CN115132369B

CN115132369B - Information propagation analysis method and system based on social media mimicry environment modeling

Info

Publication number: CN115132369B
Application number: CN202210645833.8A
Authority: CN
Inventors: 殷复莲; 唐鑫夷; 梁彤宇; 胡芷溦; 陈卓; 吴杰玲; 王锦霞; 潘妍妍; 佘雨薇; 夏欣雨
Original assignee: Communication University of China
Current assignee: Communication University of China
Priority date: 2022-06-09
Filing date: 2022-06-09
Publication date: 2023-05-02
Anticipated expiration: 2042-06-09
Also published as: CN115132369A

Abstract

The invention provides aAccording to the information propagation analysis method and system based on social media mimicry environment modeling, through constructing an SFI-PE model under the visual threshold of an information ecological system and considering an M-SFI-PE model under the action of a third party intervention of factors outside the system, two propagation links of social media platform information propagation are analyzed, interaction and dynamic influence among people and environment in the social media information ecological system are comprehensively researched, and the general rule of the network information propagation ecological system under the current new media environment can be summarized by combining parameter sensitivity analysis on information propagation indexes. And aiming at the experiments of promoting propagation of positive events and inhibiting propagation of negative events, an effective information propagation intervention method of third party intervention is obtained, and the average forwarding probability p generated after the susceptible user contacts information through social relationship is determined _F Is the key point of entry and the optimal direction of the intervention of the third party.

Description

Information propagation analysis method and system based on social media mimicry environment modeling

Technical Field

The invention relates to the technical field of information propagation analysis based on model construction, in particular to an information propagation analysis method and system based on social media mimicry environment modeling.

Background

In the definition of common sense, "propagation" is often considered as a process of information transfer between people, and thus, analysis and interpretation of information propagation by taking the intrinsic characteristics of things as an access point is a common research path, but such a common research path usually ignores the importance of information environment in the process of information transfer. The people are social organisms with social properties, the communication interaction between people and people needs to be generated in a certain information space, and a community of specific topics is formed according to different information contents, so that the behaviors of the people are subjectively influenced by the people, and the people are influenced by other individuals and the environment.

Based on this, american scholars Davenport T H first put forward the concept of "information ecology", which introduces the natural ecosystem studied by ecology into human society. The information ecological system refers to that information, information people and information ecological environment form a unified whole in a certain information space due to interaction of information exchange relationship, and form basic elements of information ecology together. In particular, information ecosystems include people, human organizations, communities, and their information environments, constantly undergoing information exchange and information recycling processes. The information ecological environment refers to other information people, information content, information technology, information space time, information system and the like which directly influence the survival, life and development of the information people.

In the field of mass propagation, the information ecological environment corresponds to a certain extent to the "mimicry environment" of the propagology concept proposed by the us famous politician waltt lipman. The mimicry environment is an information environment formed by mass-transmitted activities, which is not a mirror-like reproduction of a real environment, but rather is an environment that is presented to people after the mass-transmitted medium has been selected and processed, re-structured, through object-like events or information. In the modern society of the new media information age, the social media platform is mainly embodied as a topic community constructed by the Internet technology, such as a hot search, a super-voice of a Chinese New wave microblog, a trend of foreign platforms Twitter and Facebook, and the like. On a social media platform, a topic community of a specific heat bargained event provides 'soil' for the transmission and discussion of the event, gathers related information of multi-element main body publishing, forwarding and commenting, and realizes comprehensive ordering of information presentation by means of an algorithm mechanism of the platform, and an information set capable of reflecting most of user viewpoint attitudes or having dominant transmission force is screened out and can be regarded as the centralized embodiment of the specific heat bargained event on the specific platform.

Currently, the most commonly used research approach for information dissemination of social networks is the infectious disease model. The infectious disease model is proposed to study the problems of the transmission speed, the spatial range, the transmission path, the kinetic mechanism and the like of infectious diseases so as to guide the effective prevention and control of the infectious diseases. Classical infectious disease model SI (dominant-selected) and SIR (dominant-selected-Recovered) models mainly consider the transmission path of human infectious people. Later, a learner further introduced a module of environment to study the infection mechanism of the environment with pathogen to human.

Because the infectious mechanism of infectious diseases has similarities to the human information transmission paradigm, infectious disease models have been generalized to the field of social science research. In modern society, how to formulate effective network information transmission intervention strategies and improve self-regulation capacity of network ecological environment has become an important subject which is most urgent in social management and needs to be faced for a long time. Therefore, a learner puts forward a model for spreading and controlling infectious diseases after major emergencies, divides the masses into easily affected people, latent people, affected people and cured people, and researches the dynamic influence of third party intervention on different people. However, in a network society constructed by a social media platform, information transmission is generated not only by means of the social relationship between people, but also by means of a mimicry environment of a topic community, the information transmission behavior of people can be directly influenced, and the infectious disease model is just like infectious diseases can infect people through natural environments. Therefore, a novel network social construction mechanism is needed to be established, and a network information propagation analysis method based on the social media mimicry environment is further researched.

Disclosure of Invention

In view of the above problems, the invention researches the information propagation dynamics mode of social media from a brand new information ecological view, adopts the research thought of a system theory, and regards a social media platform as an information ecological system formed by integrating information, information people and information environment (namely, mimicry environment). And (3) researching the inherent interaction and dynamic influence between people and the environment in the network information transmission process by constructing an information transmission dynamics model, and summarizing the network information transmission rule. Based on the social media information ecosystem, the influence of all links of the external factor intervention system on the network information transmission is comprehensively considered, an information transmission dynamics model based on the social media mimicry environment is constructed, an effective network information transmission analysis strategy is formulated in an assisted mode, and the self-regulation capacity of the network ecological environment is improved.

According to one aspect of the present invention, there is provided a method for modeling an information propagation dynamics model based on a social media mimicry environment, wherein in the information propagation dynamics model,

in the development process of a heat event, the information propagation generated by means of social relations and the information propagation generated by means of a mimicry environment are assumed to be two parallel propagation links which are not mutually interfered and have different information metabolism modes; the information is transmitted in a closed and stable environment, the total number N of the environment is unchanged, N is divided into three groups of a susceptible state S, a forwarding state F and an immune state I, and the state of each individual in the group is unique at any moment; the meaning represented by each state in the information propagation dynamics model is as follows:

Susceptible state S: the individual in this state has not yet contacted the information, but in the future may contact and be affected by the information, resulting in a forwarding behavior;

forwarding state F: individuals in this state develop a forwarding behavior that has the ability to infect individuals in a susceptible state to forward information;

immune status I: the population in this state consists of two parts:

the individuals in the forwarding state exceed the active exposure period and no longer have the ability to affect other people, thereby being converted into an immune state; the method comprises the steps of,

an individual in a susceptible state, after contacting the information, is directly converted into an immune state because the information is subjectively not interested;

defining B (t) as an effective propagation information set in a t-moment mimicry environment, defining S (t), F (t) and I (t) as the number of transient groups in each state at t moment, and S (t) +F (t) +I (t) =N;

the differential equation of the information propagation dynamics model is as follows:

/>

wherein ,β_F The average contact rate of information can be contacted for the susceptible users through social relations; beta _B Average contact rate, p, of information accessible to a susceptible user through a mimicry environment _F For the average forwarding probability generated after the susceptible user contacts the information through the social relationship, p _B Alpha is the average forwarding probability generated after the susceptible user contacts the information through the mimicry environment _F Alpha for forwarding the average immune rate of a user that becomes inactive during the information dissemination _B For the average metabolism rate of information in the mimicry environment which becomes inactive in the information propagation process, gamma is the average exposure probability of information forwarded by a forwarding user, which is screened and presented in the mimicry environment through a platform algorithm mechanism.

