Large-scale social network influence prediction system and method

Abstract

The invention relates to the technical field of social network analysis and influence prediction, and discloses a large-scale social network influence prediction system and method.The large-scale social network influence prediction method comprises the steps that a multi-language knowledge graph alignment system is constructed, and accurate mapping of cross-language concept nodes is achieved; constructing a culture vector space representation system, and extracting culture features from the social network user behavior data; the resonance intensity calculation between the content and the culture vector is realized, and the resonance intensity of the content in a specific culture environment is quantified; realizing culture gene transmission dynamics simulation, decomposing the content into transmissible culture gene units, and simulating the transmission process of the culture gene units; fusing prediction results to realize accurate influence evaluation; the technical problems that an existing social network influence prediction technology is inaccurate in prediction in a cross-language environment and neglects a culture resonance effect and culture dynamics are solved, and more accurate prediction support is provided for applications such as social media marketing and public opinion analysis.

Description

Technical Field

[0001] The present invention relates to the technical field of social network analysis and influence prediction, and more particularly, to a large-scale social network influence prediction system and method. Background Art

[0002] With the globalization of social networks, information is increasingly disseminated across languages and cultures. Accurately predicting the global influence of content has become a key technical requirement in areas such as social media marketing, public opinion monitoring, and information dissemination. However, existing social network influence prediction technologies have the following main technical issues: Existing technologies generally face cross-language barriers. Traditional influence prediction models are typically limited to a single language environment and rely on language-specific text features and language models. When information travels across language boundaries, existing models struggle to effectively capture and understand the semantic connections between different languages, resulting in a significant decrease in prediction accuracy in cross-language environments.

[0003] Existing technologies ignore the cultural resonance effect. Users from different cultural backgrounds will have different resonance reactions to the same content. Existing models treat users as a homogenous group and fail to fully consider the regulatory role of cultural factors on information acceptance and dissemination, resulting in significant prediction bias in cross-cultural environments.

[0004] Existing technologies lack the ability to capture the dynamic nature of culture. Existing cultural computational models typically treat culture as a static feature vector, failing to capture the dynamic evolution of memes (transmissible units within culture) during transmission. This makes it difficult to accurately predict the spread, variation, and adaptation of information across different cultural contexts.

[0005] Therefore, a social network influence prediction method that can effectively solve the above technical problems is needed to achieve high-precision prediction in a cross-language and cross-cultural environment. Summary of the Invention

[0006] The present invention provides a large-scale social network influence prediction system and method to solve the technical problem in related technologies of inaccurate influence prediction in a cross-cultural environment of social networks due to cross-language barriers, lack of cultural resonance and neglect of cultural dynamics.

[0007] The present invention provides a large-scale social network influence prediction method, comprising: Build a multilingual knowledge graph alignment system to achieve accurate mapping and semantic bridging of cross-language concept nodes; Based on multilingual knowledge graphs, a cultural vector space representation system is constructed to extract cultural features from social network user behavior data and form interpretable cultural dimensions. Using the cultural vector space, we can calculate the resonance strength between content and cultural vectors, and quantify the resonance strength of content in a specific cultural environment. Based on the results of resonance intensity calculation, the dynamics of cultural gene transmission is simulated, the content is decomposed into spreadable cultural gene units and their transmission process among different cultural groups is simulated; Integrate simulation results and fuse prediction results to achieve accurate impact assessment, and provide high-precision prediction results through multi-dimensional impact indicator integration and visual analysis.

[0008] In a preferred embodiment, the construction of a multilingual knowledge graph alignment system includes: Use multilingual entity representation learning algorithms to process multi-source language data and construct an initial multilingual knowledge graph; The entity alignment algorithm is applied to achieve accurate mapping of cross-language concept nodes. The similarity calculation formula of the entity alignment algorithm is: ; in Represents the similarity of the entity alignment algorithm, and Represents the concept nodes in the first language and the second language respectively, and Represent entities separately and The corresponding entity embedding vector, represents the cosine similarity function, 、 Represent entities separately 、 The set of neighbor nodes of 、 Control the importance of vector similarity and structural similarity in the total similarity respectively, represents the intersection, represents a union; Build a semantic bridging layer to unify the conceptual expressions in different languages.

[0009] In a preferred embodiment, the construction of the culture vector space representation system includes: The user behavior data is processed using a multimodal data fusion algorithm to extract implicit cultural features. The user behavior data includes text content, interaction patterns, content preferences, time patterns, and social network structure. The implicit cultural features are expressed as: ; in, Represents a user The cultural representation vector, represents the feature vector extracted from the user text content, represents the feature vector extracted from user interaction behavior, represents the feature vector extracted from the user's social structure, represents the feature vector extracted from the user's time pattern, represents the multimodal feature fusion function, which is used to integrate feature vectors from different sources into a unified cultural representation; Apply unsupervised clustering algorithm to construct cultural vector space; Extract core cultural dimensions to form an interpretable cultural vector space.

