CN113781250A - Social media information propagation evaluation method and device - Google Patents
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Abstract
The application discloses a social media information propagation evaluation method and device. One embodiment of the method comprises: acquiring users related to the current social media information and relations among the users; calculating the influence scores of the users based on the users and the relations among the users; acquiring interactive content of each user related to the current social media information based on the relationship among the users; calculating the quality scores of the interactive contents of the users based on the interactive contents of the users; and calculating the propagation evaluation score of the current social media information based on the influence score of each user and the quality score of the interactive content of each user. The implementation mode can effectively measure the propagation effect of the social media information.
Description
Technical Field
The disclosure relates to the technical field of internet, in particular to a social media information propagation evaluation method, a social media information propagation evaluation device, electronic equipment and a computer readable medium.
Background
Currently, billions of users in the world use social media such as facebook, twitter, microblog, etc. Many ' big V (microblog users who have fan after microblog certification) ' KOL (Key Opinion Leader) ' generate a large number of topics every day through social media, and influence the mind of millions of users. However, unlike traditional internet advertising, social media information is not amenable to evaluation of its effectiveness using traditional methods.
Disclosure of Invention
The embodiment of the disclosure provides a social media information propagation evaluation method, a social media information propagation evaluation device, electronic equipment and a computer readable medium.
In a first aspect, an embodiment of the present disclosure provides a social media information propagation evaluation method, including: acquiring users related to the current social media information and relations among the users; calculating the influence scores of the users based on the users and the relations among the users; acquiring interactive content of each user related to the current social media information based on the relationship among the users; calculating the quality scores of the interactive contents of the users based on the interactive contents of the users; and calculating the propagation evaluation score of the current social media information based on the influence score of each user and the quality score of the interactive content of each user.
In some embodiments, the method comprises: an interactive user related to the current social media information and an associated user related to the interactive user; the relationships between the respective users include: the relationship between the interactive user and the current social media information, and the relationship between the interactive user and the associated user; the calculating the influence score of each user based on each user and the relationship between each user includes: establishing a user relationship network based on the interactive user, the associated user, the relationship between the interactive user and the current social media information and the relationship between the interactive user and the associated user; based on the user relationship network, an influence score of each user in the user relationship network is determined.
In some embodiments, the interactive content includes: interactive information and recognition; the calculating the quality score of the interactive content of each user based on the interactive content of each user includes: inputting interactive information of each user into trainedObtaining the probability value of forward content of the interaction information of each user output by the emotion analysis model; inputting the probability value and the recognition degree of the forward content into a quality score formula, and calculating to obtain the quality score of the interactive content of each user; the formula of mass fraction: sX,A＝Min(1.0,PX,A-μ+LX,Aε) of which SX,AA quality score of the interactive content for user X; l isX,AEndorsement of user X to current social media information A advertisement, LX,A0 denotes no approval, LX,A1 indicates approval; pX,A∈[0,1.0]A probability value representing that the interaction information of the user X is forward content; μ and ε are the specified coefficients.
In some embodiments, the interactive content includes: a recognition degree; the calculating the quality score of the interactive content of each user based on the interactive content of each user includes: for each user, in response to determining that the user has a recognition level, determining that the quality score of the interactive content of the user is: 0.5-mu + 1/epsilon, where mu and epsilon are the specified coefficients.
In some embodiments, the calculating a propagation evaluation score of the current social media information based on the influence score of each user and the quality score of the interactive content of each user includes: multiplying the influence score of each user with the quality score of the interactive content corresponding to each user to obtain the propagation evaluation score of each user; and adding the propagation evaluation scores of all the users to obtain the propagation evaluation score of the current social media information.
In some embodiments, further comprising: and determining a core user in the users based on the influence scores of the users and the quality scores of the interactive contents of the users.
In some embodiments, the determining a core user among the users based on the influence score of each user and the quality score of the interactive content of each user includes: multiplying the influence score of each user with the quality score of the interactive content corresponding to each user to obtain the propagation evaluation score of each user; and taking the user with the highest propagation evaluation score in all users as a core user in the users.
In a second aspect, an embodiment of the present disclosure provides a social media information dissemination evaluation apparatus, including: an acquisition unit configured to acquire users related to current social media information and relationships between the users; an influence calculation unit configured to calculate an influence score of each user based on each user and a relationship between each user; the interaction unit is configured to acquire interaction content of each user related to the current social media information based on the relationship among the users; a quality calculation unit configured to calculate a quality score of the interactive content of each user based on the interactive content of each user; and the evaluation unit is configured to calculate the propagation evaluation score of the current social media information based on the influence score of each user and the quality score of the interactive content of each user.