According to another aspect of the invention, an information propagation analysis method based on social media mimicry environment modeling is provided, and the information propagation analysis method is used for performing information propagation analysis by using an information propagation dynamics model based on social media mimicry environment, wherein the information propagation dynamics model based on social media mimicry environment is an information propagation dynamics model based on social media mimicry environment, which is established by using the information propagation dynamics model modeling method based on social media mimicry environment; the method comprises the following steps:

carrying out information propagation analysis by using the information propagation dynamics model based on the social media mimicry environment; and carrying out information propagation analysis by combining a third party intervention information propagation dynamics model based on the social media mimicry environment on the basis of the information propagation dynamics model based on the social media mimicry environment;

The method for carrying out information propagation analysis by utilizing the information propagation dynamics model based on the social media mimicry environment comprises the following steps:

searching and collecting original information data of an event to be analyzed on a preset social platform based on a preset search engine, wherein the information data comprises a user forwarding text and forwarding time;

preprocessing the original information data to obtain a user forwarding text without noise redundancy and forwarding time under each piece of information;

carrying out data fitting and parameter estimation on data driving by using the user forwarding text without noise redundancy and forwarding time; estimating parameters of the information propagation dynamics model based on the social media mimicry environment and the total number of the initial susceptible people by using a least square method;

and determining information propagation index data of the event to be analyzed through the information propagation dynamics model based on the social media mimicry environment based on the parameters and the total number of the initial susceptible people.

According to still another aspect of the present invention, there is provided an information propagation analysis system based on social media mimicry environment modeling, including:

the modeling unit is used for creating an information propagation dynamics model based on the social media mimicry environment and a third party intervention information propagation dynamics model based on the social media mimicry environment; the information propagation dynamics model based on the social media mimicry environment is an information propagation dynamics model based on the social media mimicry environment, which is built by using the information propagation dynamics model modeling method based on the social media mimicry environment in

claim

1 or 2;

The information propagation analysis unit comprises a primary analysis unit and an intervention analysis unit, wherein the primary analysis unit is used for carrying out information propagation analysis by utilizing the information propagation dynamics model based on the social media mimicry environment; the intervention analysis unit is used for carrying out information propagation analysis by combining a third party intervention information propagation dynamics model based on the social media mimicry environment on the basis of the information propagation dynamics model based on the social media mimicry environment;

wherein the primary analysis unit comprises:

the information acquisition unit is used for searching and acquiring original information data of an event to be analyzed on a preset social platform based on a preset search engine, wherein the information data comprises a user forwarding text and forwarding time;

the information preprocessing unit is used for preprocessing the original information data to obtain a user forwarding text without noise redundancy and forwarding time under each piece of information;

the data fitting and parameter estimating unit is used for carrying out data fitting and parameter estimation on data driving by using the user forwarding text without noise redundancy and forwarding time; estimating parameters of the information propagation dynamics model based on the social media mimicry environment and the total number of the initial susceptible people by using a least square method;

And the index data determining unit is used for determining information transmission index data of the event to be analyzed through the information transmission dynamics model based on the social media mimicry environment based on the parameters and the total number of the initial susceptible population.

The invention constructs an SFI-PE model under the visual threshold of the information ecosystem, further considers the influence of each link of a third party intervention system on the information transmission on the basis of the SFI-PE model, constructs an M-SFI-PE model under the action of the third party intervention taking the factors outside the system into consideration, and comprehensively researches the interaction and dynamic influence between people in the social media information ecosystem and between people and the environment by deeply analyzing two transmission links of the information transmission of the social media platform, namely the social relationship and the mimicry environment which are constructed by relying on a platform mechanism, and summarizes the parameter sensitivity analysis on the information transmission index so as to be more suitable for the current new mediaThe network information propagates the general laws of the ecosystem in the environment. And aiming at the experiments of promoting propagation of positive events and inhibiting propagation of negative events, an effective information propagation intervention method of third party intervention is obtained, and the average forwarding probability p generated after the susceptible user contacts information through social relationship is determined _F Is the key point of entry and the optimal direction of the intervention of the third party.

To the accomplishment of the foregoing and related ends, one or more aspects of the invention comprise the features hereinafter fully described and particularly pointed out in the claims. The following description and the annexed drawings set forth in detail certain illustrative aspects of the invention. These aspects are indicative, however, of but a few of the various ways in which the principles of the invention may be employed. Furthermore, the invention is intended to include all such aspects and their equivalents.

Drawings

Other objects and results of the present invention will become more apparent and readily appreciated by reference to the following description and claims in conjunction with the accompanying drawings and a more complete understanding of the invention. In the drawings:

FIGS. 1a and 1b are schematic diagrams of SFI-PE models M-SFI-PE models, respectively, according to the present invention;

FIG. 2 is a flow chart of an information propagation analysis method based on social media mimicry environment modeling according to the present invention;

FIG. 3 is a graph of information propagation for certain positive events and certain negative events according to the present invention;

FIG. 4 shows the index F under the influence of various parameters according to the invention _max 、C _s 、

PRCC results of (c);

FIG. 5 is a graph showing information propagation index fluctuations caused by single parameter variations according to the present invention;

FIG. 6 is a schematic diagram of crowd evolution with a third party facilitating positive event information dissemination in accordance with the present invention;

fig. 7 is a schematic diagram of crowd evolution with negative event information propagation suppressed by a third party according to the present invention.

The same reference numerals will be used throughout the drawings to refer to similar or corresponding features or functions.

Detailed Description

In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of one or more embodiments. It may be evident, however, that such embodiment(s) may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to facilitate describing one or more embodiments.

Through the deep analysis of the operation logic and algorithm mechanism of the social media platform, the information, information people and information ecological environment, namely the 'topic community' mimicry environment constructed by the social media platform Internet technology, is an integral organic body. Therefore, the invention can be more suitable for the current network information spreading ecological system by reconstructing the traditional infectious disease model and considering the dynamic influence of the intervention of a third party on different crowds, crowds and environments, so as to provide the following information spreading analysis method and system based on social media mimicry environment modeling.

Specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

According to the information propagation analysis method and system based on social media mimicry environment modeling, an information propagation dynamics model based on the social media mimicry environment is firstly required to be constructed. Therefore, the invention firstly provides an information propagation dynamics model modeling method based on social media mimicry environment.

The internet technology of social media platforms determines that users can access information through two approaches: 1. the friend relationship is a social relationship constructed by platform mechanisms such as vermicelli, attention and the like, and a user can directly touch information forwarded by friends on a personal homepage; 2. the topic communities are mimicry environments (hereinafter simply referred to as "mimicry environments") constructed by means of platform mechanisms such as "trending", "keyword searching", and the like, and users can enter relevant pages of specific topics or events so as to contact information of other people in the environment.

Meanwhile, as the algorithm of the social media platform needs to comprehensively consider the factors such as information release time, information content quality and heat, information release account activity, whether a publisher is authenticated by real names or not and the like, and screen and filter the information based on the factors and sequentially display the information in a topic community, the algorithm mechanism determines that the information contacted by a user through the topic community is not all the information in the topic, but is selected, processed and re-structured and presented to the masses.

Based on the current situation of the social media platform mechanism, the invention firstly needs to construct an information propagation dynamics model based on the social media mimicry environment shown in fig. 1, namely a persistence-forwarding-image-environment (SFI-PE) model. In this SFI-PE model, it is assumed that during the development of a heat event, the information propagation by means of social relations and the information propagation by means of mimicry are two parallel propagation links with different information metabolism patterns, which do not interfere with each other. For example, a user may have lost the ability to propagate among friends, but the information they forward may still contact other users in the "topic community".