[0010] In a preferred embodiment, the calculation of the resonance strength between the implementation content and the cultural vector includes: Use multimodal content feature extraction algorithms to process social network content and generate content feature vectors; Apply attention mechanism algorithm to achieve matching of content features and cultural dimensions; A cultural resonance intensity function is constructed to calculate the degree of resonance between the content and a specific cultural environment. The cultural resonance intensity function is defined as: ; in, represents the cultural resonance intensity function, Represents the projection component of the content in each cultural dimension, A vector representation of a specific cultural environment, is the similarity function, is the weight parameter, indicating the The importance of each cultural dimension reflects the differentiated influence of different cultural dimensions in resonance calculation. Indicates the total number of cultural characteristics considered.

[0011] In a preferred embodiment, the implementation of the cultural gene propagation dynamics simulation includes: Applying meme decomposition algorithms to break down content into basic, spreadable units; Construct a meme fitness function to evaluate the potential of memes to spread in a specific cultural environment; The replication mutation selection algorithm is applied to simulate the process of meme propagation among different groups. The replication phase simulates the process of users contacting and sharing memes. The replication probability is expressed as: ; in, Represents a user Copying cultural genes The probability of is the sigmoid function, Represents cultural genes In the user adaptability in the cultural context, Represents a user The cultural vector representation of It indicates the inherent sharing behavior habits of users independent of cultural factors; Construct a cultural immunity mechanism model to quantify the acceptance threshold of different cultures for external information.

[0012] In a preferred embodiment, the large-scale social network influence prediction method further includes: Build a platform-aware multi-source heterogeneous knowledge fusion system to achieve cross-platform knowledge representation and semantic alignment; Construct a hierarchical cultural interest dual-space representation system to couple user cultural characteristics with interest preferences; Implementing a platform-aware content-culture-interest ternary resonance model to quantify the resonance intensity of content in a specific platform and cultural environment. The ternary resonance intensity function is defined as: ; in, represents the triple resonance intensity, represents the content feature vector, represents the culture vector, represents the interest vector, represents the platform feature vector, 、 、 Represent the weight coefficients of content culture resonance relationship, content interest resonance relationship, and cultural interest resonance relationship respectively, 、 、 Respectively represent the resonance strength between content and culture, content and interest, and culture and interest; Build an accelerated hierarchical propagation dynamics simulation system that improves the efficiency of large-scale simulations through network layering and approximate computing; Implement a comprehensive cross-platform influence assessment and prediction system that analyzes the synergistic effects of content dissemination across multiple platforms.

[0013] In a preferred embodiment, the construction of an accelerated hierarchical propagation dynamics simulation system includes: Design stratified sampling and approximate calculation strategies to divide social networks into multiple levels and adopt simulation strategies of different granularity for different levels; Introducing parallel computing and incremental update mechanisms, the network is divided into relatively independent blocks for parallel simulation, and only the part where the network state changes is calculated; Build an adaptive precision control mechanism to dynamically adjust simulation parameters and computing resource allocation according to the real-time requirements and precision requirements of the prediction task.

[0014] In a preferred embodiment, the cross-platform comprehensive influence assessment and prediction system includes: Build a multi-level cascade model that perceives platform characteristics and simulates the flow and transformation of information between different platforms; Design a platform synergy effect evaluation model to analyze the interaction pattern when content is disseminated simultaneously on multiple platforms; Achieve cross-platform influence attribution analysis to identify the contribution of different platforms, different user groups, and different communication paths to the final influence; It supports interactive hypothesis testing and strategy simulation to assist users in planning and optimizing communication strategies.

[0015] In a preferred embodiment, a large-scale social network influence prediction system is used to implement a large-scale social network influence prediction method, characterized in that the system includes: Multilingual knowledge graph alignment module, used to achieve accurate mapping and semantic bridging of cross-language concept nodes; Cultural vector space representation module, which is used to extract cultural features from social network user behavior data and form interpretable cultural dimensions; Resonance intensity calculation module, used to quantify the resonance intensity of content in a specific cultural environment; A module for simulating the dynamics of cultural gene dissemination, which is used to decompose content into disseminable cultural gene units and simulate their dissemination process among different cultural groups; The precise impact assessment module is used to provide high-precision prediction results through the integration and visual analysis of multi-dimensional impact indicators.

[0016] In a preferred embodiment, the large-scale social network influence prediction system further includes: A platform-aware multi-source heterogeneous knowledge fusion module for cross-platform knowledge representation and semantic alignment; A hierarchical cultural interest dual-space representation module is used to couple user cultural characteristics with interest preferences; The platform-perceived content-culture-interest triadic resonance module is used to quantify the resonance intensity of content in a specific platform and cultural environment; An accelerated hierarchical propagation dynamics simulation module, which is used to improve the efficiency of large-scale simulations through network layering and approximate computing; The cross-platform influence comprehensive evaluation and prediction module is used to analyze the synergistic effect of content dissemination on multiple platforms.