In some embodiments, the respective users include: an interactive user related to the current social media information and an associated user related to the interactive user; the relationship among the users comprises: the relationship between the interactive user and the current social media information, and the relationship between the interactive user and the associated user; the influence calculating unit includes: a networking module configured to establish a user relationship network based on the interactive users, the associated users, the relationship between the interactive users and the current social media information, and the relationship between the interactive users and the associated users; a determination module configured to determine an influence score for each user in a user relationship network based on the user relationship network.
In some embodiments, the interactive content includes: interactive information and recognition; the mass calculation unit includes: the output module is configured to input the interaction information of each user into the trained emotion analysis model, and obtain a probability value of forward content of the interaction information of each user output by the emotion analysis model; the input module is configured to input the probability value and the recognition degree of the forward content into a quality score formula, and the quality score of the interactive content of each user is obtained through calculation; the formula of mass fraction: sX,A＝Min(1.0,PX,A-μ+LX,Aε) of which SX,AA quality score of the interactive content for user X; l isX,AEndorsement of user X to current social media information A advertisement, LX,A0 denotes no approval, LX,A1 indicates approval; pX,A∈[0,1.0]A probability value representing that the interaction information of the user X is forward content; μ and ε are the specified coefficients.
In some embodiments, the interactive content includes: a recognition degree; the quality calculating unit is further configured to determine, for each user, in response to determining that the user has a recognition degree, the quality score of the interactive content of the user as: 0.5-mu + 1/epsilon, where mu and epsilon are the specified coefficients.
In some embodiments, the above evaluation unit comprises: the multiplying module is configured to multiply the influence scores of the users with the quality scores of the interactive contents corresponding to the users to obtain the propagation evaluation scores of the users; and the adding module is configured to add the propagation evaluation scores of all the users to obtain the propagation evaluation score of the current social media information.
In some embodiments, the above apparatus further comprises: the determining unit is configured to determine a core user of the users based on the influence scores of the users and the quality scores of the interactive contents of the users.
In some embodiments, the determining unit includes: the obtaining module is configured to multiply the influence scores of the users with the quality scores of the interactive contents corresponding to the users to obtain the propagation evaluation scores of the users; and the ranking module is configured to rank the user with the highest propagation evaluation score in all the users as a core user in the users.
In a third aspect, an embodiment of the present disclosure provides an electronic device, including: one or more processors; a storage device having one or more programs stored thereon; when the one or more programs are executed by the one or more processors, the one or more processors are caused to implement the method as described in any implementation of the first aspect.
In a fourth aspect, embodiments of the present disclosure provide a computer-readable medium on which a computer program is stored, which when executed by a processor implements the method as described in any of the implementations of the first aspect.
According to the social media information propagation evaluation method and device provided by the embodiment of the disclosure, firstly, users related to current social media information and relations among the users are obtained; then, based on each user and the relationship among the users, calculating the influence score of each user; then, based on the relationship among the users, obtaining the interactive content of each user related to the current social media information; then, based on the interactive content of each user, calculating the quality score of the interactive content of each user; and finally, calculating the propagation evaluation score of the current social media information based on the influence score of each user and the quality score of the interactive content of each user. Therefore, information propagation power and propagation quality are comprehensively considered for the propagation of the social media information, invalid interaction of false accounts and inactive accounts can be avoided, the propagation power of the social media advertisement is more truly evaluated, and the propagation effect of the social media information is effectively measured.
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Other features, objects and advantages of the disclosure will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which one embodiment of the present disclosure may be applied;
FIG. 2 is a flow diagram of one embodiment of a social media information dissemination evaluation method in accordance with the present disclosure;
FIG. 3 is an exemplary diagram of various users and relationships between various users according to the present disclosure;
FIG. 4 is a flow chart of a method of calculating an influence score for individual users according to the present disclosure;
FIG. 5 is a flow chart of a method of calculating a quality score of interactive content for respective users according to the present disclosure;
FIG. 6 is a flow diagram of a method of calculating a propagation evaluation score for current social media information in accordance with the present disclosure;
FIG. 7 is a schematic block diagram illustrating one embodiment of a social media information dissemination evaluation apparatus in accordance with the present disclosure;
FIG. 8 is a schematic structural diagram of an electronic device suitable for use in implementing embodiments of the present disclosure.
Detailed Description
The present disclosure is described in further detail below with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
FIG. 1 illustrates an exemplary system architecture 100 to which the social media information dissemination evaluation method of the present disclosure may be applied.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, and typically may include wireless communication links and the like.
The terminal devices 101, 102, 103 interact with a server 105 via a network 104 to receive or send messages or the like. Various communication client applications, such as an instant messaging tool, a mailbox client, etc., can be installed on the terminal devices 101, 102, 103.
The terminal devices 101, 102, 103 may be hardware or software; when the terminal devices 101, 102, 103 are hardware, they may be user devices having communication and control functions, and the user settings may be communicated with the server 105. When the terminal devices 101, 102, 103 are software, they can be installed in the user device; the terminal devices 101, 102, 103 may be implemented as a plurality of software or software modules (e.g., software or software modules for providing distributed services) or as a single software or software module. And is not particularly limited herein.