All parameters involved in the SFI-PE model and their corresponding interpretation are shown in Table 1 below.

TABLE 1SFI-PE model parameter Table

For a particular network hotmatter event for a social media platform, it is assumed that the information dissemination is performed in a closed stable space, the headcount (N) of which is constant. In the model, S (t), F (t) and I (t) are defined as the instantaneous population number in each state at the moment t, and S (t) +F (t) +I (t) =N. The population in N is subdivided into three states, namely a susceptible state (S), a forwarding state (F), an immune state (I), and it is assumed that at any moment in time each individual in the population is in a unique state. The meaning represented by each state in the SFI-PE model is as follows:

1. Susceptible state (S): the individual in this state has not yet contacted the information, but in the future it is possible to contact and be influenced by the information, resulting in a forwarding behaviour.

2. Forwarding state (F): individuals in this state develop a forwarding behavior with the ability to infect individuals in a susceptible state to forward information.

3. Immune status (I): the population in the state mainly consists of two parts, wherein the individual in the forwarding state exceeds the active exposure period and has no ability to influence other people, so that the individual is converted into an immune state; and secondly, the individuals in the susceptible state are directly converted into an immune state after contacting the information because the information is not interested subjectively.

Meanwhile, B (t) is defined as an effective propagation information set in a t-moment mimicry environment in the SFI-PE model, and the SFI-PE model can act on other users through a propagation link of a topic community.

Whereas the SFI-PE model is constructed by differential equations, the main path is as follows:

in the dynamic system constructed by the model, for an information propagation link depending on social relations, an active forwarding user has average influence beta per unit time _F The ability of N users, at the current time t, the proportion of the susceptible users in the total crowd is S (t)/N, so the users in forwarding state will contact beta per unit time _F S (t) names of susceptible users, and these users will have p _F Generates a forwarding behavior. Thus, there is beta _F The susceptible users of the S (t) F (t) name are affected by active forwarding users. Wherein p is _F β _F S (t) F (t) users generate forwarding actions to be converted into forwarding states (F), (1-p) _F )β _F S (t) F (t) users are not sensitive to informationThe interest is converted into an immune state (I) and does not participate in information transmission. Over time, there is alpha _F F (t) the user no longer has the ability to affect other users, turning to immune state (I). Under the algorithm mechanism of the social media platform, the forwarding information of one forwarding user has the average exposure probability of gamma, so that gamma F (t) pieces of information are screened and presented in a mimicry environment per unit time. Thus similarly, for an information dissemination link that relies on a mimicry environment, active information in a mimicry environment will contact beta per unit time _B S (t) names of susceptible users, and these users will have p _B Generates a forwarding behavior. Thus, there is p _B β _B S (t) B (t) users generate forwarding behaviors to be converted into forwarding states (F), (1-p) _B )β _B S (t) B (t) users are not involved in information propagation because the users are not interested in the information and are converted into an immune state (I). Over time, α _B B (t) pieces of information no longer have the ability to affect other users, reaching the end of the information lifecycle.

In the SFI-PE model, a social media information propagation system under the ecological view threshold of the information is constructed. Next, the invention further considers the intervention effect of the external third party measures on the system to construct a third party intervention information propagation dynamics model based on the social media mimicry environment, namely a macro-controlled susceptible-forwarding-immune pseudo-environment (M-SFI-PE) model.

The M-SFI-PE model is constructed on the basis of the SFI-PE model by considering the influence of each link of the third party intervention system on information transmission, and the model architecture of the M-SFI-PE model is shown in figure 1 b. Since the average immune rate of a forwarding user and the average metabolic rate of information in a mimicry environment are often closely related to the user's personal usage habits, third party intervention is not considered from this perspective. In this M-SFI-PE model, the original assumption premise of the SFI-PE model is still maintained.

The new parameters and corresponding explanations related to the M-SFI-PE model are shown in the following Table 2, wherein, beta _F 、β _B 、p _F 、p _B The definition of parameters such as γ is shown in table 1.

TABLE 2M-SFI-PE model parameter Table

It is noted that the effect of the intervention of the third party is related to the sign of the above parameters, i.e. the intervention of the third party may either suppress the propagation of the information in reverse or promote the propagation of the information in forward direction. In view of the fact that the M-SFI-PE model is built on the basis of the SFI-PE model, the main process is as follows:

similar to the SFI-PE model, after considering the impact of adding third party intervention, it can be considered: in the dynamic system provided by the invention, for an information propagation link depending on social relations, one active forwarding user has an average influence (beta) _F -β _MF ) The ability of N users, at the current time t, the proportion of the susceptible users in the total population is S (t)/N, so that users in forwarding state will contact (beta) _F -β _MF ) S (t) is susceptible to users, and these users will have (p) _F -p _MF ) Generates a forwarding behavior.

Thus, there is (beta) _F -β _MF ) The susceptible users of the S (t) F (t) name are affected by active forwarding users. Wherein, (p) _F -p _MF )(β _F -β _MF ) S (t) F (t) users generate forwarding actions to be converted into forwarding states (F), (1-p) _F +p _MF )(β _F -β _MF ) S (t) F (t) users are not involved in information propagation because the users are not interested in the information and are converted into an immune state (I). Over time, there is alpha _F F (t) the user no longer has the ability to affect other users, turning to immune state (I). Under the algorithm mechanism of the social media platform, the forwarding information of one forwarding user has (gamma-gamma) _M ) So there will be (gamma-gamma) per unit time _M ) F (t) piece of informationIs screened for presentation in a mimicry environment.

Thus, similarly, for an information dissemination link that relies on a mimicry environment, active information in a mimicry environment will contact (beta _B -β _MB ) S (t) is susceptible to users, and these users will have (p) _B -p _MB ) Generates a forwarding behavior. Thus, there is (p) _B -p _MB )(β _B - β _MB ) S (t) B (t) users generate forwarding behaviors to be converted into forwarding states (F), (1-p) _B +p _MB )(β _B - β _MB ) S (t) B (t) users are not involved in information propagation because the users are not interested in the information and are converted into an immune state (I). Over time, α _B B (t) pieces of information no longer have the ability to affect other users, reaching the end of the information lifecycle.

And after the model is built, the information propagation analysis can be performed by applying the model. The information propagation analysis method based on social media mimicry environment modeling is used for performing information propagation analysis on the basis of the information propagation dynamics model based on social media mimicry environment modeling.

FIG. 2 illustrates a method flow diagram for information dissemination analysis based on social media mimicry environment modeling in accordance with an embodiment of the present invention.

As shown in fig. 2, the method for information propagation analysis based on social media mimicry environment modeling provided in the present embodiment includes the following steps:

s210: carrying out information propagation analysis by using an information propagation dynamics model based on social media mimicry environment;

s220: and carrying out information propagation analysis by combining a third party intervention information propagation dynamics model based on the social media mimicry environment on the basis of the information propagation dynamics model based on the social media mimicry environment.

Wherein, step S210 further includes:

s211: searching and collecting original information data of an event to be analyzed on a preset social platform based on a preset search engine, wherein the information data comprises a user forwarding text and forwarding time; the event to be analyzed is a specific example of information, and the text forwarded by the user is the text forwarded by all forwarding individuals in the propagation group.

S212: preprocessing original information data to obtain users without noise redundancy, forwarding text and forwarding time under each piece of information;

s213: carrying out data fitting and parameter estimation on data driving by using the user forwarding text without noise redundancy and forwarding time; estimating parameters of the information propagation dynamics model based on the social media mimicry environment and the total number of the initial susceptible people by using a least square method;

S214: and determining information propagation index data of the event to be analyzed through the information propagation dynamics model based on the social media mimicry environment based on the parameters and the total number of the initial susceptible people.

