CN110297984A

CN110297984A - Information transmission dynamics system, construction method, device and medium based on microblogging

Info

Publication number: CN110297984A
Application number: CN201910411316.2A
Authority: CN
Inventors: 殷复莲; 吴建宏; 邵雪莹; 吴佳乐; 王颜颜; 李思彤; 庞红玉
Original assignee: Communication University of China
Current assignee: Communication University of China
Priority date: 2019-05-17
Filing date: 2019-05-17
Publication date: 2019-10-01
Anticipated expiration: 2039-05-17
Also published as: CN110297984B

Abstract

The present invention provides a kind of information transmission dynamics system, construction method, device and medium based on microblogging, comprising: the state of user in microblogging is divided into sensitization, forwarding state or/and read state and immune state；Information transmission dynamics model is constructed according to the variation of User Status；The following number of users in each state and variation tendency are predicted according to above- mentioned information transmission dynamics model by the monitoring of accumulative transfer amount or/and accumulative amount of reading to microblogging.The forwarding or/and reading information that above system, construction method, device and medium externally announce microblogging are analyzed, and are predicted public sentiment.

Description

Information transmission dynamics system, construction method, device and medium based on microblogging

Technical field

The present invention relates to the dynamical system constructing technology fields that public sentiment is propagated, more particularly, to a kind of based on microblogging Information transmission dynamics system, construction method, device and medium.

Background technique

By the end of the year 2018, Sina weibo monthly possesses 4.46 hundred million any active ues (the 32% of Zhan Zhongguo total population), is The maximum social media of state.By taking " one, Chongqing bus falls river event " as an example, microblogging produces about 10,000 forwardings in 24 hours User and 100,000,000 reads user, can illustrate that the information of microblogging is propagated rapidly and influenced significant.Since the network of each complexity has There is specific topological characteristic, some articles investigate relevant characteristic measure.Due to the difference of culture, Chinese Sina Microblogging has some different features in terms of content and information propagation.In China, Sina weibo is that an important public opinion passes Platform is broadcast, therefore, the research of micro-blog information mechanism of transmission is increasingly becoming current research hotspot.

The information communication process of blog (Microblog) is the network reality that can be interacted by " concern " behavior connection Existing.The user for implementing concern behavior is known as " bean vermicelli ", i.e. follower, and the user as person of being concerned that the bean vermicelli is of interest. The diffusion model of basic structure based on blog, information mainly includes three categories: model based on communication process, based on influencing The model of power and based on forwarding factor model.When focus to be conceived to the dynamic change of information communication process, based on dynamic The Epidemic Model of mechanics has obtained extensive research, although this field has numerous scholars and expands extensive work, Due to the difference of cultural traits, microblogging has some specific characteristics for being different from other social medias, and dynamic communication characteristic is also Up for further studying.

Have some information transmission dynamics systems based on classical Epidemic Model at present, crowd is divided into and is not overlapped Three classes: not in contact with the people (know-nothing) of rumour, actively the people (disseminator) that spreads rumors and the people no longer to spread rumors are (immune Person).It is the refinement of propagation characteristic to first thinking that classical model improves, it is contemplated that the incomplete reading of user is gone For researcher proposes the blog-easy infection-has infected-propagated for information and deletes (Microblog-Susceptible- Infected-Removed, Mb-SIR) model.In addition, there is also two kinds of improved models, the first kind considers disseminator not Always active situation, the second class consider the know-nothing of information to spreading rumors and uninterested possibility, and by pair Twitter platform data progress numerical simulation obtains the more classical gossip propagation model of model constructed by it closer to actual conditions Conclusion.Some scholars consider the shadow that the intimate degree between the communication effect of immune and members of society propagates information It rings, proposes improved gossip propagation model (modified rumor spreading model, SIRe).Numerical simulation result Show that the higher group of cohesion is easier to possess the disseminator of higher proportion, and disperse the immune concentration that is better than and be immunized, can make The maximum rate of disseminator is reduced.In addition, another crowd of scholar has also set up and has retouched to distinguish the effects of different methods of refuting a rumour The gossip propagation model of user browsing behavior feature is stated, analysis is the result shows that the propagation of information has certain timeliness.

It is the research that new module is added and improves communication process to another thinking that classical propagation model improves, It is exactly other than know-nothing, disseminator and immune, it is also contemplated that other states propagated.It especially incorporates with infectiousness Bean vermicelli quantity, the research verified using truthful data to revised model reflects the information characteristics of Sina weibo And propagation characteristic.Researcher constructs a dynamic model (Susceptible users (S), super-spreaders (A), normal spreaders (I) recovered users (R), SAIR) it is existing to characterize the super propagation pushed away in literary information propagation As.In addition, super-spreader's state proposes an improved model (Ignorant spreaders (know-nothing), Super- Spreaders (super-spreader), Stiflers model, ISJR), this demonstrate super-spreader how in the network of blog plus Fast information is propagated, and informational influence power is expanded.Meanwhile there are many more other models by the recovery status merging of the user of different platform Increase is new plate, including Chinese microblogging, Japan Mixi and Facebook.

In the research above to all kinds of blog propagation characteristics and transmission structure, scholars mostly concentrate on focus of attention On the information communication theory of broad sense, the difference and connection of different data propagation characteristic are not accounted for, is only carried out with single-dimensional data real Example verifying, without combining the expansion of true available data to combine the research of actual conditions multi-angle.

Summary of the invention

In view of the above problems, the present invention provides forwarding that a kind of pair of microblogging is externally announced or/and reading information is analyzed, Information transmission dynamics system, construction method, device and the medium based on microblogging that public sentiment is predicted.

According to an aspect of the present invention, a kind of information transmission dynamics system constituting method based on microblogging is provided, comprising:

The state of user in microblogging is divided into three kinds of sensitization, forwarding state and immune state states, wherein described easy Sense state indicates that user not yet contacts issuing microblog but has the state that microblogging is forwarded ability；The forwarding state indicates Have been carried out forwarding and in active state can be to the state that other people have an impact；The immune state indicates that user turns Lost after hair the ability of enlivening state or reading information after actively abandon forwarding microblogging state；

Information transmission dynamics model is constructed by following formula (1)-(4)

S ' (t)=- β S (t) F (t) (1)

F ' (t)=p₁βS(t)F(t)-α₁F(t) (2)

I ' (t)=(1-p₁)βS(t)F(t)+α₁F(t) (3)

E ' (t)=p₁βS(t)F(t) (4)

Wherein, t is time index, and β indicates the user in sensitization in the rate of the mean exposure information of microblogging, p₁ Indicate that the user in sensitization forwards the rate of microblogging, α₁Indicate that the user in forwarding state becomes sluggish speed Rate, S (t) are Susceptible population's quantity, and F (t) is forwarding crowd quantity, and I (t) is immune crowd's quantity, and E (t) is accumulative transfer amount, S ' (t), F ' (t), I ' (t) and E ' (t) are respectively the derivative of S (t), F (t), I (t) and E (t), and the derivative is greater than 0 expression and refers to It marks numerical value to increase, derivative indicates that index value reduces less than 0, and derivative, which is equal to 0, indicates that index value is constant；

By the monitoring to accumulative transfer amount according to above- mentioned information transmission dynamics model to following Susceptible population's quantity, turn Hair crowd quantity and immune crowd's quantity and its variation are predicted.