The server 105 may be a server that provides various services, such as a social media information analysis server that provides support for a social media information analysis system on the terminal devices 101, 102, 103. The social media information analysis server can analyze and process the related information of each user in the network, and feed back the processing result (such as the propagation evaluation score of the social media information) to the terminal equipment.
The server may be hardware or software. When the server is hardware, it may be implemented as a distributed server cluster formed by multiple servers, or may be implemented as a single server. When the server is software, it may be implemented as multiple pieces of software or software modules (e.g., software or software modules used to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein.
It should be noted that the social media information propagation evaluation method provided by the embodiment of the present disclosure is generally executed by the server 105.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring to FIG. 2, a flow 200 of one embodiment of a social media information dissemination evaluation method according to the present disclosure is shown, the social media information dissemination evaluation method comprising the steps of:
in step 201, users related to the current social media information and relationships among the users are obtained.
In this embodiment, social media (social media) is a virtual community and network platform that people use to create, share, and exchange opinions, viewpoints, and experiences. The most remarkable difference between social media and general social mass media is that users enjoy more selection rights and editing ability, and the social media and the general social mass media are automatically gathered into a certain listening community. Social media can also be presented in a number of different forms, including text, images, music, and video. And the social media information is information published on the virtual community and the network platform, such as advertisements, videos and the like.
In this embodiment, the user related to the current social media information refers to an effective user directly or indirectly related to the current social media information, and the effective user refers to a user who has at least published a piece of content on the social media in a set time period (approximately 3 months), or has at least two fans.
In this embodiment, an executive subject of the social media information dissemination and evaluation method grabs valid users directly or indirectly related to the social media information at regular intervals (for example, 1 day) for each piece of social media information.
Further, the executing agent may also implement capturing of the users and the relationships between the users on the social media through some commonly used capturing tools (such as script, Selenium), specifically, when the social media is presented in the form of a web page, the capturing tools start from the page of each user, read the page, obtain their fans or links of the objects of interest and put them in a queue, continuously obtain links from the queue and repeat the above process until all links have been captured, so that all users related to the current social media information and the relationships between each user can be obtained.
In this embodiment, each user may be a user directly related to social media information (e.g., user 1, user 2, and user 3 directly related to an advertisement in fig. 3), which is also referred to as an interactive user, and in fig. 3, the interactive user is a user who has a liking relationship D or a comment relationship P with the advertisement; further, each user may also be a user further related to a user directly related to the social media information (e.g., a user 4, a user 5, and a user 6 indirectly related to the advertisement in fig. 3), which is also referred to as an associated user, where the associated user is a user directly related to the interactive user, in fig. 3, the user 4 has a fan relationship F with the user 1, and the user 6 has an attention relationship G with the user 1.
In this embodiment, a user relationship network including all users may be established based on the relationship between the users and all users, and the influence score of each user may be determined according to the ranking of each user in the user relationship network.
In this embodiment, the user may have various feedback on the social media information, for example, the recognition degree of the social media information or the interaction information of the social media information. And the interactive information for commenting the social media information can be positive content or negative content. In this embodiment, the endorsement of the social media information may be expressed by the user's approval of the social media information, for example, if the user approves the social media information, it indicates that the user expresses approval; the user disapproval of the social media information indicates that the user does not approve.
In this embodiment, the interaction information is information interaction content between each user or multiple users in a social media information dissemination process, for example, the interaction information includes: comment content or/and forward content.
Specifically, the interactive content includes: the interactive information and the recognition degree, or the interactive content comprises: the recognition degree comprises: indicating approval and non-approval.
And step 204, calculating the quality scores of the interactive contents of the users based on the interactive contents of the users.
In this embodiment, the interaction content of each user with the social media information may be positive, neutral or even negative. For example, "the brand of ice cream is really good" (positive), "how long the ice cream can be kept" (neutral), "who buy the ice cream and regret" (negative), "the microblog still has the ice cream advertisement at present" (negative). If there is a negative emotion in the reviews of advertisements, in the forwarded content, the dissemination of such content may harm the brand. Neutral content is relatively good and can help the dissemination gracefully. Therefore, it is necessary to analyze all interactive contents to evaluate whether each review/forward has a positive or negative effect.
In some optional implementations of this embodiment, the interactive content includes: indicating approval; the calculating the quality score of the interactive content of each user based on the interactive content of each user includes: for each user, in response to determining that the user represents approval, determining the quality score of the interactive content of the user as: 0.5- μ + 1/epsilon, where μ and epsilon are designated coefficients, where μ and epsilon can be self-designated based on empirical values, such as, in one example, epsilon 5 and mu 0.4.
