In the current politics field, the measurement and evaluation of the media propaganda department is based on the measurement standard of the traditional media, such as the number of letters, the reading quantity and the like, the data source of the traditional method is based on the report of each department, the real-time property and objectivity of the data have great problems, and the characteristic of more emphasized interactivity of the new media is not specific quantitative indexes, so that the propagation force index system of the traditional media cannot be used for measuring the propagation force of the new media.
Disclosure of Invention
The embodiment of the disclosure provides a method, a device, equipment and a storage medium for detecting political new media propagation force. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In a first aspect, an embodiment of the present disclosure provides a method for detecting a political new media propagation force, including:
Acquiring new media data of a government department to be detected;
Inputting the new media data into a pre-trained political news classification model to obtain political new media data;
and calculating the propagation force of the political new media according to the political new media data.
In one embodiment, after obtaining new media data for the government agency to be detected, further comprising:
Word segmentation processing and vectorization processing are carried out on the text of the new media data, and vectorized text data are obtained;
And extracting keywords in the text data after the orientation quantization by adopting a TF-IDF algorithm.
In one embodiment, before inputting the new media data into the pre-trained political news classification model, further comprising:
Acquiring a news corpus in the marked political field;
Word segmentation and vectorization are carried out on text data in a news corpus to obtain vectorized text data;
Extracting keywords of the text data after orientation quantization by adopting a TF-IDF algorithm;
And training a political news classification model according to the keywords.
In one embodiment, calculating the propagation force of political new media from political new media data comprises:
and calculating the transmission force of the political new media according to the release number, the reading number, the influence, the praise number, the vermicelli number, the comment number and the political related number in the new media data.
In one embodiment, the number of articles of political new media obtained from the political news classification model yields a political correlation number.
In one embodiment, calculating the influence comprises:
Acquiring articles to be calculated in political new media data and all news articles acquired in a preset time period;
Calculating the similarity between the articles to be calculated and all the news articles collected;
Adding news articles with similarity larger than a preset threshold value into a similar article list;
And obtaining an influence value according to the number of the articles in the similar article list.
In a second aspect, an embodiment of the present disclosure provides a device for detecting a political new media propagation force, including:
the acquisition module is used for acquiring new media data of the government departments to be detected;
the classification module is used for inputting the new media data into a pre-trained political news classification model to obtain political new media data;
And the calculating module is used for calculating the propagation force of the political new media according to the political new media data.
In a third aspect, an embodiment of the disclosure provides a device for detecting a political new media propagation force, which includes a processor and a memory storing program instructions, where the processor is configured to execute the method for detecting a political new media propagation force provided in the foregoing embodiment when executing the program instructions.
In a fourth aspect, an embodiment of the disclosure provides a computer readable medium having computer readable instructions stored thereon, where the computer readable instructions are executable by a processor to implement a method for detecting a political new media propagation force provided by the above embodiment.
The technical scheme provided by the embodiment of the disclosure can comprise the following beneficial effects:
According to the method for detecting the transmission force of the new political media, which is provided by the embodiment of the disclosure, objective data such as manuscripts, audience behavior data and the like which are issued by a plurality of new media channels are collected, and the analysis data are quantized, so that the transmission force of the new media of a government department is detected, and the real-time performance, accuracy and objectivity of a detection result are greatly improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow chart illustrating a method of detecting political new media transmission forces, according to an exemplary embodiment;
FIG. 2 is a flow diagram illustrating a method of training a political news classification model in accordance with an exemplary embodiment;
FIG. 3 is a flow diagram illustrating a method of operating a political news classification model in accordance with an exemplary embodiment;
FIG. 4 is a flow chart diagram illustrating a method of influence calculation according to an exemplary embodiment;
FIG. 5 is a schematic diagram of a device for detecting political new media transmission forces, according to an example embodiment;
FIG. 6 is a schematic diagram of a political new media transmission force detection device, according to an example embodiment;
Fig. 7 is a schematic diagram of a computer storage medium shown according to an example embodiment.