In order to perform qualitative and quantitative analysis on information propagation and provide evaluation standards for an information propagation intervention method, the invention constructs an information propagation index for expressing the development condition of information in the information propagation process. The information transmission index includes information transmission reproducible number

Peak information propagation, final information propagation scale, and time of climax information propagation.

Wherein the information is propagated reproducible number

For determining whether the information is likely to be disseminated in bursts; the information transmission peak value is used for measuring the highest point of the public opinion burst; the final information propagation scale is used for measuring the range to which the information propagation can be finally spread; the information propagation high tide time is used for measuring the speed of the public opinion outbreak to the highest point. The method for acquiring information propagation index data in the present invention will be described in further detail.

Information propagation reproducible number

In the case of a model of an infectious disease,

for a substantially reproducible number, the average number of persons who were secondarily infected with one patient during the average period of infection is expressed. Similarly, in the SFI-PE model of the present invention, the basic reproducible numbers are extended to represent the average value of secondary forwarders caused by valid information in each forwarding user and each mimicry environment under the condition that external force intervention is excluded and all users are easily affected, and the average value of secondary forwarders can be obtained by- >

It is determined whether the information is likely to be disseminated in bursts.

At the beginning of information distribution, if the forwarding amount per unit time is decreased, public opinion will not burst. That is, in the foregoing formula (1), when S (t) =s ₀ When meeting the following conditions

Then the public opinion presents a tendency to decay, namely:

because the forwarding group number F (t) and the effective propagation information number B (t) in the social media mimicry environment are non-negative numbers, the method meets the following conditions:

F(t)≥B(t)>0 (4)

thus, the above formula (3) can be converted into

Thereby obtaining the information transmission reproducible number

wherein ,

meaning that the total forwarding group number shows a descending trend at the beginning of information release, and public opinion never bursts; />

It means that the total number of forwarding groups is exponentially increased at the beginning of information release, public opinion is necessarily exploded, and

the larger the public opinion burst, the faster.

Information transmission peak value, information transmission final scale, and information transmission climax time:

in the information propagation process, the accumulated forwarding quantity can be used for representing the propagation scale of the information. Further deriving from the above differential equation (1), a differential equation of the cumulative forwarding amount shown in the following equation (7) can be derived:

in order to fit the real data to the model, the invention estimates model parameters and the total number of the initial susceptible population by a least square method. Setting the parameter vector to Θ= (β) _F ,β _B ,p _F ,p _B ,α _F ,α _B ,γ,S ₀ ) By f _C (k, Θ) represents an analog value of the cumulative transfer amount at time k, and C is used _k Showing the true value of the time accumulated forwarding volume. Thus, a least squares LS error function can be obtained:

where LS is the sum of squares of the residuals, k=0, 1,2, …, T representing the sampling time. In the course of data fitting, the parameters need to meet the following conditions: 1-p _F ≥0、1-p _B ≥0、1-γ≥0。

The accumulated forwarding quantity of the event is any timeThe overall trend of the curve of the inter-change is firstly rapidly increased, then stably increased, and finally tends to be stable; the instantaneous forwarding amount of an event is a bell-shaped curve that increases with time, rising and then falling. In the present invention, the maximum value of a bell-shaped curve representing the instantaneous transfer amount is defined as the information propagation peak value (F _max ) The method is used for measuring the highest point of public opinion burst, namely the intensity of information transmission; at the same time, the maximum value of the cumulative forwarding amount C (t) is defined as the final scale (C _s ) For measuring the range to which the information propagation can ultimately be spread, i.e. the breadth of the information propagation.

In the present invention, the time to reach the peak of information propagation is defined as the time of high tide of information propagation

And the method is used for measuring the speed of the public opinion burst to the highest point.

The invention will be described in more detail below in connection with a research embodiment of the network information dissemination and procedure using the invention.

According to the invention, the recently-occurring information event is widely searched on the social platform, and a heat theme is selected on the basis of a preset social platform (the Chinese new wave microblog platform is adopted in the embodiment) so as to analyze the information propagation dynamics mode of social media from a brand new information ecological visual threshold, and the intrinsic interaction between people and the environment in the system is researched.

In order to further study the influence of all links of an external factor third party intervention system on information transmission later, the invention screens out a positive typical event and a negative typical event under the heat theme, and collects real information data containing forwarding text and accurate forwarding time under target information. Since the user mainly browses information in the physiological active period and stops browsing information in the sleep time, in this embodiment, the collected original information data needs to be preprocessed first, and the original information data is filtered to avoid information stagnation caused by physiological requirements. After preprocessing, a noise-free instant forwarding time point and forwarding text under each piece of information can be obtained, so that the accumulated forwarding quantity is calculated.

In acquiring the cumulative forwarding amount, the present embodiment performs acquisition by adding the number of users corresponding to the instantaneous time point in a certain time range. Here, the start time is set to 0, and the sampling frequency is set to 1 hour. Then, in order to obtain the best fitting result, the embodiment uses the real accumulated forwarding quantity of the user to drive data, and utilizes the least square method to estimate SFI-PE model parameters and the total number of initial susceptible people.

Fig. 3 shows information propagation curves for a specific positive event (left) and a specific negative event (right) according to an embodiment of the present invention. As shown in fig. 3, the asterisks indicate the true cumulative forwarding amount, and the solid line indicates the simulated value of the cumulative forwarding amount calculated by the SFI-PE model. From the numerical simulation result, the fitting curve of the SFI-PE model almost coincides with the real data points, so that the SFI-PE model can fully represent the information transmission process, and the effectiveness of the model is verified. In this embodiment, the estimated values of the positive event parameters and the estimated values of the negative event parameters obtained by driving the data with the actual forwarding amounts of the actual users are shown in the following tables 3 and 4, respectively.

TABLE 3 front event parameter results for SFI-PE model

TABLE 4 negative event parameter results for SFI-PE model

As can be seen from the two tables above, the initial susceptible population S of positive events ₀ =8000, initial susceptible number S of negative events ₀ =80000. For positive and negative events, the information forwarded by the forwarding user is screened by a platform algorithm mechanism to be presented in a mimicry environment, the average exposure probability gamma is smaller and approximately equivalent, the positive event is gamma=0.1000, the negative event is gamma= 0.1040, and the information forwarded by the forwarding user is shown to have few turns under the influence of the platform algorithm mechanism The information forwarded by the sender may be filtered for presentation in a mimicry environment. Average contact rate beta related to network structure _F and β_B Stable in a very small magnitude, because the user can directly contact information on a personal homepage through social relationship, and the user is required to actively enter pages such as a topic community and the like through mimicry environment contact information, beta is obtained _F Numerical ratio beta of (2) _B Larger. Average forwarding probability p generated after susceptible users contact information through social relationship _F Less than the average forwarding probability p generated after the information is contacted through the social relationship _B Indicating that the susceptible user is more willing to forward the information after contacting the information through the mimicry environment. Average immune rate alpha of a user _F Usually related to its own behavior law, whereas the average metabolic rate alpha of the information _B Generally related to the heat of the event and the attention of society, it can be seen that the average metabolic rate alpha of the information _B Significantly less than the average immune rate alpha of the user _F The method shows that the exposure period of information activity in a mimicry environment is longer, and the time for affecting information propagation is longer. Using the model value fitting results, we can calculate specific values of the event information propagation index, and the index values of the positive event and the negative event are specifically shown in tables 5 and 6 below.