[0017] The beneficial effects of the present invention are: By aligning multilingual knowledge graphs and building a semantic bridging layer, cross-language semantic understanding is achieved, effectively breaking through the limitations of language barriers on influence prediction, enabling the system to accurately understand and process multilingual information dissemination phenomena in global social networks.

[0018] Through cultural vector space representation and cultural resonance intensity calculation, the regulatory effect of different cultural backgrounds on content dissemination is accurately quantified, which improves the prediction accuracy in cross-cultural environments, and especially improves the accuracy of predicting the influence of culturally sensitive content.

[0019] Through the simulation of cultural gene propagation dynamics, the dynamic evolution characteristics of information in the process of cross-cultural communication are accurately captured, potential global topics can be discovered in advance, and sufficient time window is provided for timely intervention and guidance of public opinion.

[0020] Through the integration of multi-dimensional influence indicators and precise evaluation mechanisms, comprehensive predictions of multiple dimensions such as communication coverage, penetration depth, and duration are achieved, which improves the interpretability of prediction results and provides more valuable guidance for marketing decisions and public opinion responses.

[0021] By identifying the optimal cultural entry points and communication paths, it provides a scientific basis for cross-cultural content communication strategies, improves content communication efficiency, reduces global marketing costs, and increases target audience interaction rates. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a flow chart of a large-scale social network influence prediction method of the present invention; Figure 2 This is a module diagram of a large-scale social network influence prediction system of the present invention. DETAILED DESCRIPTION

[0023] The subject matter described herein will now be discussed with reference to example embodiments. It should be understood that these embodiments are discussed solely to enable those skilled in the art to better understand and implement the subject matter described herein, and that the functions and arrangements of the elements discussed may be varied without departing from the scope of this specification. Various examples may omit, substitute, or add various processes or components as needed. Furthermore, features described in some examples may be combined in other examples.

[0024] At least one embodiment of the present invention discloses a large-scale social network influence prediction method. Figures 1 to 2 As shown, the following steps are included: Implementation method 1: Step 1: Build a multilingual knowledge graph alignment system to achieve accurate mapping and semantic bridging of cross-language concept nodes; It includes the following sub-steps: Step 1.1: Use a multilingual entity representation learning algorithm to process multi-source language data and construct an initial multilingual knowledge graph; First, text data in multiple languages is collected from social networking platforms, including user posts, interactive comments, and hashtags. A multilingual entity recognition algorithm is then applied to extract entities in each language while preserving the semantic relationships between them. Next, a multilingual representation learning algorithm is used to map the entities and relationships in different languages into a unified vector space, generating entity embedding representations. This multilingual representation learning algorithm is based on a transformation matrix learning approach, which achieves cross-lingual semantic alignment by learning linear mappings between embedding spaces in different languages. The final output is an initial multilingual knowledge graph containing cross-lingual entity nodes and semantic relationship edges.

[0025] Step 1.2: Apply entity alignment algorithm to achieve accurate mapping of cross-language concept nodes; This sub-step achieves accurate mapping of concept nodes in knowledge graphs of different languages. Specifically, we first calculate the similarity between entity feature vectors using the following formula: ; in, Represents the similarity of the entity alignment algorithm, and Represents the concept nodes in the first language and the second language respectively, and Represent entities separately and The corresponding entity embedding vector, represents the cosine similarity function, 、 Represent entities separately 、 The set of neighbor nodes of 、 Control the importance of vector similarity and structural similarity in the total similarity respectively, represents the intersection, represents a union; Combined with the relationship triple matching method, the accuracy of entity alignment is further improved by analyzing the structural similarity of the relationship triples involved in the entities. Specifically, for two entities to be aligned and , check whether their relationships with other aligned entities are consistent, expressed as: ; in, Representing an entity and The matching score between and Respectively represent 、 The knowledge graph of a language, Represents a relation triple, is the head entity, is the relationship type, is the tail entity, is a relationship similarity function used to evaluate two relationships and The semantic similarity of is in the range of [0, 1]. To judge the entity and An indicator function that indicates whether the data is aligned. If aligned, it returns 1; otherwise, it returns 0.

[0026] Through iterative optimization, the entity alignment results are continuously updated until the convergence conditions are reached, and the final cross-language entity alignment mapping relationship is output.

[0027] Step 1.3: Build a semantic bridge layer to unify the concept expressions in different languages; Based on the entity alignment results, a semantic bridging layer is constructed to unify conceptual representations. Specifically, a unified concept node is created for each pair of aligned entities, preserving their original expressions and specific attributes in their respective languages. At the same time, hierarchical relationships and semantic connections are established between concepts, forming a unified cross-lingual conceptual system.

[0028] The semantic bridging layer implements the mapping and conversion of concepts in different languages through the following formula: ; in, Presentation Language Concepts in , Presentation Language Concepts in , Indicates that it is mapped to a language The corresponding concept The conversion function, Representation Concept In language The embedding representation in It is a semantic bridge function responsible for realizing the mapping and conversion of cross-language concepts.