According to the second aspect of the invention, a kind of information transmission dynamics system constituting method based on microblogging is provided, comprising:

The state of user in microblogging is divided into three kinds of sensitization, read state and immune state states, wherein described easy Sense state indicates that user not yet contacts issuing microblog but has the state that microblogging carries out reading ability；The read state indicates Have been carried out reading and in active state can be to the state that other people have an impact；The immune state indicates that user reads The state for the ability of enlivening is lost after reading or has exceeded the state for reading validity period；

Information transmission dynamics model is constructed by following formula (8)-(11)

S ' (t)=- β S (t) R (t)+p₂α₂R(t) (8)

R ' (t)=β S (t) R (t)-α₂R(t) (9)

I ' (t)=(1-p₂)α₂R(t) (10)

E′₂(t)=β S (t) R (t) (11)

Wherein, R (t) is reading crowd quantity, E₂It (t) is accumulative amount of reading, p₂Indicate that reading user generates repeat reading The rate of behavior, α₂Indicate that user becomes sluggish rate, S ' (t), F ' (t), I ' (t) and E ' in read state₂(t) respectively For S (t), F (t), I (t) and E₂(t) derivative, the derivative, which is greater than 0, indicates that index value increases, and derivative indicates index less than 0 Numerical value reduces, and derivative, which is equal to 0, indicates that index value is constant；

By the monitoring to accumulative amount of reading according to above- mentioned information transmission dynamics model to following Susceptible population's quantity, read Reading crowd quantity and immune crowd's quantity and its variation are predicted.

According to the third aspect of the invention we, a kind of information transmission dynamics system based on microblogging is provided, comprising:

The state of user in microblogging is divided into sensitization, forwarding state and immune state three by User Status setting module Kind state, wherein the sensitization indicates that user not yet contacts issuing microblog but has the shape that microblogging is forwarded ability State；The forwarding state expression has been carried out forwarding and can be to the state that other people have an impact in active state；Institute It states the state for losing the ability of enlivening after immune state expression user forwards or actively abandons the shape of forwarding microblogging after reading information State；

Model construction module constructs information transmission dynamics model by following formula (1)-(4)

S ' (t)=- β S (t) F (t) (1)

F ' (t)=p₁βS(t)F(t)-α₁F(t) (2)

I ' (t)=(1-p₁)βS(t)F(t)+α₁F(t) (3)

E ' (t)=p₁βS(t)F(t) (4)

Prediction module, the information transmission dynamics constructed by the monitoring to accumulative transfer amount according to above-mentioned model construction module The quantity and variation of the user in different states of model prediction User Status setting module setting.

Preferably, the User Status setting module by the state of user in microblogging be divided into sensitization, read state and Three kinds of states of immune state, wherein the sensitization indicates that user not yet contacts issuing microblog but having microblogging reads The state of reading ability；The read state expression has been carried out reading and can have an impact to other people in active state State；The immune state loses the state for the ability of enlivening after indicating user's reading or has exceeded the shape for reading validity period State；

The model construction module constructs information transmission dynamics model by following formula (8)-(11)

S ' (t)=- β S (t) R (t)+p₂α₂R(t) (8)

R ' (t)=β S (t) R (t)-α₂R(t) (9)

I ' (t)=(1-p₂)α₂R(t) (10)

E′₂(t)=β S (t) R (t) (11)

The prediction module is propagated by the monitoring to accumulative amount of reading according to the information that above-mentioned model construction module constructs Dynamic model predicts the quantity and variation of the user in different states of User Status setting module setting.

In addition, the present invention also provides a kind of electronic device, including memory and processor, base is stored in the memory In the information transmission dynamics system construction procedures of microblogging, the information transmission dynamics system construction procedures based on microblogging are described The step of processor realizes the above-mentioned information transmission dynamics system constituting method based on microblogging when executing.

In addition, including in the computer readable storage medium the present invention also provides a kind of computer readable storage medium There are the information transmission dynamics system construction procedures based on microblogging, the information transmission dynamics system construction procedures quilt based on microblogging Processor execute when, realize the above-mentioned information transmission dynamics system constituting method based on microblogging the step of.

Above-mentioned information transmission dynamics system, construction method, device and medium based on microblogging is to micro-blog information mechanism of transmission It is studied, analyzes the general modfel of rich information dissemination mechanism, the forwarding externally announced microblogging, reading information are analyzed, Construct information transmission dynamics model, carry out public sentiment index building, in advance grasp Internet streaming action to and detect micro-blog information Propagate, the current development propagated in time to information is desk-top and change in future is judged and predicted, it is final realize public sentiment prediction and Early warning is of great significance to maintaining social stability, building a harmonious society.

Detailed description of the invention

Fig. 1 is the schematic diagram of information transmission dynamics model SFI of the present invention；

Fig. 2 is the composition block diagram of one embodiment of the information transmission dynamics system of the present invention based on microblogging；

Fig. 3 is the signal of the public sentiment index of one embodiment of the information transmission dynamics system of the present invention based on microblogging Figure；

Fig. 4 is that of the present invention the present invention is based on one embodiment of the construction method of the information transmission dynamics system of microblogging Flow chart；

Fig. 5 is the schematic diagram of information transmission dynamics model SRI of the present invention；

Fig. 6 is the composition block diagram of another embodiment of the information transmission dynamics system of the present invention based on microblogging；

Fig. 7 is showing for the public sentiment index of another embodiment of the information transmission dynamics system of the present invention based on microblogging It is intended to；

Fig. 8 is the schematic diagram of topological diagram of the present invention；

Fig. 9 is the quantity of each User Status in mono- specific embodiment of information transmission dynamics model SFI of the present invention Matched curve figure；

Figure 10 is the number in mono- specific embodiment of information transmission dynamics model SFI of the present invention according to preceding setting time It is predicted that accumulative transfer amount and forwarding crowd's quantity schematic diagram；

Figure 11 is the quantity of each User Status in mono- specific embodiment of information transmission dynamics model SRI of the present invention Matched curve figure；

Figure 12 is the number in mono- specific embodiment of information transmission dynamics model SRI of the present invention according to preceding setting time It is predicted that accumulative amount of reading and read crowd's quantity schematic diagram.

Specific embodiment

In the following description, for purposes of illustration, it in order to provide the comprehensive understanding to one or more embodiments, explains Many details are stated.It may be evident, however, that these embodiments can also be realized without these specific details. In other examples, one or more embodiments for ease of description, well known structure and equipment are shown in block form an.

Each embodiment according to the present invention is described in detail below with reference to accompanying drawings.

Single microblogging is the most basic unit for constituting micro-blog information and propagating, and has great research significance.

In one embodiment, it for the research of unit microblogging, defines for same microblogging, each user only forwards once Hypothesis.Compared with classical Epidemic Model, the information transmission dynamics model SFI of quasi- building more meets the characteristic of microblogging, Exactly consider when user checks a microblogging, has the choice of whether to forward the microblogging, i.e. whether user can continue information It propagates, if user's selection is forwarded, forwarding state will be entered, if after a certain time, user will no longer be forwarded, i.e., Switch to immune state, the information transmission dynamics model SFI for intending building is as shown in Figure 1.