TABLE 5 information propagation index results for specific frontal events

TABLE 6 information dissemination index results for specific negative events

It can be seen that the information of the positive event and the negative event is propagated and reproducible

At the beginning of information release indicating two types of events, the total number of forwarding groups is exponentially increased, so that public opinion is bound to burst. Since two specific events studied in this embodiment are social security problems surrounding information concerns, the information propagation index results thereof are compared in this embodiment. First, the information of the negative event propagates peak F _max Information propagation peak F much greater than the frontal event _max Indicating that the negative event has a greater information propagation intensity during the information propagation than the positive event. Second, the time required to reach the peak of the information propagation of the negative event +.>

Greater than the time required to reach the peak of information propagation for the positive event

Indicating that the positive event first reached the highest point of the public opinion burst. Finally, the information of the negative event propagates to the final scale C _s Information propagation final size C much larger than the frontal event _s The information that illustrates this negative event is spread out more widely.

In the SFI-PE model of the invention, an initially susceptible population S ₀ Is a variable to be estimated, which can determine the change of the index, so that in this embodiment, it is regarded as a parameter to be analyzed together with other parameters preset by the SFI-PE model. In order to deeply explore the influence of parameter variation on indexes, the embodiment adopts a bias execution correlation coefficient (PRCCs) method, and the method uses 1000 groups of samples to carry out repeated experiments within the boundary range of input parameters, and finally gives the average sensitivity result of each parameter. The result of this method is between-1 and 1, indicating that the input parameters have a strong positive effect on the information dissemination index if the result is close to 1 (the overall trend of the scatter plot is to the right), and indicating that the input parameters have a strong negative effect on the information dissemination index if the result is close to-1 (the overall trend of the scatter plot is to the left). To explore the parameters (beta) _B 、β _F 、p _B 、p _F 、γ、α _B 、α _F 、S ₀ ) For information propagation peak value F _max Information dissemination final Scale C _s And information propagation reproducible number

In this embodiment, the PRCC result is intuitively represented by using a histogram and a scatter diagram having a correspondence relationship, taking the positive event as an example.

FIG. 4 shows an index F under the influence of a diversity parameter according to an embodiment of the invention _max 、C _s 、

Schematic of PRCC results of (c). In the invention, the peak value F is propagated by information _max As can be seen from FIG. 4, the average contact rate β at which a susceptible user can contact information through social relationships characterizes the intensity of information propagation _F Average forwarding probability p generated after susceptible users contact information through mimicry environment _B Average forwarding probability p generated after susceptible users contact information through social relationship _F Average immunization rate alpha of forwarding users _F And an initial total number of susceptible people S ₀ Forwarding peak value F to index information _max The significance level of (2) is much less than 0.01, indicating that the correlation is very significant. Wherein p is _F and S₀ For F _max Has strong positive correlation effect, beta _F For F _max Has a generally positive correlation effect, p _B For F _max Has weak positive correlation effect, alpha _F For F _max Has a strong negative correlation effect.

Information dissemination final Scale C _s Characterizing the breadth of the information propagation. As can be seen from fig. 4, the average forwarding probability p generated after the susceptible user contacts the information through the mimicry environment _B Average forwarding probability p generated after susceptible users contact information through social relationship _F Average metabolic rate alpha at which information in a mimicry environment becomes inactive during information dissemination _B Average immunization rate alpha of forwarding users _F And an initial total number of susceptible people S ₀ Forwarding and spreading the index information to final scale C _s The significance level of (2) is much less than 0.01, indicating that the correlation is very significant. Wherein p is _F and S₀ For C _s Has strong positive correlation effect, p _B For C _s Has a general positive correlation effect, alpha _B and α_F For C _s With weak negative correlation effects.

Information propagation reproducible number

Characterizing whether the public opinion will burst. As can be seen from FIG. 4, the average contact rate β at which a susceptible user can contact information through a mimicry environment _B Average contact rate beta at which a susceptible user can contact information through social relationships _F Average forwarding probability p generated after susceptible users contact information through mimicry environment _B Average forwarding probability p generated after susceptible users contact information through social relationship _F Average immunization rate alpha of forwarding users _F And an initial total number of susceptible people S ₀ Reproducible number of index information transmission->

The significance level of (2) is much less than 0.01, indicating that the correlation is very significant. Wherein p is _F and S₀ For R ₀ Has strong positive correlation effect, beta _B 、β _F and p_B For->

Has a general positive correlation effect, alpha _F For->

Has a strong negative correlation effect.

The above results indicate that the parameter p is increased _F 、S ₀ 、β _F 、p _B While reducing alpha _F The method is beneficial to improving the intensity of information transmission, and conversely, is beneficial to reducing the intensity of information transmission; increasing parameter p _F 、S ₀ and p_B While reducing alpha _B and α_F The method is beneficial to enlarging the scale of information transmission, and conversely is beneficial to reducing the scale of information transmission. From the perspective of whether the public opinion explodes, the parameter p is increased _F 、S ₀ 、β _B 、β _F and p_B While reducing alpha _F Is beneficial to promoting the public opinion outbreak and is beneficial to inhibiting the public opinion outbreak on the contrary.

Taking beta into account _F 、β _B 、p _F 、p _B Is an important parameter affecting information propagation, and it is necessary to further study their specific effects on variables (instantaneous forwarding quantity F (t), cumulative forwarding quantity C (t)) characterizing propagation trend and effective propagation information set B (t) in a mimicry environment. Fig. 5 shows a schematic diagram of information propagation index fluctuation caused by single parameter variation according to an embodiment of the present invention.

As shown in FIG. 5, from the parameter fitting result of the SFI-PE model, the average contact rate beta of the information which is accessible to the susceptible user through the social relationship can be known _F ＝9.4000×10 ^-4 Average contact rate beta at which a susceptible user can contact information through a mimicry environment _B ＝4.1900×10 ^-4 Average forwarding probability p generated after susceptible users contact information through social relationship _F = 0.4610, the average forwarding probability p generated after a susceptible user has contacted information through a mimicry environment _B =0.8500, the information forwarded by the forwarding user is screened and presented in the mimicry environment through a platform algorithm mechanism, the average exposure probability gamma=0.1000, and the average immune rate alpha of the forwarding user becoming inactive in the information propagation process _F = 1.5274, average metabolic rate α at which information in a mimicry environment becomes inactive during information dissemination _B = 0.1080, total number of initial susceptible population S ₀ =8000. By varying the parameter beta respectively _F 、β _B 、 p _F 、p _B While keeping other parameters unchanged, the information propagation index change caused by single parameter change can be studied.

By observing (a) and (b) in fig. 5, it can be found that, for the curve of the cumulative forwarding amount C (t), the average contact rate β at which the information can be contacted by the susceptible user through social relationship _F The reduction of (c) slows down the curve early-stage acceleration, but because of the prolonged duration of the B (t) peak of the set of valid propagation information in the mimicry environment, the curveLater stage slowly walks high, so the information propagates to final scale C _s Instead, the average contact rate beta of information can be contacted by users with weak expansion and easy influence on anti-observation through mimicry environment _B The reduction of (2) has very weak influence on the early stage of the curve, and the final scale C of the later stage of the curve and information transmission _s Has obvious inhibiting effect. For the curve of the instantaneous forwarding quantity F (t), the parameter β _F and β_B Is reduced by the peak value F of information propagation _max To some extent decline, beta _F The effect of (2) is relatively more pronounced. For the curves of the set of valid propagation information B (t) in a mimicry environment, the parameter β _F The reduction of (2) reduces the peak value of effective propagation information in the mimicry environment, and the arrival time of the peak value is shifted, and beta _B The effect of (2) is very weak.