In the optional implementation mode, when only the user approves the interactive content but no interactive information exists, the quality score of the interactive content is a fixed value, and the reliability of subsequent propagation evaluation score calculation is ensured.
In this embodiment, the pre-calculated influence scores of the users and the quality scores of the interactive contents of the users may be combined, so as to obtain how much score each user contributes to the current social media information (set as a). And adding the scores of all the users with the interaction generated in the step A to obtain the total score of the current social media information, wherein the total score is the propagation evaluation score of the current social media information.
In some optional implementations of this embodiment, after step 205, the method further includes: and determining a core user in the users based on the influence scores of the users and the quality scores of the interactive contents of the users.
In this embodiment, the core user refers to a person who has more and more accurate social media information, is accepted or trusted by the related group, and has a greater influence on the purchasing behavior of the group. The determined core user can be a core user with positive evaluation capability or a core user with negative evaluation capability based on the influence scores of the users and the quality scores of the interactive contents.
When the core users with positive evaluation capability are determined, a basis can be provided for the propagation and marketing of social media information. Further, the core user in the determined users may be one core user or a plurality of core users.
According to the method for determining the core users, after the propagation evaluation score of the current social media information is calculated, the core users in the users are determined based on the influence scores of the users and the quality scores of the interactive contents of the users, a basis is provided for determining the core users in the social network, and the reliability of executing the marketing strategy is guaranteed.
Further, when a core user needs to be obtained, in some optional implementation manners of this embodiment, determining the core user of the users based on the influence score of each user and the quality score of the interactive content of each user includes:
multiplying the influence score of each user with the quality score of the interactive content corresponding to each user to obtain the propagation evaluation score of each user; and taking the user with the highest propagation evaluation score in all users as a core user in the users.
The method for determining the core user among the users provided by the optional embodiment can obtain the user with the highest rank of the propagation evaluation score among all the users, and can effectively determine the influence center user of the social media information.
Optionally, when a plurality of core users need to be obtained, determining a core user of the users based on the influence score of each user and the quality score of the interactive content of each user, further includes: multiplying the influence score of each user with the quality score of the interactive content corresponding to each user to obtain the propagation evaluation score of each user; taking the users with the propagation evaluation score ranks set from top to bottom in all the users as core users in the users; the setting bit may be set according to network requirements, for example, setting bit to 5.
According to the social media information propagation evaluation method provided by the embodiment of the disclosure, firstly, users related to current social media information and relations among the users are obtained; then, based on each user and the relationship among the users, calculating the influence score of each user; then, based on the relationship among the users, obtaining the interactive content of each user related to the current social media information; then, based on the interactive content of each user, calculating the quality score of the interactive content of each user; and finally, calculating the propagation evaluation score of the current social media information based on the influence score of each user and the quality score of the interactive content of each user. Therefore, information propagation power and propagation quality are comprehensively considered for the propagation of the social media information, invalid interaction of false accounts and inactive accounts can be avoided, the propagation power of the social media advertisement is more truly evaluated, and the propagation effect of the social media information is effectively measured.
In this embodiment, each user includes a user directly related to the social media information, which is also referred to as an interactive user, and may further include a user further related to the user directly related to the social media information, which is also referred to as an associated user.
In some optional implementations of this embodiment, each user includes: an interactive user related to the current social media information and an associated user related to the interactive user; the relationships between the respective users include: the relationship between the interactive user and the current social media information, and the relationship between the interactive user and the associated user. As shown in fig. 4, a flow 400 of a method of calculating an influence score of each user of the present disclosure is illustrated, the method of calculating an influence score of each user comprising:
In this optional implementation, the execution subject captures all interactive users of ads, such as endorsements, comments, and forwards, and captures their fans (set as F set) and their objects (set as G set) until no new users are available for capture. The order in which the user is grabbed is shown in FIG. 3. All the captured users and the relationship between the powder and the powder are saved in a database. This process avoids grabbing all users (in the billions) of social media, and only grabs users (typically in the millions to tens of millions, or even smaller) who are directly or indirectly related to the advertisement.
And establishing a user relationship network according to the saved effective users and the relationship among the effective users. Specifically, if user A "eats" user B, then an edge is attached from A to B. In this way, a user relationship network is established.
In this optional implementation manner, for the constructed user relationship network, the influence score of each user may be determined by calculating a PageRank value (hereinafter referred to as a PR value) of each user. The PR value is a component of the search ranking algorithm, the level is from 1 to 10, the level 10 is full score, and the higher the PR value is, the more important the position of the webpage in the search ranking is. The algorithm of the PR value is a classic algorithm of webpage ranking, and is not described in detail herein. In this embodiment, each calculated user PR value is saved as a propagation influence of the user.