Fig. 2 is the composition block diagram of one embodiment of the information transmission dynamics system the present invention is based on microblogging, such as Fig. 2 institute Show, the information transmission dynamics system includes:

First User Status setting module 1, by the state of user in microblogging be divided into sensitization (S), forwarding state (F) and Three kinds of states of immune state (I), wherein the sensitization indicates that user not yet contacts issuing microblog but has microblogging progress The state of transfer capability；The forwarding state expression has been carried out forwarding and can generate shadow to other people in active state Loud state；The immune state indicates the state for losing the ability of enlivening after user's forwarding or actively abandons turning after reading information Send out the state of microblogging；

First model construction module 2 constructs information transmission dynamics model by following formula (1)-(4)

S ' (t)=- β S (t) F (t) (1)

F ' (t)=p₁βS(t)F(t)-α₁F(t) (2)

I ' (t)=(1-p₁)βS(t)F(t)+α₁F(t) (3)

E ' (t)=p₁βS(t)F(t) (4)

First prediction module 3, the letter constructed by the monitoring to accumulative transfer amount according to above-mentioned first model construction module The quantity and variation of the user in different states of transmission dynamics model prediction the first User Status setting module setting are ceased, In, accumulative transfer amount E (t) is the available data that microblogging is externally announced.

Preferably, first prediction module 3 also predicts public sentiment by public sentiment index, as shown in figure 3, the public sentiment index Including the maximum public sentiment spread index of the first public sentiment explosive index, the first public sentiment propagation peak and first, first prediction module Include:

First public sentiment explosive index predicting unit predicts the first carriage according to the following formula (5) by initial Susceptible population's quantity Feelings explosive index, the first public sentiment explosive index indicate that the public sentiment outburst stage actively forwards the forwarding of customer impact to use by one The quantity at family also illustrates that the severity of public sentiment event outburst

Wherein :=indicate to be defined as,For the first public sentiment explosive index, S₀Indicate the number of users for being in sensitization Initial value, 1/ α₁It indicates active time, the time span of active period is defined as forwarding validity period, due to the definition to forwarding There are certain period of time divisions, so there is forwarding validity period when user is forwarded, enabling u, (s is the use in forwarding state Ss hour is passed through still in the quantity of the user of forwarding state, if with ratio, α in the unit time in family₁Forwarding state is left, ThenIt is availableI.e. the user in forwarding state is after ss unit time Still it is for the ratio of the user in forwarding stateNamely forwarding validity period is that average value is Exponential distribution；

Judge that the first public sentiment explosive index is greater than 1, is also equal to 1 less than 1；

IfShow to forward number of users decline, microblogging will be unable to further propagate；If Show to forward number of users that will exponentially increase；IfShow to forward number of users without change；

First public sentiment propagation peak predicting unit predicts the first carriage according to the following formula (6) by initial Susceptible population's quantity Feelings propagation peak F_max, user forwards behavior within the unit time when the first public sentiment propagation peak expression public sentiment event is broken out The maximum value of increment

Wherein,₀Indicate the initial value of the number of users in forwarding state；

First time predicting unit needs to know specific microblogging for the performance for understanding the microblogging ecosystem in global range Initial time, peak value explosion time and the end time of outburst increase forwarding number of users to the first public sentiment propagation peak first Initial time is broken out as the first public sentiment at the time of setting ratio, the first public sentiment outburst initial time is predicted；It will forwarding Peak value moment is broken out as the first public sentiment at the time of number of users increases to the first public sentiment propagation peak, peak value is broken out to the first public sentiment Moment is predicted；As the first public sentiment at the time of forwarding number of users is dropped to first public sentiment the second setting ratio of propagation peak Finish time is broken out, the first public sentiment outburst finish time is predicted；

First Speed predicting unit arrives the first public sentiment outburst initial time to characterize microblogging diffusion duration The forwarding number of users that first public sentiment breaks out the unit time of peak value moment breaks out speed as the first public sentiment, realizes to the first carriage The prediction of feelings outburst speed；By the forwarding of the unit time of the first public sentiment outburst peak value moment to the first public sentiment outburst finish time Number of users realizes the prediction to the first public sentiment decline rate as the first public sentiment decline rate；First public sentiment is broken out and is originated The forwarding number of users of the unit time of moment to the first public sentiment outburst finish time averagely breaks out rate, realization pair as first First public sentiment averagely breaks out the prediction of rate；

First maximum public sentiment spread index predicting unit passes through (7) prediction the according to the following formula of initial Susceptible population's quantity One maximum public sentiment spread index, the described first maximum public sentiment spread index indicate that public sentiment event breaks out the maximum side that can reach Boundary's number, i.e. whole event Onset forward cumulative amount

Wherein, E₀Indicate initially accumulative transfer amount, E_sFor the first maximum public sentiment spread index.

Furthermore it is preferred that above- mentioned information transmission dynamics system further includes the first model training module, to the first model construction The information transmission dynamics model of module building is trained, comprising:

First training set construction unit acquires the data of microblogging setting time as training set；

The training set of first training set construction unit building is inputted the first model construction module by the first parameter training unit The information transmission dynamics model of building, using the parameter of Least Square Method information transmission dynamics model

Wherein, LS is the square error of parameter, and Θ is information transmission dynamics model parameter vectors, Θ=(β, α₁, p₁, S₀), ID indicates the cumulative actual transfer amount of training sample, t_k=k, k=1,2 ... T indicates sampling time, f_E(t_k, Θ) and it is moment t_k Training sample input parameter vector Θ the obtained accumulative transfer amount of information transmission dynamics model.

The construction method of the above-mentioned information transmission dynamics system based on microblogging is as shown in figure 3, the construction method includes:

The state of user in microblogging is divided into three kinds of sensitization, forwarding state and immune state states by step S1, In, the sensitization indicates that user not yet contacts issuing microblog but has the state that microblogging is forwarded ability；Described turn The expression of hair-like state has been carried out forwarding and can be to the state that other people have an impact in active state；The immune state It indicates the state for losing the ability of enlivening after user forwards or actively abandons the state of forwarding microblogging after reading information；

Step S2 constructs information transmission dynamics model by formula (1)-(4)；

Step S3, by the monitoring to accumulative transfer amount according to above- mentioned information transmission dynamics model to Susceptible population's quantity, Forwarding crowd quantity and immune crowd's quantity and its variation tendency (whether index derivative is greater than 0) are predicted.

In step s3, preferably further include the steps that predicting public sentiment by public sentiment index, the public sentiment index includes the The maximum public sentiment spread index of one public sentiment explosive index, the first public sentiment propagation peak and first, the step include:

The first public sentiment explosive index is predicted according to formula (5) by initial Susceptible population's quantity, and first public sentiment is quick-fried It sends out exponential representation public sentiment and breaks out the quantity that the stage actively forwards the forwarding user of customer impact by one, also illustrate that public sentiment event is quick-fried The severity of hair；

The first public sentiment propagation peak F is predicted according to formula (6) by initial Susceptible population's quantity_max, first public sentiment Propagation peak indicates the maximum value of user's forwarding behavior increment within the unit time when outburst of public sentiment event；

Forwarding number of users is broken out as the first public sentiment at the time of increase to first public sentiment the first setting ratio of propagation peak Initial time t_b, the prediction that initial time is broken out to the first public sentiment is realized by formula (6)；

Forwarding number of users is broken out into peak value moment t as the first public sentiment at the time of increase to the first public sentiment propagation peak_max, The prediction that peak value moment is broken out to the first public sentiment is realized by formula (6)；

It is broken out at the time of forwarding number of users is dropped to first public sentiment the second setting ratio of propagation peak as the first public sentiment Finish time t_e, the prediction that finish time is broken out to the first public sentiment is realized by formula (6)；

By the forwarding number of users of the unit time of the first public sentiment outburst initial time to the first public sentiment outburst peak value moment Speed V is broken out as the first public sentiment_o, realize the prediction to the first public sentiment outburst speed

Wherein, r₁For the first setting ratio；

By the forwarding number of users of the unit time of the first public sentiment outburst peak value moment to the first public sentiment outburst finish time As the first public sentiment decline rate V_d, realize the prediction to the first public sentiment decline rate

Wherein, r₂For the second setting ratio；

By the forwarding number of users of the unit time of the first public sentiment outburst initial time to the first public sentiment outburst finish time Rate V is averagely broken out as first_a, realize the prediction that rate is averagely broken out to the first public sentiment

By initial Susceptible population's quantity according to the maximum public sentiment spread index of formula (7) prediction first, described first most The public sentiment event that big public sentiment spread index indicates breaks out the maximum boundary number that can reach, i.e. whole event Onset forwarding is tired Metering.