By observing (C) and (d) in fig. 5, it can be found that the average forwarding probability p generated after the susceptible user contacts the information through the social relationship for the curve of the cumulative forwarding amount C (t) _F The influence of the reduction of the curve on the curve is the whole process, the curve speed is obviously slowed down, and the final scale C of information transmission _s Obviously reduced, average forwarding probability p generated after anti-observation and easy-to-influence user contacts information through mimicry environment _B The effect on the curve is mainly focused on the later stage and the effect is relatively weak. For the curves of the instantaneous forwarding quantity F (t) and the set of valid propagation information B (t) in the mimicry environment, the parameter p _F Is reduced by the information propagation peak value F _max And significant decrease in peak value of effective propagation information in mimicry environment, and p _B The effect of (2) is very weak.

Through the analysis, the influence of the parameters on the information transmission index can be more intuitively understood, so that the following conclusion can be obtained:

first, parameters (β) related to the propagation link of the mimicry environment _B ，p _B ) For information propagation peak value F _max The effect of (2) is not significant; second, parameters (β) related to the propagation link of the mimicry environment _B ，p _B ) The influence on the information propagation scale is mainly concentrated in the later stage; third, andsocial relationship-a propagation link-related parameter (beta) _F ，p _F ) For information propagation peak value F _max The effect of (2) is more pronounced; fourth, parameter β related to the propagation link of social relationship _F The influence on the information propagation scale is mainly concentrated in the early stage, and the final propagation scale C is enlarged in the later stage _s And p _F The impact on the information propagation scale is global.

Therefore, the four parameters can be intervened to influence the trend of information propagation, and the intensity and the breadth of the information propagation are regulated, so that the effective macroscopic regulation and control of the information propagation are realized. In addition, the average exposure probability gamma of the information forwarded by the forwarding user and screened and presented in the mimicry environment through the platform algorithm mechanism reflects the action of the social media platform algorithm mechanism, and the third party intervention research on information propagation aiming at the parameter gamma has important significance at present.

Taking the two typical events as basic scenes, taking parameter values estimated by the SFI-PE model as a comparison group, and carrying out moderate assumption on newly added parameters of the M-SFI-PE model according to the parameter sensitivity analysis result of the information transmission index. By using a control variable method, the embodiment designs 5 new scenes to form an experimental group so as to simulate the influence of different third party intervention means on the crowd state and behavior in the social media information ecosystem and examine the direction which should be selected by the third party intervention information transmission.

First, intervention studies on third parties to facilitate positive event information dissemination are as follows:

as can be seen from the parameter fitting result of the SFI-PE model, the average contact rate beta of the susceptible user to the information can be reached through the social relationship _F ＝9.4000×10 ^-4 Average contact rate beta at which a susceptible user can contact information through a mimicry environment _B ＝4.1900×10 ^-4 Average forwarding probability p generated after susceptible users contact information through social relationship _F = 0.4610, the average forwarding probability p generated after a susceptible user has contacted information through a mimicry environment _B =0.8500, and the information forwarded by the forwarding user is processed by the platform algorithm mechanismScreening the average exposure probability gamma=0.1000 presented in a mimicry environment, forwarding the average immune rate alpha at which the user becomes inactive during the information dissemination process _F = 1.5274, average metabolic rate α at which information in a mimicry environment becomes inactive during information dissemination _B = 0.1080, total number of initial susceptible population S ₀ =8000. On this basis, the specific setting of the new parameters of the M-SFI-PE model for promoting the propagation of the positive event information by the third party is shown in the following table 7.

TABLE 7M-SFI-PE model addition parameter settings to third facilitate front event information propagation

/>

The parameter values in Table 7 are each set to 1/10 of the parameter being intervened, and the negative sign preceding the value reflects the contribution of third party intervention to information dissemination. For example, in setting up new parameters of M-SFI-PE model, beta _MF ＝-9.4000×10 ^-5 Take the value of beta _F ＝9.4000×10 ^-4 The negative "-" indicates promotion of 1/10 of (C). And carrying out experiments considering third party intervention to promote information transmission on the event, and carrying out simulation analysis on the M-SFI-PE model by utilizing Matlab, so as to obtain the change conditions of the number of people S (t) in the susceptible state, the number of people F (t) in the forwarding state, the number of people I (t) in the immune state and the effective transmission information set B (t) in the mimicry environment in each scene.

Fig. 6 is a crowd evolution schematic of a third party facilitating positive event information dissemination according to an embodiment of the invention. As shown in FIG. 6, in the simulation results for promoting the propagation of the frontal event information, the control group and the experimental group (a) beta are respectively in the order from top to bottom as indicated by the line marks in FIG. 6 _MF ＝-9.4000×10 ^-5 Scenario, experimental group (b) beta _MB ＝-4.1900×10 ^-5 Scenario, experiment group (c) p _MF Scene of = -0.0461, experimental group (d) p _MB Scene of = -0.0850, experimental group (e) γ _M Scene = -0.0100. As can be seen by examining fig. 6 (a) and (b), in the vulnerable state (S) the transition to forwarding state (F) In this link, only experimental group (c) helps to accelerate the decline in the number of susceptible people S (t). The experimental groups (a) and (c) can improve the peak value of the crowd quantity F (t) in the forwarding state, so that the peak point of information transmission arrives more quickly. While other intervention methods are relatively inefficient. From an examination of FIGS. 6 (b) and (c), it can be seen that the transition from forwarding state (F) to immune state (I) is due to the fact that the average immune rate α that the forwarding user becomes inactive during the information dissemination is not _F Direct intervention is performed at p _MF Under the influence of the above, the number of people I (t) in the immune state is increased synchronously, but the final immune crowd size is not obviously enlarged, so that the proportion of effective information transmission groups is improved. From an examination of the graph (d), it can be seen that all five intervention modes contribute to an increase in the amount of information available in the mimicry environment, wherein by adjusting the parameter p _MF and γ_M The mode of the method has the best intervention effect, the peak value of the effective information of the obtained mimicry environment is the highest, and the descending rate is obviously slowed down.

The specific values of the dry prognosis information propagation index can then be calculated using the new model parameter values, thereby evaluating these five intervention modes. The information propagation index values of the positive event dry prognosis are specifically shown in table 8 below.

TABLE 8 information propagation index results for specific positive events after intervention

By comparing the index results in table 8, the following conclusions can be drawn:

1. if it is desired to increase the intensity of the information propagation, from the information propagation peak value F _max From the index point of view, by adjusting the parameter p _MF The intervention effect is optimal, and the intervention mode is selected and ordered as p _MF >β _MF >p _MB >β _MB ≈γ _M The method comprises the steps of carrying out a first treatment on the surface of the 2. If it is desired to expand the breadth of information propagation, the final scale C is propagated from the information _s From the index point of view, by adjusting the parameter p _MF Is optimal in intervention effect in the way of interventionThe mode is selected and ordered as p _MF >p _MB >β _MB ≈γ _M By adjusting the parameter beta _MF In a way that will instead reduce the information propagation final size C _s The method comprises the steps of carrying out a first treatment on the surface of the 3. If it is desired to reach the highest point of public opinion faster, the climax time t is propagated from the information _Fmax From the viewpoint, the order of p is selected on the intervention mode _MF ≈β _MF >β _MB ≈p _MB ≈γ _M 。

In combination, the average forwarding probability intervention rate p generated by adjusting the contact of a third party to information of a susceptible user through social relationship _MF The method has the best intervention effect in promoting the information propagation of the positive event, and the average forwarding probability p generated after the susceptible user contacts the information through the social relationship _F Is a key parameter that facilitates the propagation of positive event information. Therefore, for information transmission of positive events, the effective direction of third party intervention information transmission is to improve the forwarding willingness of the susceptible users after the users contact the information through social relations.