Detailed Description
So that the manner in which the features and techniques of the disclosed embodiments can be understood in more detail, a more particular description of the embodiments of the disclosure, briefly summarized below, may be had by reference to the appended drawings, which are not intended to be limiting of the embodiments of the disclosure. In the following description of the technology, for purposes of explanation, numerous details are set forth in order to provide a thorough understanding of the disclosed embodiments. However, one or more embodiments may still be practiced without these details. In other instances, well-known structures and devices may be shown simplified in order to simplify the drawing.
The method for detecting the political new media transmission force according to the embodiment of the application will be described in detail with reference to fig. 1 to 4.
Referring to fig. 1, the method specifically includes the following steps.
S101 acquires new media data of a government department to be detected.
In one exemplary scenario, it is desirable to detect the new media distribution forces of a government agency, and first acquire new media data that the agency has released within a preset period of time. For example, new media data that the department has released within a month is acquired.
The new media data may include media data such as WeChat data, microblog data, tremble data, and today's headline data.
S102, inputting the new media data into a pre-trained political news classification model to obtain political new media data.
In one possible implementation, the acquired new media data may not be political-related new media data, and thus, the acquired new media data is first input into a pre-trained political-news classification model to obtain new media data related to the political field.
Specifically, word segmentation and vectorization are carried out on the text of the obtained new media data to obtain vectorized text data, the reverse document frequency of each word is calculated by adopting a TF-IDF algorithm, keywords in the vectorized text data are extracted according to the reverse document frequency, and the keywords are input into a pre-trained political news classification model to obtain media data related to the political field.
Fig. 3 is a flow chart illustrating a method for operating a political news classification model according to an exemplary embodiment, as shown in fig. 3, in which a trained political news classification model is loaded first, then text data is acquired, word segmentation and vectorization are performed on the text to obtain vectorized text data, keywords in the vectorized text data are extracted by a TF-IDF algorithm, the keywords are input into the classification model to be classified, new media data related to the political field are obtained, and classification results are stored.
In one exemplary scenario, training the political news classification model is also included before inputting the new media data into the pre-trained political news classification model.
Specifically, a news corpus in the marked politics field is obtained, in one possible implementation manner, related corpora of politics class are grabbed from new media platforms such as WeChat public numbers, present headings and the like by utilizing a crawler technology, 43774 articles are obtained after preprocessing operations such as format processing, duplication removal and the like, and the articles are manually marked to obtain 1875 politics class, 6647 public security class, 8404 inspection class, 8720 court class, 8021 judicial class, 7301 politics class and 2806 other classes.
Word segmentation and vectorization are carried out on text data in a news corpus to obtain vectorized text data, keywords of the vectorized text data are extracted by adopting a TF-IDF algorithm, and a political news classification model is trained according to the keywords.
Fig. 2 is a schematic flow chart of a method for training a political news classification model according to an exemplary embodiment, as shown in fig. 2, training text data, that is, a labeled political news corpus is first obtained, then word segmentation and vectorization are performed on text in the corpus, keywords of the text data after the orientation quantization are extracted by using a TF-IDF algorithm, the political news classification model is trained according to the keywords, verification is performed by using a verification set, if the classification model meets the requirements, training is stopped, and the trained classification model is saved.
According to this step, new media data related to the political field can be obtained.
S103, calculating the propagation force of the political new media according to the political new media data.
In order to adapt to the form characteristics of new media, the embodiment of the disclosure calculates the transmission force of political new media according to the release number, reading number, influence, praise number, vermicelli number, comment number and political related number in the new media data.
In one possible implementation, the new media data may include WeChat data, microblog data, tremble data, and today's headline data.
The WeChat data comprises article release numbers, release times, average reading numbers, highest reading numbers, influence, average praise numbers, highest praise numbers and political related numbers. The microblog data comprises the number of microblog publications, the number of original microblogs, the number of vermicelli, the number of forwarding, the number of comments, the average praise number and the highest praise number. The tremble data includes the number of publications, the number of fans, the number of comments, the number of shares, the average praise, and the highest praise. The present day top data includes posting count, fan count, reading count, comment count, average praise count, highest praise count, political correlation count.