Preferably, the construction method further includes the training step of the information transmission dynamics model, and the step includes:

The data of microblogging setting time are acquired as training set, input information transmission dynamics model；

Using the parameter of Least Square Method information transmission dynamics model.

Microblogging propagate final result just only and parameter S₀It is related.It is however noted that not with epidemiological study Together, susceptible number of users initial in microblogging is usually unknown, it is preferable that when event starts, the initial user of microblogging is issued, Only publisher's issuing microblog sets primary condition: E₀=F₀=1, I₀=0, S₀=N-F₀=N-1, N indicate closed in spaces, table Show the set for being likely to be exposed all users of this microblogging.

Above- mentioned information transmission dynamics system and its construction method also construct public sentiment index, realize and pass through information transmission dynamics mould Type obtains the collateral information that can more characterize public sentiment propagation condition.

In another embodiment of the present invention, the outburst of public sentiment event is frequently accompanied by the generation of many topics, and one Topic is made of many information, this makes the discussion of event and propagates efficient and information concentration.Topic amount of reading is as microblogging pair Another important information of outer publication contributes an amount of reading when a user browses to a microblogging, that is, enters read state, Since user may repeat repeatedly to read, i.e., susceptible person is become with certain ratio again, then remaining user is no longer right Public sentiment has of interest i.e. into immune state.Whether user selects forwarding simultaneously, it is built upon on the basis of having read, then Amount of reading can describe the scale and concerned degree of event outburst to a certain extent.In view of user may be to the same topic It is repeatedly read, i.e., user behavior has repeatability, intends the information transmission dynamics model SRI of single topic propagation of building as schemed Shown in 5, information transmission dynamics system can be constructed using multiple microbloggings of a topic, enhancement information transmission dynamics model adapts to Popularity, information transmission dynamics system can also be constructed using each microblogging, when multiple microbloggings building using topic When information transmission dynamics system, add up the sum of the accumulative amount of reading of multiple microbloggings that amount of reading is the topic, other parameters (S (t), F (t), I (t)) and so on.

Fig. 6 is the composition block diagram of another embodiment of the information transmission dynamics system the present invention is based on microblogging, such as Fig. 6 institute Show, the information transmission dynamics system includes:

The state of user in microblogging is divided into sensitization (S), read state (R) by 1 ' of second user state setting module With three kinds of states of immune state (I), wherein the sensitization indicate user not yet contact issuing microblog still have microblogging into The state of row reading ability；The read state expression has been carried out reading and can generate to other people in active state The state of influence；The immune state loses the state for the ability of enlivening after indicating user's reading or has exceeded reading validity period State；

Second model construction module, 2 ' constructs information transmission dynamics model by following formula (8)-(11)

S ' (t)=- β S (t) R (t)+p₂α₂R(t) (8)

R ' (t)=β S (t) R (t)-α₂R(t) (9)

I ' (t)=(1-p₂)α₂R(t) (10)

E′₂(t)=β S (t) R (t) (11)

Second prediction module, 3 ', the letter constructed by the monitoring to accumulative amount of reading according to above-mentioned second model construction module Cease the quantity and variation of the user in different states of transmission dynamics model prediction second user state setting module setting.

Preferably, the second prediction module also passes through public sentiment index prediction public sentiment, and the public sentiment index includes that the second public sentiment is quick-fried Several, the second public sentiment propagation peak of bristling with anger and the second maximum public sentiment spread index, second prediction module further include:

Second public sentiment explosive index predicting unit predicts the second carriage according to the following formula (12) by initial Susceptible population's quantity Feelings explosive index, the second public sentiment explosive index indicate the severity of public sentiment event outburst

Wherein :=indicate to be defined as,For the second public sentiment explosive index, E₂₀Indicate initially accumulative amount of reading, S₀It indicates The initial value of number of users in sensitization；

Judge that the second public sentiment explosive index is greater than 1, is also equal to 1 less than 1；

IfShow to read number of users decline, microblogging will be unable to further propagate；If Show that reading number of users will exponentially increase；IfShow to read number of users without change；

Second public sentiment propagation peak predicting unit predicts the second carriage according to the following formula (13) by initial Susceptible population's quantity Feelings propagation peak R_max, user's reading behavior is within the unit time when the second public sentiment propagation peak expression public sentiment event is broken out The maximum value of increment

Second time prediction unit, at the time of number of users being read and increase to the second public sentiment propagation peak third setting ratio Initial time is broken out as the second public sentiment, the second public sentiment outburst initial time is predicted；Number of users will be read to increase to the Peak value moment is broken out as the second public sentiment at the time of two public sentiment propagation peaks, the second public sentiment outburst peak value moment is predicted； It will read and break out finish time as the second public sentiment at the time of number of users drops to the second public sentiment four setting ratio of propagation peak, Second public sentiment outburst finish time is predicted；

Second speed predicting unit, when the second public sentiment is broken out unit of the initial time to the second public sentiment outburst peak value moment Between reading number of users as the second public sentiment break out speed, realize to the second public sentiment outburst speed prediction；By the second public sentiment The reading number of users of outburst peak value moment to the unit time that the second public sentiment breaks out finish time are fast as the decline of the second public sentiment Degree realizes the prediction to the second public sentiment decline rate；Second public sentiment is broken out into initial time and breaks out finish time to the second public sentiment Unit time reading number of users as second averagely break out rate, realization the pre- of rate is averagely broken out to the second public sentiment It surveys；

Second maximum public sentiment spread index predicting unit passes through (14) building the according to the following formula of initial Susceptible population's quantity Two maximum public sentiment spread indexs, the described second maximum public sentiment spread index indicate that public sentiment event breaks out the maximum side that can reach Boundary's number, i.e. whole event Onset read cumulative amount

Wherein, E_2sFor the second maximum public sentiment spread index.

Preferably, above- mentioned information transmission dynamics system further includes the second model training module, to the second model construction module The information transmission dynamics model of building is trained, comprising:

Second training set construction unit acquires the data of microblogging setting time as training set；

Second parameter training unit, the training set that training set construction unit is constructed input information transmission dynamics model, adopt With the parameter of Least Square Method information transmission dynamics model

Wherein, LS is the square error of parameter, and Θ ' is information transmission dynamics model parameter vectors, Θ '=(β, α₂, p₂, S₀), ID_k' indicates training sample moment t_kCumulative actual amount of reading, t_k=k, k=1,2 ... T indicates sampling time, f_E(t_k, Θ ') it is moment t_kTraining sample input parameter vector Θ ' the obtained accumulative amount of reading of information transmission dynamics model.

The construction method construction method of above- mentioned information transmission dynamics system includes:

The state of user in microblogging is divided into three kinds of sensitization, read state and immune state states, wherein described easy Sense state indicates that user not yet contacts issuing microblog but has the state that microblogging is forwarded ability；The read state indicates Have been carried out reading and in active state can be to the state that other people have an impact；The immune state indicates that user reads The state for the ability of enlivening is lost after reading or has exceeded the state for reading validity period；

Information transmission dynamics model is constructed by formula (8)-(11)；

By the monitoring to accumulative amount of reading according to above- mentioned information transmission dynamics model to Susceptible population's quantity, reading crowd Quantity and immune crowd's quantity and its variation are predicted.