Second, intervention studies on third parties to suppress negative event information propagation are as follows:

as can be seen from the parameter fitting result of the SFI-PE model, the average contact rate beta of the susceptible user to the information can be reached through the social relationship _F ＝1.3400×10 ^-4 Average contact rate beta at which a susceptible user can contact information through a mimicry environment _B ＝1.200×10 ^-4 Average forwarding probability p generated after susceptible users contact information through social relationship _F = 0.2200, the average forwarding probability p generated after a susceptible user has contacted information through a mimicry environment _B = 0.7290, the information forwarded by the forwarding user is screened and presented in the mimicry environment through a platform algorithm mechanism, the average exposure probability gamma=0.0104, and the average immune rate alpha of the forwarding user which becomes inactive in the information propagation process _F = 1.3384, average metabolic rate α at which information in a mimicry environment becomes inactive during information dissemination _B = 0.1740, total number of initial susceptible population S ₀ =80000. On the basis of thatSpecific settings of the M-SFI-PE model add-on parameters for the third party to suppress negative event information propagation are shown in table 9 below.

TABLE 9M-SFI-PE model addition parameter settings for third party suppression of negative event information propagation

The parameter values in Table 9 are all set to 1/10 of the parameter to be intervened, and the positive sign before the value reflects the inhibition of information propagation by third party intervention. For example, in setting up new parameters of M-SFI-PE model, beta _MF ＝1.3400×10 ^-5 Take the value of beta _F ＝1.3400×10 ^-4 The positive sign "+" indicates inhibition 1/10 of the above. And carrying out experiments considering third party intervention to inhibit information transmission on the event, and carrying out simulation analysis on the M-SFI-PE model by utilizing Matlab, so as to obtain the change conditions of the number of people S (t) in the susceptible state, the number of people F (t) in the forwarding state, the number of people I (t) in the immune state and the effective transmission information set B (t) in the mimicry environment in each scene.

Fig. 7 is a crowd evolution schematic diagram of suppressing negative event information propagation by a third party according to an embodiment of the invention. As shown in FIG. 7, in the simulation results of the third party suppressing the propagation of negative event information, the control group and the experimental group (a) beta are respectively in the order from top to bottom according to the line marks in FIG. 7 _MF ＝1.3400×10 ^-5 Scenario, experimental group (b) beta _MB ＝1.200×10 ^-5 Scenario, experiment group (c) p _MF Scenario= 0.0220, experimental group (d) p _MB Scenario = 0.0729, experimental group (e) γ _M A scenario=0.0104. As can be seen from the observation of (a) and (b) in fig. 7, in the step of converting the susceptible state (S) into the forwarding state (F), the experimental groups (a) and (c) help to slow down the fall of the population number S (t) in the susceptible state and reduce the peak value of the population number F (t) in the forwarding state, so that the peak point of information transmission is delayed, and the significance p of the intervention effect is achieved _MF >β _MF . As can be seen from an examination of (b) and (c) in FIG. 7, the state of immunity is shifted from the forwarding state (F)I) Conversion is a link, since the average immune rate alpha which is inactive to the forwarding user in the information transmission process is not reached _F Direct intervention is performed at p _MF and β_MF Under the influence of the above, the number of people I (t) in the immune state is reduced, but the final immune population size is not obviously reduced, so that the proportion of effective information transmission population is reduced. As can be seen by examining fig. 7 (d), all five intervention modes help to reduce the amount of effective information in the mimicry environment, by adjusting the parameter γ _M The mode of the method has the best intervention effect, the peak value of the effective information of the obtained mimicry environment is obviously reduced, and then the adjustment parameter p is adopted _MF Again, the adjustment parameter p _MB and β_MB By adjusting the parameter beta _MF The intervention effect is least obvious.

The specific values of the dry prognosis information propagation index can then be calculated using the new model parameter values, thereby evaluating these five intervention modes. The information dissemination index values for the negative event dry prognosis are specifically shown in table 10 below.

TABLE 10 information propagation index results for specific negative events after intervention

By comparing the index results in table 10, the following conclusions can be drawn:

1. If it is desired to reduce the intensity of the information propagation, from the information propagation peak value F _max From the index point of view, by adjusting the parameter p _MF The intervention effect is optimal, and the intervention mode is selected and ordered as p _MF >β _MF >p _MB >β _MB ≈γ _M The method comprises the steps of carrying out a first treatment on the surface of the 2. If it is desired to reduce the breadth of the information propagation, the final scale C is propagated from the information _s From the index point of view, by adjusting the parameter p _MF The intervention effect is optimal, and the intervention mode is selected and ordered as p _MF >p _MB >β _MB ≈γ _M By adjusting the parameter beta _MF In a way that instead increases the ultimate size of the information propagationC _s The method comprises the steps of carrying out a first treatment on the surface of the 3. If it is desired to delay the time to reach the highest point of public opinion, the time of high tide is propagated from the information

From the viewpoint, the order of p is selected on the intervention mode _MF ≈β _MF >β _MB ≈p _MB ≈γ _M 。

In terms of the intervention of the third party to inhibit the information propagation of the negative event, the conclusion obtained through the embodiment and the information propagation experimental result of the intervention of the third party to promote the proving event are mutually proved. That is, in combination, by adjusting the average forwarding probability intervention rate p generated by a third party after a susceptible user contacts information through social relationships _MF The method has the best intervention effect in the aspect of inhibiting the information propagation of negative events, and the average forwarding probability p generated after the susceptible user contacts the information through the social relationship _F Is a key parameter to inhibit the propagation of negative event information. Therefore, for information propagation of negative events, the effective direction of third party intervention information propagation is to reduce forwarding will of susceptible users after contacting the information through social relations.

With the above-described embodiments, the validity of two models (the SFI-PE model and the M-SFI-PE model) can be verified. On one hand, the invention adopts the research thought of the system theory to construct the SFI-PE model under the visual threshold of the information ecological system. Based on the novel network society construction mechanism, two propagation links of social media platform information propagation are deeply analyzed, namely, social relations constructed by platform mechanisms such as vermicelli, attention and the like and mimicry environments constructed by platform mechanisms such as hot, keyword search and the like, interaction and dynamic influence among people and between people and environments in a social media information ecological system are comprehensively researched, and the general rule of the network information propagation ecological system which is more suitable for the current new media environment is summarized by combining parameter sensitivity analysis on information propagation indexes. On the other hand, the invention further considers the influence of each link of the third party intervention system on information transmission on the basis of the SFI-PE model, and constructs the third party trunk considering the factors outside the system M-SFI-PE model under pre-action. In the above embodiment, by focusing on the hot spot problem, taking two typical events as examples, a control experiment is developed for the promotion propagation of the positive event and the inhibition propagation of the negative event, so that an effective information propagation intervention method and conclusion of the intervention of a third party are obtained. In combination, whether promoting the propagation of positive events or inhibiting the propagation of negative events, the average forwarding probability p generated after the susceptible user contacts the information through social relationship _F Is the key point of entry and the optimal direction of the intervention of the third party.

The experimental conclusion is helpful to construct an efficient information propagation intervention method. If the aim of promoting the information propagation of positive events is to make the average forwarding probability p generated after the information is contacted by the susceptible users through social relations by the intervention of a third party _F For example, more emotion value is added on the establishment of a medium issue, the actual scene of the specific implementation of epidemic situation prevention and control and social security measures is shown by the method of image-text combination and disciplinary description, and emotion resonance is enhanced by taking we as a main language, and the epidemic situation prevention and control and social security measures are more participated in through a call and other modes. If the information propagation of the negative event is to be inhibited, the comprehensive optimal scheme is that the third party intervenes to enable the average forwarding probability p generated after the susceptible user contacts the information through the social relationship _F For example, for negative false information, a pop-up window may be provided to the false information to let the contacted person know the distorting nature of the information; for a true negative event, the third party can indicate that the third party knows about the related situation by adding the related issues, and because the spontaneous forwarding of the information is often to draw attention in this case, a jump link can be set on the page of the original negative information, so that a person who touches the information again can click directly to jump to the information page responded by the third party. Therefore, the information transmission intervention method constructed by the invention can provide strategy support for information management so as to assist in maintaining social stability and network security.