[0029] The final output of the multilingual knowledge graph alignment system includes aligned cross-language concept nodes, semantic relationships, and semantic bridging layers, providing basic knowledge support for subsequent cross-language social network influence prediction.

[0030] Step 2: Based on the multilingual knowledge graph, a cultural vector space representation system is constructed to extract cultural features from social network user behavior data and form interpretable cultural dimensions. It includes the following sub-steps: Step 2.1: Use multimodal data fusion algorithm to process user behavior data and extract implicit cultural features; Collect multimodal user behavior data in social networks, including text content, interaction patterns (such as likes, comments, forwarding, etc.), content preferences, time patterns, and social network structure.

[0031] Multimodal deep learning methods are applied to process and fuse these heterogeneous data to extract the implicit cultural characteristics contained in user behaviors.

[0032] Specifically, for text data, a cross-language semantic analysis model is used to extract features such as topic preferences, expressions, and emotional tendencies; for interactive behavior data, a user content interaction matrix is constructed, and matrix decomposition and neural collaborative filtering algorithms are applied to mine user interaction patterns; for social network structure data, graph neural networks are applied to analyze user social relationships and information dissemination paths.

[0033] A multimodal feature fusion network is designed to integrate various features into a unified user culture representation vector, which is expressed as: ; middle, Represents a user The cultural representation vector, represents the feature vector extracted from the user text content, represents the feature vector extracted from user interaction behavior, represents the feature vector extracted from the user's social structure, represents the feature vector extracted from the user's time pattern, Represents a multimodal feature fusion function, which is used to integrate feature vectors from different sources into a unified cultural representation.

[0034] Step 2.2, apply unsupervised clustering algorithm to construct cultural vector space; Based on user cultural representation vectors, an unsupervised clustering algorithm is applied to culturally group users and construct a cultural vector space. Specifically, dimensionality reduction techniques (such as t-SNE or Unified Mapping) are used to reduce the high-dimensional cultural representation vectors to facilitate visualization and subsequent analysis. An improved hierarchical clustering algorithm is then used to adaptively determine the optimal number of clusters to form meaningful cultural group divisions.

[0035] The clustering process can be expressed as finding the optimal cultural group division , so that the objective function is minimized: ; in, Indicates the division of cultural groups. 、 、 Respectively represent 、 、 cultural groups, represents the total number of cultural groups divided, Represents finding the optimal cultural group division , so that the value of the entire objective function is minimized, Indicates the The center vector of each cultural group, Represents the square of the Euclidean distance between the user's cultural vector and the group center vector, which is used to measure the matching degree between the user and the cultural group. is a regularization term used to control the complexity of the clustering results. is a trade-off parameter.

[0036] Step 2.3: extract the core cultural dimensions and form an interpretable cultural vector space; Specifically, we conduct statistical analysis on the characteristic distribution of each cultural group to identify the key dimensions that significantly distinguish different cultural groups; combine cultural theory knowledge (such as Hofstede's cultural dimension theory) to name and explain these key dimensions, forming semantically meaningful cultural dimensions such as individualism and collectivism tendencies, power distance, and uncertainty avoidance; map users onto these cultural dimensions to generate a structured cultural vector representation.

[0037] The cultural vector space can be expressed as ,in, represents the cultural vector space, 、 、 Respectively represent 、 、 A cultural dimension with specific semantic meaning, Represents the total number of dimensions of the culture vector space.

[0038] user The representation in the cultural vector space is: ; in, Represents a user The cultural vector representation of 、 、 Represents users In the 、 、 The scores on the cultural dimensions, Represents the number of dimensions of the culture vector space.

[0039] The final output cultural vector space representation system includes cultural dimension definition, user cultural vector representation, and cultural group division, providing support for subsequent cultural resonance intensity calculation and influence prediction.

[0040] Step 3: Using the cultural vector space, we calculate the resonance strength between the content and the cultural vector, and quantify the resonance strength of the content in a specific cultural environment. It includes the following sub-steps: Step 3.1, using a multimodal content feature extraction algorithm to process social network content and generate a content feature vector; Specifically, it involves collecting multimodal content data from social networks, including text, images, videos, and audio. For content in different modalities, corresponding feature extraction models are applied: for text content, a deep semantic understanding model is used to extract features such as theme, sentiment, and style; for image content, a convolutional neural network is used to extract visual features; for video content, a spatiotemporal feature extraction model is combined to capture dynamic information; and for audio content, an audio feature extraction model is used to obtain acoustic features.

[0041] The multimodal features are fused to generate a unified content feature vector, which is expressed as: ; in, Display content The eigenvector of 、 、 and Represent the feature vectors extracted from text, image, video and audio respectively, Represents the content feature fusion function, which is responsible for integrating the feature vectors of different modalities into a unified representation.