Preferably, further include the steps that predicting that public sentiment, the public sentiment index are broken out including the second public sentiment by public sentiment index The maximum public sentiment spread index of index, the second public sentiment propagation peak and second, the schematic diagram of public sentiment index is as shown in fig. 7, the step Suddenly include:

The second public sentiment explosive index is predicted according to formula (12) by initial Susceptible population's quantity, and second public sentiment is quick-fried Send out the severity of exponential representation public sentiment event outburst；

The second public sentiment propagation peak R is predicted according to formula (13) by initial Susceptible population's quantity_max, second carriage Feelings propagation peak indicates the maximum value of user's reading behavior increment within the unit time when outburst of public sentiment event；

It will read at the time of number of users increases to the second public sentiment propagation peak third setting ratio and be broken out as the second public sentiment Initial time t_b' realizes the prediction that initial time is broken out to the second public sentiment by formula (13)；

It will read and break out peak value moment t as the second public sentiment at the time of number of users increases to the second public sentiment propagation peak_max', The prediction that peak value moment is broken out to the second public sentiment is realized by formula (13)；

It will read at the time of number of users drops to the second public sentiment four setting ratio of propagation peak and broken out as the second public sentiment Finish time t_e' realizes the prediction that finish time is broken out to the second public sentiment by formula (13)；

By the reading number of users of the unit time of the second public sentiment outburst initial time to the second public sentiment outburst peak value moment Speed V is broken out as the second public sentiment_o' realizes the prediction to the second public sentiment outburst speed

Wherein, r₃For third setting ratio；

By the reading number of users of the unit time of the second public sentiment outburst peak value moment to the second public sentiment outburst finish time As the second public sentiment decline rate V_d' realizes the prediction to the second public sentiment decline rate

Wherein, r₄For the 4th setting ratio；

By the reading number of users of the unit time of the second public sentiment outburst initial time to the second public sentiment outburst finish time Rate V is averagely broken out as second_a' realizes the prediction that rate is averagely broken out to the second public sentiment

By initial Susceptible population's quantity according to the maximum public sentiment spread index of formula (14) building second, described second most The public sentiment event that big public sentiment spread index indicates breaks out the maximum boundary number that can reach, i.e. whole event Onset is read tired Metering.

Preferably, further include the steps that training information transmission dynamics model, the step includes: acquisition microblogging setting time Data as training set, input information transmission dynamics model；Using the ginseng of Least Square Method information transmission dynamics model Number.

The public sentiment explosive index of one topicIt is by parameter beta, α₂And S₀It determines, and public sentiment propagation peak R_maxMost Big public sentiment spread index E_sIt is by parameter beta, α₂,S₀And p₂It is different from epidemiological study come what is determined, it is initial easy in microblogging Feel number of users S₀Usually it is unknown preferably, it is preferable that setting primary condition are as follows: E₂₀=R₀< <, I₀=0, S₀=N-R₀。

In the various embodiments of the invention, first setting ratio, the second setting ratio, third setting ratio and the 4th Setting ratio can be the same or different, for example, can be 0.1.

In the various embodiments described above of the invention, in Chinese public sentiment field, a maximum problem is exactly our needs The data unit (hour) for adding up reading or forwarding number of users in model parameter can be estimated, so as to quick-fried to event The propagation trend of hair early stage is predicted and calculates critical public sentiment index.Therefore, if the mistake of event is only used only in we Data are gone to estimate all model parameters, model can there is very big then our single microblogging SFI model is with list topic SRI Uncertain and unstability, it is preferred that being estimated when predicting public sentiment using partial parameters priori (a-priori) Method, specifically, comprising:

The topological diagram that micro-blog information is propagated is constructed, as shown in figure 8, the topological mode that message is propagated, comprising: star-like, constellation One or more of type and nebula type, wherein central point represents leader of opinion, around central point reading or forwarded correspondence Leader of opinion bean vermicelli, the line between point represents concern relation；

The similarity of premonitoring public sentiment with the public sentiment that finished is judged according to the registration of leader of opinion, what leader of opinion was overlapped More, similarity is higher；

The public sentiment that finished is ranked up according to the sequence of similarity from high to low, the public sentiment that finished of setting quantity before taking Model parameter average value as with monitoring public sentiment model parameter, the mould of the highest public sentiment that finished of similarity can also be taken Shape parameter is as the model parameter with monitoring public sentiment.

Preferably, model parameter β is determined by the compactedness of network structure, thus can when event starts just to its into Row estimation, model parameter β indicates mean exposure rate, because the susceptible number of users and entire subnet of whole event are (in topological diagram not With the topological subgraph of leader of opinion) compactedness just had determined that when topic starts to occur.

Furthermore it is preferred that model parameter α is usually the specific attribute of user rather than some topic or microblogging institute are peculiar 's.We are difficult to change the habit of user, because several talentes of some users can browse a microblogging, but some users are often short Several hours just select to open Sina weibo.Therefore, identical as model parameter β, we can be in the early stage (example of event Such as, from other events) remove estimation model parameter α.

In one particular embodiment of the present invention, the Sina weibo social activity most as Chinese current application user is flat The propagation of platform, various information focuses primarily upon microblogging, so Sina weibo researchs and analyses the control propagated today's society information System has great significance.Using a microblogging public sentiment in information transmission dynamics model SFI monitoring Sina weibo, comprising:

It goes to collect the quantity for accumulating forwarding user by microblogging API, as shown in table 1 below,

Table 1

Table 1 lists the data set of the accumulation transfer amount for the real event that a time span is 16 days；

The value range that model parameter is obtained using the model parameter of knowledge professional library or similar microblogging, in example as above, β ∈[2×10^-6, 8 × 10^-6], p₁∈ [0.1,0.5], α₁∈ [0.5,1.5], S₀∈[7×10⁶, 2 × 10⁷]；

According to above-mentioned value range, the initial value Θ of setting model parameter₀=(4.0 × 10^-6, 0.3,1.0,1.0 × 10⁷), using least square method obtain information transmission dynamics model parameter, and to S (t), F (t), I (t) sum E (t) curve into Row fitting, as a result as shown in figure 9, β=2.5651 × 10^-6, α₁=1.0365, p₁=0.1006, S₀=1.0221 × 10⁷；

Public sentiment is monitored in conjunction with above-mentioned curve and public sentiment index, as shown in figure 9, F_max= 2.4724×10⁵, E_s=0.9243 × 10⁶, t_b=5.92 (day), t_e=12.57day), t_max=8.49 (day), V_o= 0.8658×10⁵/ day, V_d=5.4538 × 10⁴/ day, V_a=3.3461 × 10⁴/day。

Preferably, following public sentiment index can be predicted using the accumulation forwarding number of users of setting time, For example, the accumulation forwarding number of users of first day, the 8th day second day ... is respectively adopted, using the above method to the model of SFI Parameter and public sentiment index predicted, as shown in Figure 10, it can be seen that model parameter can be estimated from preceding 3 days data Out, this is much earlier than peak explosion time, can not only indicate that first 3 days earliest pre-warning times occur for outgoing event, but also for The prediction of each public sentiment index is also more ideal.Especially, after the 8th day close to public sentiment outburst peak value, we can be quasi- Really predict our interested all indexes.