The PR value shows that the method has a plurality of fans (possibly corpse powder) which have no influence and are not active, and the propagation influence of KOL cannot be increased; in contrast, high quality vermicelli, even in small quantities, can cause the contents of KOL publications to pop red. Therefore, KOL users with greater dissemination ability do not necessarily have the most fans, but are necessarily the center of the social network. Therefore, the PR value is used as a measure of the user's influence in this embodiment.
Optionally, the influence scores of the users can be obtained through modeling and the like. For example, a deep learning technology is adopted, and based on the constructed user relationship network of the users, the scores of the users on the user relationship network, which are output by the trained influence score model, are obtained, and the scores are the influence scores of the users.
In the optional implementation mode, the interactive users related to the social media information are firstly captured, and then the associated users related to the interactive users are captured, so that all the users related to the social media information are prevented from being captured, and the pressure of an execution main body in the data processing process is reduced on the basis of effectively capturing the users.
In this embodiment, the user may have various feedback on the social media information, for example, recognition of the social media information or interaction information for commenting on the social media. And the interactive information for commenting the social media information can be positive content or negative content. In some optional implementations of this embodiment, the interactive content includes: interactive information and recognition;
referring to fig. 5, a flow 500 of a method of calculating a quality score of interactive content of each user is shown, the method of calculating a quality score of interactive content of each user comprising:
In this alternative implementation, a BERT (Bidirectional Encoder feature) model, which is one of the most advanced natural language processing models at present, can be used as the emotion analysis model. The published Chinese emotion analysis data set is used as a transfer learning training sample (for example, Chinese microblog emotion analysis). Specifically, the training process of the emotion analysis model comprises the following steps:
1) from the transfer learning training samples, a batch of texts and their corresponding emotion polarities (positive is 1, negative is 0) are sampled.
2) Each text is encoded and input to the BERT model.
3) The BERT model outputs a category (for example, 768-dimensional Embedding vector) of a certain text (set as S), the category is used as input to a classifier (which can be implemented by adopting a fully-connected layer of a sigmoid activation function), and in step 1), an emotion label y (equal to 1 or 0) corresponding to the sampled text S is used as label data corresponding to the text to train the classifier.
Loop through steps 1) -3) until all samples have been trained for K rounds (e.g., K is 100), resulting in a trained BERT model (set to M). Thereafter, the trained BERT model is used to predict the emotional polarity of the interactive content of each user.
Specifically, taking an advertisement a as an example, the detailed description of the emotion analysis model prediction process is as follows:
capturing all forwarded and commented contents (hereinafter referred to as interactive contents) of an advertisement A and corresponding users;
aggregating a plurality of pieces of interactive contents of respective users (for example, a plurality of comments P of the user 3 in the figure, separated and combined into one by commas); taking out the interactive contents of each user one by one, and setting the currently taken out user as X; inputting the interactive content of the user X into the model M, and outputting the probability that the interactive content of the user X is the forward content, P, by a classifier of the model MX,AWherein P isX,A∈[0,1.0]。
In this optional implementation, the mass fraction formula: sX,A＝Min(1.0,PX,A-μ+LX,Aε) of which SX,AA quality score of the interactive content for user X; l isX,AEndorsement of user X to current social media information A advertisement, LX,A0 denotes no approval, LX,A1 indicates approval; pX,A∈[0,1.0]A probability value representing that the interaction information of the user X is forward content; μ and ε are the specified coefficients.
In this embodiment, S obtained by calculationX,AAnd storing the emotion scores into a database as the emotion scores of the user X on the advertisement A, namely the quality scores of the user X on the advertisement A.
In the formula of mass fraction, PX,Aμ denotes the positive or negative emotion of the user interaction content, and LX,AThe/epsilon is used to measure the impact of user acceptance (e.g., like praise).
If P isX,A>0.5, then user X is biased forward with respect to the interactive content of ad a, otherwise, the other way around. For PX,AContent closer to 0.5 (e.g., greater than 0.4 less than 0.5), and generally more neutral content (not significantly negative), their propagation is still valuable, so the relaxation operation can be done so that μ equals 0.4 rather than 0.5.
If (P)X,A-μ)<0, but LX,A1, that is, although the interactive content of the user is negative, it is favorable, and a contradiction occurs. The two possibilities are that the emotion two classification of the BERT model is inaccurate in judgment of the example, and the interactive content of the user is at least neutral; secondly, the user really issues negative content but the misoperation is praise. Due to the propagation influence of the written content, the content is far greater than the praise behavior of the user. Therefore, using the epsilon variable in the equation, the impact of praise is attenuated (assuming epsilon is 5 in this example, that praise is increased by an emotion score of 0.2).
From another perspective, the time and effort cost for the user to write the content (even a few words are very simple) is far more than that of praise, so that the behaviors of forwarding, commenting and the like can indicate the emotional attitude of the user, and the epsilon is more reasonable than 2 generally.
The embodiment can identify false users and low-activity users of social media, reasonably evaluate the marketing effect on the false users and the low-activity users, and avoid paying excessive advertising cost for the low-value and non-value users.