[0042] Step 3.2: Apply the attention mechanism algorithm to match content features with cultural dimensions; Establish a matching relationship between content features and cultural dimensions, and determine the relevance of content in each cultural dimension. Specifically, first design an attention mechanism to calculate the correlation strength between content features and each cultural dimension: ; in, Indicates the content and The attention weight of each cultural dimension, is the scoring function, which calculates the matching degree between the content feature vector and the cultural dimension vector. A feature vector representing the content, 、 Respectively represent 、 cultural dimension vectors, represents the total number of cultural dimensions, represents the natural exponential function.

[0043] Based on the attention weights, the projection representation of the generated content in the cultural vector space is: ; in, Display content Projection representation in the culture vector space, is the attention weight calculated in the previous step, For the cultural dimension vectors, is the total number of cultural dimensions.

[0044] Step 3.3: Construct a cultural resonance intensity function to calculate the degree of resonance between the content and the specific cultural environment; The cultural resonance intensity function is defined as: ; in, represents the cultural resonance intensity function, Represents the projection component of the content in each cultural dimension, A vector representation of a specific cultural environment, is the similarity function, is the weight parameter, indicating the The importance of each cultural dimension reflects the differentiated influence of different cultural dimensions in resonance calculation. Indicates the total number of cultural characteristics considered.

[0045] Weight parameter This can be optimized by: ; in, is the weight parameter, is the importance evaluation function, For historical dissemination data, 、 Respectively represent 、 cultural dimension vectors, is the total number of cultural dimensions, is a natural exponential function. By analyzing the impact of each cultural dimension on the content dissemination effect in historical data, the weight of each dimension is adaptively adjusted.

[0046] The resonance intensity function can be further refined for different types of content and cultural environments: ; in, A resonance strength function representing a specific type of content, represents the fundamental resonance intensity function, Indicates a specific resonance calculation method for this type of content. is the balance parameter, represents the content feature vector, represents the cultural environment vector, Indicates the type of content.

[0047] The final output of the cultural resonance intensity calculation system can accurately quantify the degree of resonance of any content in a specific cultural environment, providing key input for subsequent influence prediction.

[0048] Step 4: Based on the resonance strength calculation results, the cultural gene propagation dynamics simulation is implemented to decompose the content into spreadable cultural gene units and simulate their propagation process among different cultural groups; It includes the following sub-steps: Step 4.1: Apply the meme decomposition algorithm to break the content into basic units that can be spread; The concept of meme is introduced to break down complex content into basic units with independent spreadability. Specifically, for text content, it can be broken down into key topics, viewpoints, expressions, or emotions based on semantic integrity and expression independence; for multimedia content, key visual elements, plot fragments, or sound units can be extracted.

[0049] The meme decomposition algorithm is implemented through the following steps: applying a semantic segmentation model to identify key components in the content; evaluating the independent dissemination potential of each component and screening out basic units with sufficient influence and self-replication capabilities; assigning a unique identifier to each meme and building an association relationship with the original content.

[0050] The process of cultural gene decomposition can be expressed as: ; in, Display content The decomposed cultural gene set, It is the cultural gene decomposition function, responsible for splitting the content into basic communication units. 、 、 Respectively represent 、 、 Cultural gene unit, Represents the total number of cultural genes obtained by decomposition.

[0051] Step 4.2: Construct a meme fitness function to evaluate the potential of memes to spread in a specific cultural environment. The meme fitness function is defined as: ; in, Represents cultural genes In the cultural environment The fitness in Indicates the evaluation factors, Indicates the The importance of the evaluation factor, represents the total number of evaluation factors; Specific evaluation factors include: Cultural resonance: the degree of resonance between the content of the cultural gene and the cultural environment; Novelty: the freshness of the meme in the target cultural environment; Transmissibility: the simplicity, memorability and replicability of cultural genes; Emotional arousal: the intensity of the emotional response induced by the cultural gene; Utility value: the practical or entertainment value of the meme in the target culture; Cultural compatibility: The degree to which the cultural meme is compatible with the values of the target culture.

[0052] Weight coefficient These weights can be automatically learned from historical communication data through machine learning methods or pre-set based on expert knowledge. These weight coefficients represent the relative importance of each evaluation factor in the fitness calculation, reflecting the contribution of each factor to the communication potential in different cultural environments.

[0053] Step 4.3: Apply the replication mutation selection algorithm to simulate the spread of cultural genes among different groups; The replication phase simulates the process of users encountering and sharing memes. The replication probability is related to the meme's fitness and user characteristics: ; in, Represents a user Copying cultural genes The probability of is the sigmoid function, Represents cultural genes In the user The fitness under the cultural vector background, is the user characteristic bias item.

[0054] The mutation phase simulates the changes in the content of a meme during its dissemination process. Mutation operations include content modification, combination, simplification, or expansion: ; in, For the mutated cultural gene, For the original cultural gene, is the mutation operation function, is the variation function, according to the user The characteristics and cultural environment of the content produce corresponding variation effects, indicating the amount of personalized adaptation of the original content by the user.