Further, it is preferable that using preceding accumulation in 3 days forwarding number of users as training set, obtained by least square method Model parameter is obtained, so that the public sentiment index to future time predicts that prediction result is as shown in following table 2-4

Table 2

Table 3

Table 4

In another specific embodiment of the invention, public sentiment is predicted using information transmission dynamics model SRI, comprising:

It goes to collect by microblogging API and accumulates the quantity for reading user, as shown in table 5 below,

Table 5

Table 5 lists the data set of the accumulation amount of reading of a length of 4 hours topics when four acquisitions；

The initial value of setting model parameter, using least square method obtain information transmission dynamics model parameter, and to S (t), F (t), I (t) and E₂(t) curve is fitted, as a result as shown in figure 11, β=2.5791 × 10^-7, α₂=0.22122, p₂= 0.10607, S₀=3240000；

Public sentiment is monitored in conjunction with above-mentioned curve and public sentiment index, as shown in figure 11,R_max= 1.3023×10⁶, E_2s=3.5307 ××s 10⁶, t_b'=23.12 (h), t_max=51.68 (h), t_e'=113.88 (h), V_o'= 4.1039×10⁴(/h), V_a'=1.2914 × 10⁴(/h), V_d'=1.8844 × 10⁴(/h)。

Preferably, predicting following public sentiment index for number of users can be read using the accumulation of setting time, For example, with 8 hours accumulative amount of reading using frequency collection microblogging, using the above method to the model parameter and public sentiment of SRI Index predicted, as shown in figure 12,3 days data can estimate Prediction Parameters (p before topic₂, S₀), and this when Between put this much earlier than peak explosion time.It was found that the parameter of estimation and SRI model are combined pre- to carry out public sentiment It surveys, real data can be ideally fitted, until there is turning point, such as time point 16 hours, 40 hours and 72 hours.Total comes Say, after there is variation tendency in information communication process, SRI model can in 8 hours Fast Fitting, therefore have to early prediction Good effect.

Further, it is preferable that number of users is read as training set using preceding accumulation in 40 hours, passes through least square method Model parameter is obtained, so that the public sentiment index to future time predicts that prediction result is as shown in following table 6-8

Table 6

Table 7

Table 8

From table 6-8 can be seen that using the past 40 hours data go prediction when, public sentiment explosive indexAnd public sentiment Break out speed (V_o') all there is lower prediction accuracy, still, initial time (t is broken out for public sentiment_b') but have quite not Wrong prediction effect for 24 hours.In particular, if propagation peak time (t_max'=50.08h) after 40 hours that public sentiment occurs, Our partial parameters apriority can accurately predict it.Regrettably, it within public sentiment is broken out 56 hours, can only obtain The lower public sentiment of one accuracy breaks out peak value (R_max), but this is still worth because need not until public sentiment it is final when Between point (110h), can know earlier public sentiment outburst peak value quantity.In addition, breaking out end time (t for public sentiment_e') or Person is public sentiment decay rates (V_d'), maximum public sentiment spread index (E_2s) and averagely outburst rate (V_aFor '), optimal early stage Predicted time point is 70 hours, 78 hours and 62 hours respectively.

Generally speaking, the partial parameters prior estimate method that the present invention is proposed for early prediction can help us to be advanced by The progress of microblog topic is solved, therefore, network public-opinion can be monitored and controlled in we in time.

Information transmission dynamics system, construction method, device and medium of the present invention based on microblogging passes through cross discipline, By mathematical theory be applied to the analysis of public opinion, Information dynamics with public sentiment event information propagation combine, Information dynamics analysis and News public sentiment event analysis, which combines, establishes model.For the exclusive information propagating characteristic and different user's rows of Sina weibo To be modeled respectively to the reading behavior of user and forwarding behavior, passing through transfer amount to single microblogging and single topic Amount of reading constructs parameter and carries out parameter Estimation, and the indexs such as outburst rate, explosion time for breaking out to public sentiment are analyzed, and building is deposited In the new public sentiment ecological models of three kinds of spread states, to be applied to the prediction, early warning and control analysis of public sentiment.

The various embodiments described above are respectively described SRI and SFI information transmission dynamics system, but of the invention and unlimited In this, information transmission dynamics system of the invention can use the combination of SRI and SFI, to forwarding number it is predicted that while to readding It reads data to be predicted, can also need to assign two kinds of different weights of model according to different, for example, laying particular stress on forwarding data When (the public sentiment index of forwarding number and SFI), so that the weight of SFI is greater than the weight of SRI.

In addition, the present invention also provides a kind of electronic device, including memory and processor, base is stored in the memory In the information transmission dynamics system construction procedures of microblogging, the information transmission dynamics system construction procedures based on microblogging are described Processor realizes the step of the above-mentioned information transmission dynamics system constituting method based on microblogging when executing.

In the present embodiment, memory includes the readable storage medium storing program for executing of at least one type.At least one type Readable storage medium storing program for executing can be the non-volatile memory medium of such as flash memory, hard disk, multimedia card, card-type memory.In some realities It applies in example, the readable storage medium storing program for executing can be the internal storage unit of the electronic device, such as the hard disk of the electronic device. In further embodiments, the readable storage medium storing program for executing is also possible to the external memory of the electronic device, such as the electricity The plug-in type hard disk being equipped in sub-device, intelligent memory card (Smart Media Card, SMC), secure digital (Secure Digital, SD) card, flash card (Flash Card) etc., the memory can be also used for temporarily storing exported or The data that will be exported.

Processor can be a central processing unit (Central Processing Unit, CPU) in some embodiments, Microprocessor or other data processing chips, program code or processing data for being stored in run memory.

Preferably, electronic device further includes network interface, optionally may include standard wireline interface and wireless interface (such as WI-FI interface), commonly used in establishing communication connection between the electronic device, with other electronic equipments；Communication bus is used Connection communication between these components of realization.

In addition, the embodiment of the present invention also proposes a kind of computer readable storage medium, the computer readable storage medium In include the information transmission dynamics system construction procedures based on microblogging, it is described based on microblogging information transmission dynamics system building When program is executed by processor, realize the above-mentioned information transmission dynamics system constituting method based on microblogging the step of.

The electronic device of the present invention and the specific embodiment of computer readable storage medium and the above-mentioned letter based on microblogging Breath transmission dynamics system constituting method, the specific embodiment of system are roughly the same, and details are not described herein.

Although content disclosed above shows exemplary embodiment of the present invention, it should be noted that without departing substantially from power Under the premise of benefit requires the range limited, it may be many modifications and modify.According to the side of inventive embodiments described herein Function, step and/or the movement of method claim are not required to the execution of any particular order.In addition, although element of the invention can It is unless explicitly limited individual element it is also contemplated that having multiple elements to be described or be required in the form of individual.