The method for calculating the quality score of the interactive content provided by the optional implementation mode comprises the steps of inputting the interactive information of each user into an emotion analysis model aiming at the interactive content comprising the interactive information and the recognition degree to obtain the probability value of the interactive information being the forward content; the probability value and the recognition degree of the forward content are input into a quality score formula to obtain the quality score of the interactive content, and therefore the reliability of the interactive content quality score calculation is guaranteed by the method for calculating the quality score of the interactive content, namely the forward probability value of the interactive information is calculated, and the recognition degree is added into the calculation process.
In this embodiment, evaluating the propagation effect of the social media information needs to comprehensively consider the influence score of the user and the quality score of the interactive content of the user.
Referring to fig. 6, a flow 600 of a method of calculating a propagated evaluation score of current social media information of the present disclosure is shown, the method of calculating a propagated evaluation score of current social media information comprising:
step 601, multiplying the influence score of each user with the quality score of the interactive content corresponding to each user to obtain the propagation evaluation score of each user.
In this optional implementation manner, taking a current advertisement a as an example, all users who like, comment, and forward the advertisement a are captured, and the user set is set as UA. To UAGenerates an influence score table including influence scores of the respective users and an emotion score table including quality scores of the respective users, and extracts an influence score PR of the user uuAnd a quality score S of user u for advertisement Au,A。
Calculating to obtain a propagation evaluation score R of the user uu,AThe propagation assessment score is the impact score, i.e., the mass score, i.e., Ru,A＝PRu*Su,A。
In this optional implementation, the total propagation effect score, i.e., the propagation evaluation score R of the advertisement a, is obtained by adding all the user propagation effects interacting with the advertisement aAI.e. by
Through the steps, the propagation evaluation score R of the current social media advertisement A can be obtainedAI.e., marketing effectiveness scores for the advertisement on social media.
The invention also integrates the propagation quality score, can identify advertisements with negative emotion of the user, and can identify advertisements with negative emotion according to Ru,A＝PRu*Su,AIf the user is more dislike to the advertisement, the advertisement's propagation influence(PRu) The larger the score, the lower the marketing effectiveness score.
According to the method for calculating the propagation evaluation score of the current social media information, the propagation evaluation scores of all the users are calculated, and the propagation evaluation scores of all the users are added to obtain the propagation evaluation score of the current social media information, so that the calculation effect of the propagation evaluation score of the current social media information is ensured on the basis of integrating the effects of the propagation evaluation scores of all the users.
With further reference to fig. 7, as an implementation of the method shown in the above figures, the present disclosure provides an embodiment of a social media information dissemination evaluation apparatus, which corresponds to the method embodiment shown in fig. 2, and which can be applied in various electronic devices.
As shown in fig. 7, an embodiment of the present disclosure provides a social media information dissemination evaluation apparatus 700, where the apparatus 700 includes: an acquisition unit 701, an influence calculation unit 702, an interaction unit 703, a quality calculation unit 704, and an evaluation unit 705. The obtaining unit 701 may be configured to obtain users related to the current social media information and relationships between the users. The influence calculation unit 702 may be configured to calculate an influence score for each user based on each user and a relationship between each user. The interaction unit 703 may be configured to obtain interaction content of each user related to the current social media information based on a relationship between the users. The quality calculation unit 704 may be configured to calculate a quality score of the interactive content of each user based on the interactive content of each user. The evaluation unit 705 may be configured to calculate a propagation evaluation score of the current social media information based on the influence score of each user and the quality score of the interactive content of each user.
In the present embodiment, in the social media information dissemination estimation apparatus 700, specific processes of the obtaining unit 701, the influence calculating unit 702, the interacting unit 703, the quality calculating unit 704, and the estimating unit 705 and technical effects thereof may refer to step 201, step 202, step 203, step 204, and step 205 in the corresponding embodiment of fig. 2, respectively.
In some embodiments, the respective users include: an interactive user related to the current social media information and an associated user related to the interactive user; the relationship among the users comprises: the relationship between the interactive user and the current social media information, and the relationship between the interactive user and the associated user; the influence calculation unit 702 includes: a networking module (not shown), and a determining module (not shown). Wherein the networking module may be configured to establish a user relationship network based on the interactive users, the associated users, the relationship between the interactive users and the current social media information, and the relationship between the interactive users and the associated users. The determination module may be configured to determine an influence score for each user in the user relationship network based on the user relationship network.