[0055] The selection phase simulates the competition process in a social network environment, where cultural genes with high fitness gain more opportunities to spread: ; in, Represents cultural genes The probability of being selected in the competition, 、 Represents original cultural genes , the mutated cultural genes In the cultural environment The fitness in is the set of all cultural genes in the current environment, and the denominator represents the exponential sum of the fitness of all competing cultural genes, which implements softmax normalization. Represents the exponential function.

[0056] By iteratively applying replication, mutation and selection operations, we simulate the spatiotemporal evolution of cultural genes in the network and predict their propagation paths, coverage and duration.

[0057] Step 4.4: Construct a cultural immunity mechanism model to quantify the acceptance threshold of different cultures for external information; The cultural immunity mechanism is modeled from two dimensions: The cultural acceptance threshold indicates the degree of openness of the cultural environment to external information: ; in, Represents cultural environment The acceptance threshold, is the threshold calculation function, It is the culture's historical contact experience, including the historical records and response patterns of the culture's past exposure to foreign information.

[0058] The strength of cultural defense indicates the degree to which the cultural environment resists external information: ; in, Represents cultural environment Cultural genes The defensive strength, is the defense strength calculation function, Represents cultural genes and cultural environment A collection of cultural genes touched by history The similarity reflects the degree of correlation between new information and historical experience.

[0059] Combining the cultural acceptance threshold and defense strength, the probability of a cultural gene successfully entering the target cultural environment can be calculated: ; in, Represents cultural environment Accept cultural genes The probability of is the sigmoid function, For cultural genes In the cultural environment The fitness in Represents cultural environment The acceptance threshold, Represents cultural environment Cultural genes The entire formula represents the degree to which the cultural gene fitness exceeds the sum of the cultural acceptance threshold and the defense strength, and is converted into the acceptance probability.

[0060] The final output of the cultural gene communication dynamics simulation system can accurately capture the dynamic evolution characteristics of content in the process of cross-cultural communication, predict the content communication path, variation rules, communication rate and coverage, and provide dynamic support for accurate prediction of influence.

[0061] Step 5: Integrate simulation results and fuse prediction results to achieve accurate impact assessment. High-precision prediction results are provided through multi-dimensional impact indicator integration and visual analysis. It includes the following sub-steps: Step 5.1: Construct a multi-level cascade model to simulate the propagation path of cultural genes in the network; Specifically, a social network is represented as a directed graph ,in, represents a directed graph of a social network, Represents a set of user nodes, Indicates the attention or interaction relationship between users.

[0062] For each user node , according to its culture vector And the social influence index, defining the probability of its influence on neighbor nodes: ; in, Represents a user Impact on users The probability of To influence the probability function, we evaluate the compatibility and interaction potential of two users’ cultural vectors. and Represents users and users The cultural vector, Represents a user and The strength of social relationships between people includes factors such as interaction frequency and relationship closeness.

[0063] Based on the characteristics of the meme and the user's cultural background, the activation probability of the meme spreading among users is defined as follows: ; in, Represents cultural genes From the user Spread to users The probability of represents the influence probability between users, Represents a user Acceptance of cultural genes based on their cultural background probability.

[0064] Through multiple rounds of iterative simulation, the propagation path and coverage of cultural genes in social networks are predicted.

[0065] Step 5.2: Apply the spatiotemporal evolution prediction algorithm to evaluate the temporal and geographical distribution of influence; In the time dimension, a time series model of influence propagation is constructed: ; in, 、 Respectively Moment and The state of each node in the network at any moment, express The network topology at the moment, express The cultural distribution status at the moment, is the time evolution function, which describes how the system state evolves over time.

[0066] In the spatial dimension, we combine the user's geographic location information and regional cultural characteristics to build a spatial communication model: ; in, express Geographical distribution map of the time, express The state of each node in the network at any moment, Indicates the user's geographic location information. Represents regional cultural characteristics, is a spatial distribution function that maps the network status to the geographic space.

[0067] Through the spatiotemporal evolution model, we can predict the temporal change trend of content influence (such as propagation speed, peak time, attenuation cycle, etc.) and geographical distribution characteristics (such as hot spots, propagation boundaries, regional differences, etc.).

[0068] Step 5.3: Integrate multi-source influence indicators and build a comprehensive evaluation model; Specifically, we first define the following impact assessment dimensions: Reach: the number and diversity of users reached by the content; Depth of interaction: the degree of user interaction with content (such as likes, comments, and reposts); Emotional intensity: the strength of the emotional response elicited by the content; Propagation speed: the rate at which content spreads across the network; Duration: How long the content’s impact lasts; Cultural penetration: the ability of content to transcend different cultural groups; For each dimension, design corresponding evaluation indicators and calculation methods. For example, for coverage, you can define: ; in, Display content Coverage indicators, Represents a user Whether the content The indicator function of activation (value 0 or 1), User weight, reflecting the user Importance or representativeness in the network, Represents the set of all user nodes.