Corresponding to the information propagation analysis method based on social media mimicry environment modeling, the invention further provides an information propagation analysis system based on social media mimicry environment modeling, which comprises a modeling unit and an information propagation analysis unit.

The modeling unit is used for creating an information propagation dynamics model based on the social media mimicry environment and a third party intervention information propagation dynamics model based on the social media mimicry environment; the information propagation dynamics model based on the social media mimicry environment is an information propagation dynamics model based on the social media mimicry environment, which is established by using the information propagation dynamics model modeling method based on the social media mimicry environment; the information propagation analysis unit comprises a primary analysis unit and an intervention analysis unit, wherein the primary analysis unit is used for carrying out information propagation analysis by utilizing the information propagation dynamics model based on the social media mimicry environment; the intervention analysis unit is used for carrying out information propagation analysis by combining a third party intervention information propagation dynamics model based on the social media mimicry environment on the basis of the information propagation dynamics model based on the social media mimicry environment;

Further, the primary analysis unit includes:

The information propagation analysis system based on social media mimicry environment modeling corresponds to the information propagation analysis method based on social media mimicry environment modeling, and specific implementation manner of the system can refer to the description of the information propagation analysis method based on social media mimicry environment modeling, and the description is not repeated here.

The hybrid information propagation dynamics model based on individual emotion interactions and the method for information propagation analysis using the same according to the present invention are described above by way of example with reference to the accompanying drawings. However, it will be appreciated by those skilled in the art that various modifications may be made to the hybrid information dissemination dynamics model based on individual emotion interactions and the method of using the model for information dissemination analysis as set forth in the foregoing description without departing from the scope of the invention. Accordingly, the scope of the invention should be determined from the following claims.

Claims

1. An information propagation analysis method based on social media mimicry environment modeling is used for carrying out information propagation analysis by utilizing an information propagation dynamics model based on social media mimicry environment, wherein in the information propagation dynamics model,

immune status I: the population in this state consists of two parts:

wherein ,β_F The average contact rate of information can be contacted for the susceptible users through social relations; beta _B Average contact rate, p, of information accessible to a susceptible user through a mimicry environment _F For the average forwarding probability generated after the susceptible user contacts the information through the social relationship, p _B Alpha is the average forwarding probability generated after the susceptible user contacts the information through the mimicry environment _F Alpha for forwarding the average immune rate of a user that becomes inactive during the information dissemination _B The average metabolism rate of information in the mimicry environment, which becomes inactive in the information propagation process, is the average exposure probability of information forwarded by a forwarding user, which is screened and presented in the mimicry environment through a platform algorithm mechanism;

on the basis of the information propagation dynamics model, the influence of a third-party intervention system on information propagation is increased, a third-party intervention information propagation dynamics model based on a social media mimicry environment is constructed, wherein the assumption premise of the information propagation dynamics model based on the social media mimicry environment is maintained, and a differential equation of the third-party intervention information propagation dynamics model based on the social media mimicry environment is as follows:

wherein ,β_MF For third party pair beta _F Intervention coefficient, beta _MB For third party pair beta _B Intervention coefficient, p _MF For the third party p _F Intervention coefficient, p _MB For the third party p _B Intervention coefficient of gamma _M An intervention coefficient of a third party for gamma;

the method comprises the following steps:

carrying out information propagation analysis by using the information propagation dynamics model based on the social media mimicry environment; the method comprises the steps of,

carrying out information propagation analysis by combining a third party intervention information propagation dynamics model based on the social media mimicry environment on the basis of the information propagation dynamics model based on the social media mimicry environment;

2. The information dissemination analysis method based on social media mimicry environment modeling in claim 1, wherein the information dissemination index data includes information dissemination renewable numbers

Information transmission peak value, information transmission final scale and information transmission climax time; wherein,

the information spreading reproducible number

For determining whether the information is likely to be disseminated in bursts;

the information transmission peak value is used for measuring the highest point of public opinion outbreaks;

the final information propagation scale is used for measuring the range to which the information propagation can be finally propagated;

the information propagation high tide time is used for measuring the speed of the public opinion outbreak to the highest point.

3. The social media mimicry environment modeling based information propagation analysis method as claimed in claim 2, wherein the information propagation is renewable in number

The method comprises the following steps of:

at the beginning of information distribution, if the forwarding amount per unit time is decreased, the public opinion will not burst, and in the differential equation of the information propagation dynamics model, when S (t) =s ₀ When meeting the following conditions

The public opinion presents a tendency to decay, at which point,

because the forwarding population quantity F (t) and the effective propagation information quantity B (t) in the mimicry environment are non-negative numbers, the method meets the following conditions: f (t) > B (t) >0, thereby obtaining:

to obtain the information spreading reproducible number

wherein ,

It means that at the beginning of information release, the total number of forwarding groups is exponentially increased, public opinion is necessarily exploded, and +.>

The larger the public opinion burst, the faster.

4. The method for information dissemination analysis based on social media mimicry environment modeling, as defined in claim 3, wherein in estimating parameters of the social media mimicry environment-based information dissemination dynamics model and the initial total number of susceptible people using a least squares method,

setting the parameter vector to Θ= (β) _F ,β _B ,p _F ,p _B ,α _F ,α _B ,γ,S ₀ ) By f _C (k, Θ) represents an analog value of the cumulative transfer amount at time k, and C is used _k The true value of the time-of-day cumulative forwarding quantity is shown, thereby yielding a least squares LS error function:

where LS is the sum of squares of the residuals, k=0, 1,2, …, T represents the sampling time;

in the process of data fitting, parameters of the information propagation dynamics model based on the social media mimicry environment need to meet the following conditions: 1-p _F ≥0、1-p _B ≥0、1-γ≥0。

5. The information dissemination analysis method based on social media mimicry environment modeling, as defined in claim 4, wherein the physical quantities used to characterize the information dissemination scale include a cumulative amount of forwarding and an instantaneous amount of forwarding, wherein a differential equation of the cumulative amount of forwarding is:

wherein the accumulated forwarding amount is a curve changing along with time, the overall trend is rapidly increased, then is stably increased, and finally tends to be stable; the instantaneous forwarding quantity is a bell-shaped curve which increases with time and then descends; defining the maximum value of the bell-shaped curve representing the instantaneous forwarding quantity as the information propagation peak value F _max At the same time, the maximum value of the accumulated forwarding quantity C (t) is defined as the information propagation final scale C _s The time to reach the information propagation peak is defined as the information propagation high tide time

6. An information propagation analysis system based on social media mimicry environment modeling, comprising:

the modeling unit is used for creating an information propagation dynamics model based on the social media mimicry environment and a third party intervention information propagation dynamics model based on the social media mimicry environment; wherein, in the information propagation dynamics model,

immune status I: the population in this state consists of two parts:

wherein the primary analysis unit comprises:

7. The social media mimicry environment modeling based information dissemination analysis system in claim 6, wherein the information dissemination index data includes information dissemination renewable numbers

the information spreading reproducible number

For determining whether the information is likely to be disseminated in bursts;

8. The social media mimicking environment modeling based information propagation analysis system as claimed in claim 7, wherein the physical quantities used to characterize the information propagation scale include a cumulative forwarding quantity and an instantaneous forwarding quantity, wherein a differential equation of the cumulative forwarding quantity is:

/>