In some embodiments, the interactive content includes: interactive information and recognition; the mass calculation unit 704 includes: an output module (not shown), and an input module (not shown). The output module can be configured to input the interaction information of each user into the trained emotion analysis model, and obtain a probability value that the interaction information of each user output by the emotion analysis model is forward content. The input module can be configured to input the probability value and the recognition degree of the forward content into a quality score formula, and the quality score of the interactive content of each user is calculated; the formula of mass fraction: sX,A＝Min(1.0,PX,A-μ+LX,Aε) of which SX,AA quality score of the interactive content for user X; l isX,AEndorsement of user X to current social media information A advertisement, LX,A0 denotes no approval, LX,A1 indicates approval; pX,A∈[0,1.0]A probability value representing that the interaction information of the user X is forward content; μ and ε are the specified coefficients.
In some embodiments, the interactive content includes: a recognition degree; the quality calculating unit 704 is further configured to determine, for each user, in response to determining that the user has a recognition degree, the quality score of the interactive content of the user as: 0.5-mu + 1/epsilon, where mu and epsilon are the specified coefficients.
In some embodiments, the above evaluation unit 705 comprises: a multiplication module (not shown), an addition module (not shown). Wherein, the multiplying module can be configured to multiply the influence score of each user with the quality score of the interactive content corresponding to each user, so as to obtain the propagation evaluation score of each user. The addition module may be configured to add the propagation evaluation scores of all users, resulting in a propagation evaluation score for the current social media information.
In some embodiments, the apparatus 700 further comprises: a determination unit (not shown in the figure). The determining unit may be configured to determine a core user among the users based on the influence scores of the respective users, the quality scores of the interactive contents of the respective users.
In some embodiments, the determining unit includes: a get module (not shown), a ranking module (not shown). Wherein the obtaining module may be configured to multiply the influence score of each user with the quality score of the interactive content corresponding to each user to obtain the propagation evaluation score of each user. The ranking module may be configured to rank the user with the highest propagation evaluation score among all users as a core user among the users.
In the social media information propagation evaluation device provided by the embodiment of the present disclosure, first, the obtaining unit 701 obtains users related to current social media information and relationships between the users; thereafter, the influence calculation unit 702 calculates the influence score of each user based on each user and the relationship between each user; then, the interaction unit 703 acquires interaction content of each user related to the current social media information based on the relationship between each user; then, the quality calculating unit 704 calculates the quality score of the interactive content of each user based on the interactive content of each user; finally, the evaluation unit 705 calculates a propagation evaluation score of the current social media information based on the influence score of each user and the quality score of the interactive content of each user. Therefore, information propagation power and propagation quality are comprehensively considered for the propagation of the social media information, invalid interaction of false accounts and inactive accounts can be avoided, the propagation power of the social media advertisement is more truly evaluated, and the propagation effect of the social media information is effectively measured.
Referring now to FIG. 8, shown is a schematic diagram of an electronic device 800 suitable for use in implementing embodiments of the present disclosure.
As shown in fig. 8, an electronic device 800 may include a processing means (e.g., central processing unit, graphics processor, etc.) 801 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage means 808 into a Random Access Memory (RAM) 803. In the RAM803, various programs and data necessary for the operation of the electronic apparatus 800 are also stored. The processing apparatus 801, the ROM 802, and the RAM803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
Generally, the following devices may be connected to the I/O interface 805: input devices 806 including, for example, a touch screen, touch pad, keyboard, mouse, etc.; an output device 807 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage 808 including, for example, magnetic tape, hard disk, etc.; and a communication device 809. The communication means 809 may allow the electronic device 800 to communicate wirelessly or by wire with other devices to exchange data. While fig. 8 illustrates an electronic device 800 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided. Each block shown in fig. 8 may represent one device or may represent multiple devices as desired.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication means 809, or installed from the storage means 808, or installed from the ROM 802. The computer program, when executed by the processing apparatus 801, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It should be noted that the computer readable medium of the embodiments of the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In embodiments of the disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In embodiments of the present disclosure, however, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the server; or may exist separately and not be assembled into the server. The computer readable medium carries one or more programs which, when executed by the server, cause the server to: acquiring users related to the current social media information and relations among the users; calculating the influence scores of the users based on the users and the relations among the users; acquiring interactive content of each user related to the current social media information based on the relationship among the users; calculating the quality scores of the interactive contents of the users based on the interactive contents of the users; and calculating the propagation evaluation score of the current social media information based on the influence score of each user and the quality score of the interactive content of each user.
Computer program code for carrying out operations for embodiments of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor comprises an acquisition unit, an influence calculation unit, an interaction unit, a quality calculation unit and an evaluation unit. Where the names of these elements do not in some cases constitute a limitation on the elements themselves, for example, the obtaining element may also be described as an element configured to obtain users related to current social media information and relationships between respective users.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the invention in the embodiments of the present disclosure is not limited to the specific combination of the above-mentioned features, but also encompasses other embodiments in which any combination of the above-mentioned features or their equivalents is made without departing from the inventive concept as defined above. For example, the above features and (but not limited to) technical features with similar functions disclosed in the embodiments of the present disclosure are mutually replaced to form the technical solution.