Typically, users will post articles on WeChat and today's headlines, and political related numbers are established in order to obtain the number of articles published that are related to the political field. In one possible implementation, the number of articles related to the political field obtained according to the political news classification model is the political relevance number.
For example, the police department may obtain documents related to public security according to the classification model, the inspection department may obtain documents related to the inspection department according to the classification model, and the court may obtain documents related to the court.
In general, there are no forwarding volumes for WeChat and headline, and there are some articles with no labeling sources, and in order to know the influence of a certain article issued by a government agency, the forwarding volumes of the certain article need to be calculated.
FIG. 4 is a schematic diagram of calculating the influence of an article, as shown in FIG. 4, including: the method comprises the steps of obtaining articles to be calculated in political new media data and all news articles collected in a preset time period, preprocessing the obtained articles, including word segmentation of the obtained articles by utilizing a natural language processing technology, feature word extraction to form feature parameters of texts, then calculating similarity between the articles to be calculated and all the news articles on a collected network, adding the network news articles with similarity larger than a preset threshold value into a similar article list, and obtaining influence values according to the number of the articles in the similar article list. According to the step, the articles which are similar to the articles sent by the government departments and flow on the network can be obtained, and the forwarding quantity of the articles sent by the government departments, namely the influence of the articles sent by the government departments, is obtained.
Further, according to the political new media data, the propagation force of the new media is calculated. In one possible implementation manner, a micro-letter propagation force, a micro-blog propagation force, a tremble sound propagation force and a present head bar propagation force are calculated respectively, and the propagation force of the political new media is calculated according to the preset weights of the micro-letter propagation force, the micro-blog propagation force, the tremble sound propagation force and the present head bar propagation force.
Specifically, the calculation method of the WeChat propagation force N 1 is as follows:
Wherein N max represents the maximum value of the article number of all departments within the statistical range, N 111 represents the article number of the current department to be detected, F max represents the maximum value of the article number of all departments within the statistical range, N 112 represents the article number of the current department to be detected, Representing the maximum value of the average reading of all departments in the statistical range, N 121 representing the average reading of the current department to be detected, R max representing the maximum value of the highest reading of all departments in the statistical range, N 122 representing the highest reading of the current department to be detected, I max representing the maximum value of the influence of all departments in the statistical range, N 123 representing the influence of the current department to be detected,/>Representing the maximum value of the average praise numbers of all departments in the statistical range, N 131 representing the average praise number of the current department to be detected, Z max representing the maximum value of the highest praise number of all departments in the statistical range, N 132 representing the highest praise number of the current department to be detected, C max representing the maximum value of the political news correlation numbers of all departments in the statistical range, and N 141 representing the political correlation numbers of the current department to be detected.
Wherein, the selection of the weight satisfies the normalization condition:
the microblog propagation force N 2 is calculated as follows:
N max represents the maximum value of the article number issued by all departments in the statistical range, N 211 represents the article number issued by the current department to be detected, Y max represents the maximum value of the original microblog number issued by all departments in the statistical range, N 212 represents the original microblog number of the current department to be detected, B max represents the maximum value of the vermicelli number of all departments in the statistical range, N 221 represents the vermicelli number of the current department to be detected, I max represents the maximum value of the forwarding number of all departments in the statistical range, N 222 represents the forwarding number of the current department to be detected, P max represents the maximum value of the comment number of all departments in the statistical range, N 223 represents the comment number of the current department to be detected, Representing the maximum value of the mean praise of all departments within the statistical range, N 231 representing the mean praise of the current department to be detected, Z max representing the maximum value of the highest praise of all departments within the statistical range, and N 232 representing the highest praise of the current department to be detected.