Claims

1. a kind of information transmission dynamics system constituting method based on microblogging characterized by comprising

The state of user in microblogging is divided into three kinds of sensitization, forwarding state and immune state states, wherein the susceptible shape State indicates that user not yet contacts issuing microblog but has the state that microblogging is forwarded ability；The forwarding state indicates Carried out forward and in active state can be to the state that other people have an impact；After the immune state indicates user's forwarding Lose the ability of enlivening state or reading information after actively abandon forwarding microblogging state；

S ' (t)=- β S (t) F (t) (1)

F ' (t)=p₁βS(t)F(t)-α₁F(t) (2)

I ' (t)=(1-p₁)βS(t)F(t)+α₁F(t) (3)

E ' (t)=p₁βS(t)F(t) (4)

Wherein, t is time index, and β indicates the user in sensitization in the rate of the mean exposure information of microblogging, p₁It indicates User in sensitization forwards the rate of microblogging, α₁Indicate that the user in forwarding state becomes sluggish rate, S It (t) is Susceptible population's quantity, F (t) is forwarding crowd quantity, and I (t) is immune crowd's quantity, and E (t) is accumulative transfer amount, S ' (t), F ' (t), I ' (t) and E ' (t) are respectively the derivative of S (t), F (t), I (t) and E (t), and the derivative, which is greater than 0, indicates index Numerical value increases, and derivative indicates that index value reduces less than 0, and derivative, which is equal to 0, indicates that index value is constant；

By the monitoring to accumulative transfer amount according to above- mentioned information transmission dynamics model to following Susceptible population's quantity, forwarding people Group's quantity and immune crowd's quantity and its variation are predicted.

2. the information transmission dynamics system constituting method according to claim 1 based on microblogging, which is characterized in that further include The step of predicting public sentiment by public sentiment index, the public sentiment index include the first public sentiment explosive index, the first public sentiment propagation peak With the first maximum public sentiment spread index, the step includes:

The first public sentiment explosive index is predicted according to the following formula (5) by initial Susceptible population's quantity, and the first public sentiment outburst refers to Number indicates that the public sentiment outburst stage actively forwards the quantity of the forwarding user of customer impact by one, also illustrates that the outburst of public sentiment event Severity

Wherein :=indicate to be defined as,For the first public sentiment explosive index, S₀Indicate the first of the number of users in sensitization Initial value；

IfShow to forward number of users decline, microblogging will be unable to further propagate；IfShow Forwarding number of users will exponentially increase；IfShow to forward number of users without change；

The first public sentiment propagation peak F is predicted according to the following formula (6) by initial Susceptible population's quantity_max, the first public sentiment propagation Peak value indicates the maximum value of user's forwarding behavior increment within the unit time when outburst of public sentiment event

Wherein, F₀Indicate the initial value of the number of users in forwarding state；

Forwarding number of users is broken out as the first public sentiment at the time of increase to first public sentiment the first setting ratio of propagation peak and is originated Moment predicts the first public sentiment outburst initial time；

Forwarding number of users is broken out into peak value moment as the first public sentiment at the time of increase to the first public sentiment propagation peak, to the first carriage Feelings outburst peak value moment is predicted；

Terminate at the time of forwarding number of users is dropped to first public sentiment the second setting ratio of propagation peak as the outburst of the first public sentiment Moment predicts the first public sentiment outburst finish time；

Using the first public sentiment break out initial time to the first public sentiment outburst peak value moment unit time forwarding number of users as First public sentiment breaks out speed, realizes the prediction to the first public sentiment outburst speed；

Using the first public sentiment break out peak value moment to the first public sentiment outburst finish time unit time forwarding number of users as First public sentiment decline rate realizes the prediction to the first public sentiment decline rate；

Using the first public sentiment break out initial time to the first public sentiment outburst finish time unit time forwarding number of users as First averagely breaks out rate, realizes the prediction that rate is averagely broken out to the first public sentiment；

The first maximum public sentiment spread index, the described first maximum carriage are predicted according to the following formula (7) by initial Susceptible population's quantity The public sentiment event that feelings spread index indicates breaks out the maximum boundary number that can reach, i.e. whole event Onset forwards cumulative amount

3. the information transmission dynamics system constituting method according to claim 1 based on microblogging, which is characterized in that the letter Breath transmission dynamics model training method include:

Using the parameter of Least Square Method information transmission dynamics model

Wherein, LS is the square error of parameter, and Θ is information transmission dynamics model parameter vectors, Θ=(β, α₁, p₁, S₀), ID table Show the cumulative actual transfer amount of training sample, t_k=k, k=1,2 ... T indicates sampling time, f_E(t_k, Θ) and it is moment t_k's The accumulative transfer amount that the information transmission dynamics model of training sample input parameter vector Θ obtains.

4. a kind of information transmission dynamics system constituting method based on microblogging characterized by comprising

The state of user in microblogging is divided into three kinds of sensitization, read state and immune state states, wherein the susceptible shape State indicates that user not yet contacts issuing microblog but has the state that microblogging carries out reading ability；The read state indicates Carried out read and in active state can be to the state that other people have an impact；After the immune state indicates that user reads It loses the state for the ability of enlivening or has exceeded the state for reading validity period；

S ' (t)=- β S (t) R (t)+p₂α₂R(t) (8)

R ' (t)=β S (t) R (t)-α₂R(t) (9)

I ' (t)=(1-p₂)α₂R(t) (10)

E′₂(t)=β S (t) R (t) (11)

Wherein, R (t) is reading crowd quantity, E₂It (t) is accumulative amount of reading, p₂Indicate that reading user generates repeat reading behavior Rate, α₂Indicate that user becomes sluggish rate, S ' (t), F ' (t), I ' (t) and E ' in read state₂(t) be respectively S (t), F (t), I (t) and E₂(t) derivative, the derivative, which is greater than 0, indicates that index value increases, and derivative subtracts less than 0 expression index value Small, derivative, which is equal to 0, indicates that index value is constant；

By the monitoring to accumulative amount of reading according to above- mentioned information transmission dynamics model to following Susceptible population's quantity, reader Group's quantity and immune crowd's quantity and its variation are predicted.

5. the information transmission dynamics system constituting method according to claim 4 based on microblogging, which is characterized in that further include The step of predicting public sentiment by public sentiment index, the public sentiment index include the second public sentiment explosive index, the second public sentiment propagation peak With the second maximum public sentiment spread index, the step includes:

The second public sentiment explosive index is predicted according to the following formula (12) by initial Susceptible population's quantity, and the second public sentiment outburst refers to Number indicates the severity of public sentiment event outburst

Wherein :=indicate to be defined as,For the second public sentiment explosive index, E₂₀Indicate initially accumulative amount of reading, S₀Expression is in The initial value of the number of users of sensitization；

IfShow to read number of users decline, microblogging will be unable to further propagate；IfShow Reading number of users will exponentially increase；IfShow to read number of users without change；

The second public sentiment propagation peak R is predicted according to the following formula (13) by initial Susceptible population's quantity_max, the second public sentiment biography Broadcasting peak value indicates the maximum value of user's reading behavior increment within the unit time when outburst of public sentiment event

It will read to break out at the time of number of users increases to the second public sentiment propagation peak third setting ratio as the second public sentiment and originate Moment predicts the second public sentiment outburst initial time；

It will read and break out peak value moment as the second public sentiment at the time of number of users increases to the second public sentiment propagation peak, to the second carriage Feelings outburst peak value moment is predicted；

To read at the time of number of users drops to the second public sentiment four setting ratio of propagation peak terminates as the outburst of the second public sentiment Moment predicts the second public sentiment outburst finish time；

Using the second public sentiment break out initial time to the second public sentiment outburst peak value moment unit time reading number of users as Second public sentiment breaks out speed, realizes the prediction to the second public sentiment outburst speed；

Using the second public sentiment break out peak value moment to the second public sentiment outburst finish time unit time reading number of users as Second public sentiment decline rate realizes the prediction to the second public sentiment decline rate；

Using the second public sentiment break out initial time to the second public sentiment outburst finish time unit time reading number of users as Second averagely breaks out rate, realizes the prediction that rate is averagely broken out to the second public sentiment；

The second maximum public sentiment spread index, the described second maximum carriage are constructed according to the following formula (14) by initial Susceptible population's quantity The public sentiment event that feelings spread index indicates breaks out the maximum boundary number that can reach, i.e. whole event Onset reads cumulative amount

Wherein, E_2sFor the second maximum public sentiment spread index.