[0069] Integrate indicators from various dimensions to build a comprehensive impact assessment model: ; in, Display content The comprehensive impact index, Indicates the The evaluation indicators of the dimensions, Indicates the The relative importance of each dimension in the comprehensive evaluation Indicates the total number of evaluation dimensions.

[0070] Weight coefficient It can be set through historical data analysis and expert knowledge, or it can be automatically adjusted according to different scenarios using adaptive methods to adapt to changes in the relative importance of indicators in various dimensions in different application environments.

[0071] Step 5.4: Visualize and interactively analyze the prediction results. The system includes the following visualization modules: Communication path diagram: shows the content's communication path in social networks, including key nodes, communication direction, and communication intensity; Time evolution curve: shows the changing trend of influence over time, including growth period, peak period and decay period; Geographic heat map: shows the distribution of content influence in different geographical areas; Cultural penetration map: shows the penetration of content among different cultural groups; Multi-dimensional radar chart: Compare the performance of content in different influence dimensions.

[0072] At the same time, it provides interactive analysis functions, allowing users to perform the following operations: adjust content feature parameters to observe the impact on prediction results; modify network structure or cultural distribution to conduct hypothetical scenario analysis; select specific cultural groups or geographical regions to view local prediction results; compare the influence prediction results of multiple contents; set custom evaluation indicators and weights Through visualization and interactive analysis, users can gain a deeper understanding of prediction results and provide decision support for content optimization and communication strategy formulation.

[0073] The final output of the precise influence prediction system can comprehensively consider factors such as cross-language understanding, cultural resonance intensity, and cultural gene propagation dynamics, and achieve high-precision prediction of content influence in large-scale social networks, meeting the precise prediction needs in a global communication environment.

[0074] Implementation 2: This implementation is applicable to influence prediction scenarios within highly heterogeneous cross-platform social network environments, such as collaborative information dissemination analysis across different social media platforms, user influence assessment in the entertainment industry, and government public opinion monitoring and early warning systems. In these complex and ever-changing scenarios, information dissemination must overcome multiple limitations, such as differences in platform mechanisms, heterogeneous user groups, and diverse forms of information expression, to achieve accurate cross-platform collaborative influence assessment and prediction.

[0075] This embodiment further solves three core technical problems based on embodiment 1: The challenge of adapting to platform heterogeneity: user behavior patterns, content distribution mechanisms, and interaction methods vary significantly across different social platforms, making it difficult for traditional influence prediction models to effectively adapt to a multi-platform environment. It is difficult to map user identities across platforms. There are technical barriers to identifying and associating the same user's identity with their behavior on different platforms, which hinders the accurate tracking of cross-platform communication paths. The real-time requirements are increasing, and large-scale social network environments have put forward higher requirements for the balance between computing efficiency and accuracy of real-time predictions.

[0076] The method of this embodiment includes the steps of: Based on the five basic steps of Implementation Method 1, this implementation method achieves the following optimizations through improvement and expansion: Step 100: Building a platform-aware multi-source heterogeneous knowledge fusion system; This step introduces platform feature modeling based on the multilingual knowledge graph alignment in implementation mode 1 to achieve cross-platform knowledge fusion.

[0077] It includes the following sub-steps: Step 101: Design a platform characteristic representation model to capture the structural and mechanism characteristics of different social platforms; Specifically, for each social platform , construct the feature vector: ; in, 、 、 Representation Platform In the 、 、 The values of each characteristic dimension include quantitative indicators such as algorithm recommendation strength, social network density, content display mechanism, and interaction type diversity. Indicates the total number of feature dimensions.

[0078] Step 102: Expand the multilingual knowledge graph into a multimodal, multi-source, heterogeneous knowledge base; Based on the multilingual knowledge graph in implementation 1, the knowledge representation is expanded to a heterogeneous knowledge base that supports multimodal content, achieving a unified representation of multimodal content such as text, images, videos, and audio. At the same time, platform attribution is introduced to add platform source identifiers to knowledge entities and relationships, building: ; in, Represents an extended entity representation, where Represents the entity itself, Represents the entity type, Indicates the platform to which the entity belongs, represents the modal type of the entity, Represents a collection of additional properties of an entity.

[0079] Step 103: Design a cross-platform semantic alignment and knowledge mapping mechanism; This sub-step designs a semantic alignment mechanism for platform differences based on the semantic bridging layer in implementation 1. Specifically, a concept mapping function between platforms is constructed: ; in, Indicates that the platform Concepts in Mapping to Platform The corresponding expression of is the mapping function, Represents the concept to be mapped, Indicates the source platform The characteristic vector of Indicates the target platform The characteristic vector of .