Wherein, the selection of the weight satisfies the normalization condition:
The jitter propagation force N 3 is calculated as follows:
Wherein N max represents the maximum value of the article number of all departments in the statistical range, N 311 represents the number of the current departments to be detected, B max represents the maximum value of the vermicelli number of all departments in the statistical range, N 321 represents the vermicelli number of the current departments to be detected, P max represents the maximum value of the comment number of all departments in the statistical range, N 322 represents the comment number of the current departments to be detected, I max represents the maximum value of the forwarding number of all departments in the statistical range, N 323 represents the forwarding number of the current departments to be detected, Representing the maximum value of the mean praise of all departments within the statistical range, N 331 representing the mean praise of the current department to be detected, Z max representing the maximum value of the highest praise of all departments within the statistical range, and N 332 representing the highest praise of the current department to be detected.
Wherein, the selection of the weight satisfies the normalization condition:
The present-day head-strip propagation force N 4 is calculated as follows:
Wherein N max represents the maximum value of the number of articles issued by all departments in the statistical range, N 411 represents the number of articles issued by the current department to be detected, B max represents the maximum value of the number of vermicelli of all departments in the statistical range, N 421 represents the number of vermicelli of the current department to be detected, R max represents the maximum value of the reading number of all departments in the statistical range, N 422 represents the reading number of the current department to be detected, P max represents the maximum value of the number of comments of all departments in the statistical range, N 423 represents the number of comments of the current department to be detected, Representing the maximum value of the average praise numbers of all departments in the statistical range, N 431 representing the average praise number of the current department to be detected, Z max representing the maximum value of the highest praise number of all departments in the statistical range, N 432 representing the highest praise number of the current department to be detected, C max representing the maximum value of the political correlation numbers of all departments in the statistical range, and N 441 representing the political correlation numbers of the current department to be detected.
Wherein, the selection of the weight satisfies the normalization condition:
after obtaining the WeChat transmission force, the microblog transmission force, the tremble transmission force and the today's head transmission force, calculating new media data transmission force NCI of the government department according to preset weights:
NCI＝w1*N1+w2*N2+w3*N3+w4*N4
Likewise, the weights also satisfy the normalization condition:
The weight of each propagation channel can be set by a person skilled in the art, and the embodiments of the present disclosure are not particularly limited. From this step, new media transmission forces for the government authorities to be detected can be calculated. If the new media transmission forces of the departments are required to be ordered, the new media transmission forces of the departments can be calculated respectively according to the method.
According to the method for detecting the transmission force of the political new media, which is provided by the embodiment of the disclosure, objective data such as manuscripts, audience behavior data and the like which are released by a plurality of new media channels are collected, the transmission force of the new media of government departments is detected, and the real-time performance, accuracy and objectivity of detection results are greatly improved.
The embodiment of the present disclosure further provides a device for detecting a political new media propagation force, where the device is configured to execute the method for detecting a political new media propagation force according to the foregoing embodiment, as shown in fig. 5, and the device includes:
an obtaining module 501, configured to obtain new media data of a government department to be detected;
The classification module 502 is configured to input new media data into a pre-trained political news classification model to obtain political new media data;
a calculating module 503, configured to calculate a propagation force of the political new media according to the political new media data.
It should be noted that, when the political new media transmission force detection device provided in the foregoing embodiment performs the political new media transmission force detection method, only the division of the foregoing functional modules is used for illustration, in practical application, the foregoing functional allocation may be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the device for detecting the political new media transmission force provided in the above embodiment belongs to the same concept as the embodiment of the method for detecting the political new media transmission force, and the implementation process is embodied in the embodiment of the method, which is not described herein.
In a third aspect, an embodiment of the present disclosure further provides an electronic device corresponding to the method for detecting a political new media propagation force provided in the foregoing embodiment, so as to execute the method for detecting a political new media propagation force.
Referring to fig. 6, a schematic diagram of an electronic device according to some embodiments of the application is shown. As shown in fig. 6, the electronic device includes: a processor 600, a memory 601, a bus 602 and a communication interface 603, the processor 600, the communication interface 603 and the memory 601 being connected by the bus 602; the memory 601 stores a computer program executable on the processor 600, and the processor 600 executes the method for detecting the propagation force of political new media according to any of the above embodiments of the present application when the computer program is executed.