6. a kind of information transmission dynamics system based on microblogging characterized by comprising

The state of user in microblogging is divided into three kinds of sensitization, forwarding state and immune state shapes by User Status setting module State, wherein the sensitization indicates that user not yet contacts issuing microblog but has the state that microblogging is forwarded ability；Institute State forwarding state expression have been carried out forwarding and in active state can be to the state that other people have an impact；It is described immune State indicate user forwarding after lose the ability of enlivening state or reading information after actively abandon forwarding microblogging state；

S ' (t)=- β S (t) F (t) (1)

F ' (t)=p₁βS(t)F(t)-α₁F(t) (2)

I ' (t)=(1-p₁)βS(t)F(t)+α₁F(t) (3)

E ' (t)=p₁βS(t)F(t) (4)

Prediction module, the information transmission dynamics model constructed by the monitoring to accumulative transfer amount according to above-mentioned model construction module Predict the quantity and variation of the user in different states of User Status setting module setting.

7. the information transmission dynamics system according to claim 6 based on microblogging, which is characterized in that the prediction module is also Predict that public sentiment, the public sentiment index include the first public sentiment explosive index, the first public sentiment propagation peak and first by public sentiment index Maximum public sentiment spread index, the prediction module include:

First public sentiment explosive index predicting unit predicts that the first public sentiment is quick-fried according to the following formula (5) by initial Susceptible population's quantity Bristle with anger number, the first public sentiment explosive index indicates the forwarding user's that the public sentiment outburst stage actively forwards customer impact by one Quantity also illustrates that the severity of public sentiment event outburst

First public sentiment propagation peak predicting unit predicts that the first public sentiment passes according to the following formula (6) by initial Susceptible population's quantity Broadcast peak F_max, user forwards behavior increment within the unit time when the first public sentiment propagation peak expression public sentiment event is broken out Maximum value

First time predicting unit, will forwarding number of users at the time of increase to first public sentiment the first setting ratio of propagation peak as First public sentiment breaks out initial time, predicts the first public sentiment outburst initial time；Forwarding number of users is increased to the first carriage Peak value moment is broken out as the first public sentiment at the time of feelings propagation peak, the first public sentiment outburst peak value moment is predicted；It will turn Finish time is broken out as the first public sentiment at the time of hair number of users drops to first public sentiment the second setting ratio of propagation peak, to the One public sentiment outburst finish time is predicted；

First Speed predicting unit, by the unit time of the first public sentiment outburst initial time to the first public sentiment outburst peak value moment It forwards number of users to break out speed as the first public sentiment, realizes the prediction to the first public sentiment outburst speed；First public sentiment is broken out Peak value moment to the first public sentiment outburst finish time unit time forwarding number of users as the first public sentiment decline rate, it is real Now to the prediction of the first public sentiment decline rate；By the unit of the first public sentiment outburst initial time to the first public sentiment outburst finish time The forwarding number of users of time averagely breaks out rate as first, realizes the prediction that rate is averagely broken out to the first public sentiment；

First maximum public sentiment spread index predicting unit, by initial Susceptible population's quantity, (7) prediction first is most according to the following formula Big public sentiment spread index, the described first maximum public sentiment spread index indicate that public sentiment event breaks out the maximum boundary that can reach Number, i.e. whole event Onset forward cumulative amount

8. the information transmission dynamics system according to claim 6 based on microblogging, which is characterized in that the User Status is set The state of user in microblogging is divided into three kinds of sensitization, read state and immune state states by cover half block, wherein described susceptible State indicates that user not yet contacts issuing microblog but has the state that microblogging carries out reading ability；The read state indicates Carried out read and in active state can be to the state that other people have an impact；The immune state indicates that user reads The state for the ability of enlivening is lost afterwards or has exceeded the state for reading validity period；

S ' (t)=- β S (t) R (t)+p₂α₂R(t) (8)

R ' (t)=β S (t) R (t)-α₂R(t) (9)

I ' (t)=(1-p₂)α₂R(t) (10)

E′₂(t)=β S (t) R (t) (11)

Wherein, R (t) is reading crowd quantity, E₂It (t) is accumulative amount of reading, p₂Indicate that reading user generates repeat reading behavior Rate, α₂Indicate that user becomes sluggish rate, S ' (t), F ' (t), I ' (t) and E ' in read state₂It (t) is respectively S (t), F (t), I (t) and E₂(t) derivative, the derivative, which is greater than 0, indicates that index value increases, and derivative indicates index number less than 0 Value reduces, and derivative, which is equal to 0, indicates that index value is constant；

The information transmission dynamics that the prediction module is constructed by the monitoring to accumulative amount of reading according to above-mentioned model construction module The quantity and variation of the user in different states of model prediction User Status setting module setting.

9. the information transmission dynamics system according to claim 8 based on microblogging, which is characterized in that the prediction module inspection The public sentiment index of survey further includes the second public sentiment explosive index, the second public sentiment propagation peak and the second maximum public sentiment spread index, institute State prediction module further include:

Second public sentiment explosive index predicting unit predicts that the second public sentiment is quick-fried according to the following formula (12) by initial Susceptible population's quantity Bristle with anger number, the second public sentiment explosive index indicates the severity of public sentiment event outburst

Second public sentiment propagation peak predicting unit predicts that the second public sentiment passes according to the following formula (13) by initial Susceptible population's quantity Broadcast peak value R_max, user's reading behavior increment within unit time when the second public sentiment propagation peak expression public sentiment event is broken out Maximum value

Second time prediction unit, will read at the time of number of users increases to the second public sentiment propagation peak third setting ratio as Second public sentiment breaks out initial time, predicts the second public sentiment outburst initial time；Number of users will be read to increase to the second carriage Peak value moment is broken out as the second public sentiment at the time of feelings propagation peak, the second public sentiment outburst peak value moment is predicted；It will read It reads to break out finish time as the second public sentiment at the time of number of users drops to the second public sentiment four setting ratio of propagation peak, to the Two public sentiments outburst finish time is predicted；

Second speed predicting unit, by the unit time of the second public sentiment outburst initial time to the second public sentiment outburst peak value moment Number of users is read as the second public sentiment and breaks out speed, realizes the prediction to the second public sentiment outburst speed；Second public sentiment is broken out Peak value moment to the second public sentiment outburst finish time unit time reading number of users as the second public sentiment decline rate, it is real Now to the prediction of the second public sentiment decline rate；By the unit of the second public sentiment outburst initial time to the second public sentiment outburst finish time The reading number of users of time averagely breaks out rate as second, realizes the prediction that rate is averagely broken out to the second public sentiment；

Second maximum public sentiment spread index predicting unit constructs second most according to the following formula (14) by initial Susceptible population's quantity Big public sentiment spread index, the described second maximum public sentiment spread index indicate that public sentiment event breaks out the maximum boundary that can reach Number, i.e. whole event Onset read cumulative amount

Wherein, E_2sFor the second maximum public sentiment spread index.

10. the information transmission dynamics system according to any claim in claim 6-9 based on microblogging, feature exist In, further include model training module, to model construction module building information transmission dynamics model be trained, comprising:

Training set construction unit acquires the data of microblogging setting time as training set；

Parameter training unit, the training set that training set construction unit is constructed input information transmission dynamics model, using minimum two The parameter of multiplication estimated information transmission dynamics model.