Claims (12)
1. A social media information dissemination assessment method, the method comprising:
acquiring users related to the current social media information and relations among the users;
calculating the influence scores of the users based on the users and the relations among the users;
acquiring interactive content of each user related to the current social media information based on the relationship among the users;
calculating the quality scores of the interactive contents of the users based on the interactive contents of the users;
and calculating the propagation evaluation score of the current social media information based on the influence score of each user and the quality score of the interactive content of each user.
2. The method of claim 1, wherein the respective users comprise: an interactive user related to current social media information and an associated user related to the interactive user; the relationship among the users comprises: a relationship between the interactive user and the current social media information, and a relationship between the interactive user and the associated user;
the calculating the influence score of each user based on each user and the relationship between each user comprises:
establishing a user relationship network based on the interactive user, the associated user, the relationship between the interactive user and the current social media information, and the relationship between the interactive user and the associated user;
based on the user relationship network, determining the influence scores of the users in the user relationship network.
3. The method of claim 1, wherein the interactive content comprises: interactive information and recognition; the calculating the quality scores of the interactive contents of the users based on the interactive contents of the users comprises the following steps:
inputting the interaction information of each user into the trained emotion analysis model to obtain the probability value of forward content of the interaction information of each user output by the emotion analysis model;
inputting the probability value and the recognition degree of the forward content into a quality score formula, and calculating to obtain the quality score of the interactive content of each user;
the mass fraction formula is as follows: sX,A＝Min(1.0,PX,A-μ+LX,Aε) of which SX,AA quality score of the interactive content for user X; l isX,AEndorsement of user X to current social media information A advertisement, LX,A0 denotes no approval, LX,A1 indicates approval; pX,A∈[0,1.0]A probability value representing that the interaction information of the user X is forward content; μ and ε are the specified coefficients.
4. The method of claim 1, wherein the interactive content comprises: indicating approval; the calculating the quality scores of the interactive contents of the users based on the interactive contents of the users comprises the following steps:
for each user, in response to determining that the user represents approval, determining the quality score of the interactive content of the user as: 0.5-mu + 1/epsilon, where mu and epsilon are the specified coefficients.
5. The method of claim 1, wherein calculating a propagation evaluation score for current social media information based on the influence score of each user and the quality score of the interactive content of each user comprises:
multiplying the influence score of each user with the quality score of the interactive content corresponding to each user to obtain the propagation evaluation score of each user;
and adding the propagation evaluation scores of all the users to obtain the propagation evaluation score of the current social media information.
6. The method of one of claims 1-5, further comprising: and determining a core user in the users based on the influence scores of the users and the quality scores of the interactive contents of the users.
7. The method of claim 6, wherein determining a core user of the users based on the influence scores of the respective users and the quality scores of the interactive contents of the respective users comprises:
multiplying the influence score of each user with the quality score of the interactive content corresponding to each user to obtain the propagation evaluation score of each user;
and taking the user with the highest propagation evaluation score in all users as a core user in the users.
8. A social media information dissemination assessment apparatus, the apparatus comprising:
an acquisition unit configured to acquire users related to current social media information and relationships between the users;
an influence calculation unit configured to calculate an influence score of each user based on each user and a relationship between each user;
the interaction unit is configured to acquire interaction content of each user related to the current social media information based on the relationship among the users;
a quality calculation unit configured to calculate a quality score of the interactive content of each user based on the interactive content of each user;
and the evaluation unit is configured to calculate the propagation evaluation score of the current social media information based on the influence score of each user and the quality score of the interactive content of each user.
9. The apparatus of claim 8, wherein the respective users comprise: an interactive user related to current social media information and an associated user related to the interactive user; the relationship among the users comprises: a relationship between the interactive user and the current social media information, and a relationship between the interactive user and the associated user; the influence calculation unit includes:
a networking module configured to establish a user relationship network based on the interactive user, the associated user, a relationship between the interactive user and the current social media information, a relationship between the interactive user and the associated user;
a determination module configured to determine an influence score for each user in the user relationship network based on the user relationship network.
10. The apparatus of claim 8, wherein the interactive content comprises: interactive information and recognition; the mass calculation unit includes:
the output module is configured to input the interaction information of each user into the trained emotion analysis model, and obtain a probability value of forward content of the interaction information of each user output by the emotion analysis model;
the input module is configured to input the probability value and the recognition degree of the forward content into a first quality score formula, and the quality score of the interactive content of each user is obtained through calculation;
the mass fraction formula is as follows: sX,A＝Min(1.0,PX,A-μ+LX,Aε) of which SX,AA quality score of the interactive content for user X; l isX,AEndorsement of user X to current social media information A advertisement, LX,A0 denotes no approval, LX,A1 indicates approval; pX,A∈[0,1.0]A probability value representing that the interaction information of the user X is forward content; μ and ε are the specified coefficients.
11. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
12. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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