The memory 601 may include a high-speed random access memory (RAM: random Access Memory), and may further include a non-volatile memory (non-volatile memory), such as at least one disk memory. The communication connection between the system network element and at least one other network element is implemented via at least one communication interface 603 (which may be wired or wireless), the internet, a wide area network, a local network, a metropolitan area network, etc. may be used.
Bus 602 may be an ISA bus, a PCI bus, an EISA bus, or the like. The buses may be divided into address buses, data buses, control buses, etc. The memory 601 is configured to store a program, the processor 600 executes the program after receiving an execution instruction, and the method for detecting a political new media propagation force disclosed in any of the foregoing embodiments of the present application may be applied to the processor 600 or implemented by the processor 600.
The processor 600 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the methods described above may be performed by integrated logic circuitry in hardware or instructions in software in processor 600. The processor 600 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but may also be a Digital Signal Processor (DSP), application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 601 and the processor 600 reads the information in the memory 601 and performs the steps of the method described above in combination with its hardware.
The electronic equipment provided by the embodiment of the application and the political new media transmission force detection method provided by the embodiment of the application have the same beneficial effects as the method adopted, operated or realized by the electronic equipment based on the same inventive concept.
In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium corresponding to the method for detecting a political new media transmission force provided in the foregoing embodiment, referring to fig. 7, the computer readable storage medium is shown as an optical disc 700, on which a computer program (i.e. a program product) is stored, where the computer program, when executed by a processor, performs the method for detecting a political new media transmission force provided in any of the foregoing embodiments.
It should be noted that examples of the computer readable storage medium may also include, but are not limited to, a phase change memory (PRAM), a Static Random Access Memory (SRAM), a Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a flash memory, or other optical or magnetic storage medium, which will not be described in detail herein.
The computer readable storage medium provided by the above embodiment of the present application has the same advantages as the method adopted, operated or implemented by the application program stored in the computer readable storage medium, because the same inventive concept is adopted as the method for detecting the political new media propagation force provided by the embodiment of the present application.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (9)
1. A method for detecting the propagation force of a political new medium, comprising the steps of:
Acquiring new media data of a government department to be detected;
Inputting the new media data into a pre-trained political news classification model to obtain political new media data;
Calculating the propagation force of the political new media according to the political new media data, comprising: calculating the transmission force of the political new media according to the release number, the reading number, the influence, the praise number, the vermicelli number, the comment number and the political correlation number in the new media data; the number of articles related to the political field is obtained according to the political news classification model; respectively calculating a WeChat propagation force, a microblog propagation force, a tremble sound propagation force and a present head bar propagation force to obtain the WeChat propagation force, the microblog propagation force, the tremble sound propagation force and the present head bar propagation force, and then calculating the political new media propagation force of the government department according to preset weights;
NCI＝w1*N1+w2*N2+w3*N3+w4*N4
Wherein NCI represents the propagation force of a new political medium of a certain department, N 1 represents the propagation force of a WeChat, N 2 represents the propagation force of a microblog, N 3 represents the propagation force of a tremble sound, N 4 represents the propagation force of a present head, and w 1、w2、w3、w4 represents a preset weight;
The WeChat propagation force N 1 is calculated as follows:
Wherein N max represents the maximum value of the article number of all departments within the statistical range, N 111 represents the article number of the current department to be detected, F max represents the maximum value of the article number of all departments within the statistical range, N 112 represents the article number of the current department to be detected, Representing the maximum value of the average reading of all departments in the statistical range, N 121 representing the average reading of the current department to be detected, R max representing the maximum value of the highest reading of all departments in the statistical range, N 122 representing the highest reading of the current department to be detected, I max representing the maximum value of the influence of all departments in the statistical range, N 123 representing the influence of the current department to be detected,/>Representing the maximum value of the average praise numbers of all departments in the statistical range, N 131 representing the average praise number of the current department to be detected, Z max representing the maximum value of the highest praise number of all departments in the statistical range, N 132 representing the highest praise number of the current department to be detected, C max representing the maximum value of the political news correlation numbers of all departments in the statistical range, and N 141 representing the political correlation numbers of the current department to be detected;
Wherein, the selection of the weight satisfies the normalization condition:
Where θ i represents the weight.
2. The method of claim 1, wherein after obtaining new media data for the government agency to be detected, further comprising:
word segmentation and vectorization are carried out on the text of the new media data, and vectorized text data are obtained;
and extracting keywords in the vectorized text data by adopting a TF-IDF algorithm.
3. The method of claim 1, further comprising, prior to entering the new media data into the pre-trained political news classification model:
Acquiring a news corpus in the marked political field;
Word segmentation and vectorization are carried out on the text data in the news corpus to obtain vectorized text data;
Extracting keywords of the vectorized text data by adopting a TF-IDF algorithm;
and training the political news classification model according to the keywords.
4. The method of claim 1, wherein the political relevance count is derived from a number of articles of political new media obtained from a political news classification model.
5. The method of claim 1, wherein calculating the influence comprises:
acquiring articles to be calculated in the political new media data and all news articles acquired in a preset time period;
calculating the similarity between the articles to be calculated and all the collected news articles;
Adding news articles with similarity larger than a preset threshold value into a similar article list;
and obtaining the influence value according to the number of the articles in the similar article list.
6. A device for detecting the propagation force of a political new medium, comprising:
the acquisition module is used for acquiring new media data of the government departments to be detected;
The classification module is used for inputting the new media data into a pre-trained political news classification model to obtain political new media data;
A calculation module, configured to calculate a propagation force of the political new media according to the political new media data, including: calculating the transmission force of the political new media according to the release number, the reading number, the influence, the praise number, the vermicelli number, the comment number and the political correlation number in the new media data; the number of articles related to the political field is obtained according to the political news classification model; respectively calculating a WeChat propagation force, a microblog propagation force, a tremble sound propagation force and a present head bar propagation force to obtain the WeChat propagation force, the microblog propagation force, the tremble sound propagation force and the present head bar propagation force, and then calculating the political new media propagation force of the government department according to preset weights;
NCI＝w1*N1+w2*N2+w3*N3+w4*N4
Wherein NCI represents the propagation force of a new political medium of a certain department, N 1 represents the propagation force of a WeChat, N 2 represents the propagation force of a microblog, N 3 represents the propagation force of a tremble sound, N 4 represents the propagation force of a present head, and w 1、w2、w3、w4 represents a preset weight;
The WeChat propagation force N 1 is calculated as follows:
Wherein N max represents the maximum value of the article number of all departments within the statistical range, N 111 represents the article number of the current department to be detected, F max represents the maximum value of the article number of all departments within the statistical range, N 112 represents the article number of the current department to be detected, Representing the maximum value of the average reading of all departments in the statistical range, N 121 representing the average reading of the current department to be detected, R max representing the maximum value of the highest reading of all departments in the statistical range, N 122 representing the highest reading of the current department to be detected, I max representing the maximum value of the influence of all departments in the statistical range, N 123 representing the influence of the current department to be detected,/>Representing the maximum value of the average praise numbers of all departments in the statistical range, N 131 representing the average praise number of the current department to be detected, Z max representing the maximum value of the highest praise number of all departments in the statistical range, N 132 representing the highest praise number of the current department to be detected, C max representing the maximum value of the political news correlation numbers of all departments in the statistical range, and N 141 representing the political correlation numbers of the current department to be detected;
Wherein, the selection of the weight satisfies the normalization condition:
Where θ i represents the weight.
7. The apparatus as recited in claim 6, further comprising:
The preprocessing module is used for carrying out word segmentation and vectorization on the text of the new media data to obtain vectorized text data;
and extracting keywords in the vectorized text data by adopting a TF-IDF algorithm.
8. A political new media transmission force detection apparatus comprising a processor and a memory storing program instructions, the processor being configured, when executing the program instructions, to perform the political new media transmission force detection method of any of claims 1 to 5.
9. A computer readable medium having stored thereon computer readable instructions executable by a processor to implement a method of detecting political new media propagation forces as defined in any one of claims 1 to 5.
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