[0080] In addition, a cross-platform user identity mapping system is established to identify the same user on different platforms through behavioral pattern matching, content similarity analysis and social relationship comparison, supporting complete tracking of cross-platform communication paths.

[0081] Step 200, constructing a hierarchical cultural interest dual-space representation system; This step introduces user interest modeling based on the cultural vector space in implementation method 1 to construct a dual-space representation of cultural interests. It specifically includes the following sub-steps: Step 201: construct a fine-grained user interest representation model; This sub-step builds a fine-grained interest representation model by analyzing users' content consumption and interaction behaviors on different platforms. First, define the interest ontology: ; in, Represents interest ontology, 、 、 Respectively represent 、 、 Topics of interest, Indicates the total number of topics of interest.

[0082] For users , construct the interest vector: ; in, Represents a user The interest vector, 、 、 Represents users Topic 、 、 The intensity of interest, Indicates the total number of topics of interest.

[0083] Step 202, realizing dual-space coupled representation of cultural interests; This sub-step couples the cultural vector space in Implementation 1 with the user interest space to construct a unified dual-space representation. , construct a dual space vector: ; in, Represents a user The dual space vector representation of Represents a user The cultural vector representation of Represents a user The interest vector representation.

[0084] At the same time, establish a mapping relationship between culture and interests: ; ; in, Represents the mapping relationship from cultural vector space to interest space, Represents the mapping relationship from interest space to cultural vector space, and They are the mapping functions from culture vector space to interest space and from interest space to culture vector space, represents the cultural vector space, These mappings are used to analyze the impact of cultural background on interest formation and the feedback of interest expression on cultural identity.

[0085] Step 203: Design a cross-platform user behavior consistency evaluation mechanism; This sub-step analyzes the consistency and differences of user behaviors on different platforms and explores the modulation effect of platform characteristics on user behavior expression. , define its The behavior vector on : ; in, Represents a user On the platform The behavior vector on 、 、 Represents users On the platform On the 、 、 Class behavioral characteristics, The total number of dimensions representing behavioral characteristics.

[0086] Calculate the user's behavior consistency index on different platforms: ; in, Represents a user On the platform and Behavioral consistency, Represents the similarity calculation function, 、 Represents users On the platform 、 The behavior vector on .

[0087] Based on behavioral consistency analysis, the modulation effect of platform characteristics on user behavior is identified, providing a cross-platform behavioral pattern reference for subsequent influence prediction.

[0088] Step 300: Implementing a content, culture, and interest ternary resonance model perceived by the platform; This step builds a ternary resonance model based on the calculation of cultural resonance intensity in Implementation 1, by introducing platform characteristics and user interest factors. It specifically includes the following sub-steps: Step 301: Expand content feature extraction to a platform-adapted multimodal representation Based on the multimodal content feature extraction in implementation 1, a platform feature adaptation mechanism is introduced to optimize the feature extraction strategy based on the content format characteristics and display mechanisms of different platforms. Specifically, the content feature representation is expanded to: ; in, Display content On the platform The feature representation on 、 、 and Respectively indicate content Text, image, video and audio features, is the feature fusion function perceived by the platform, Representation Platform The characteristic vector of .

[0089] Step 302, constructing a ternary resonance strength calculation model; This sub-step expands the cultural resonance intensity calculation in Implementation 1 into a content, culture, and interest ternary resonance model. Specifically, the ternary resonance intensity function is defined as: ; in, represents the triple resonance intensity, represents the content feature vector, represents the culture vector, represents the interest vector, represents the platform feature vector, 、 、 Represent the weight coefficients of content culture resonance relationship, content interest resonance relationship, and cultural interest resonance relationship respectively, 、 、 Respectively represent the resonance strength between content and culture, content and interest, and culture and interest; In particular, the intensity of content cultural resonance Based on implementation 1, the platform modulation factor is introduced: ; in, Indicates that on the platform Upper content feature vector and cultural vectors The resonance strength between is the cultural resonance intensity function, is the platform modulation factor, when When the platform It has an enhancing effect on the cultural resonance of content. When the platform It has an inhibitory effect on the cultural resonance of content. When the platform No regulatory effect on content cultural resonance Step 303: Implement cross-platform resonance consistency evaluation and optimization; This sub-step analyzes the differences in resonance intensity of content on different platforms and builds a cross-platform resonance consistency evaluation mechanism. and user groups , compare its performance on different platforms and The difference in resonance intensity on: ; in: Display content For user groups On the platform and The difference in resonance intensity on 、 Display content For user groups On the platform 、 The triple resonance intensity on Represents the vector norm, which is used to calculate the size of the difference.

[0090] Based on resonance difference analysis, the key factors leading to inconsistency are identified, and the parameters of the ternary resonance model are optimized to improve cross-platform prediction consistency.

[0091] Step 400, constructing an accelerated hierarchical propagation dynamics simulation system; This step, based on the meme propagation dynamics simulation in Implementation 1, introduces computational acceleration and layered simulation mechanisms to improve simulation efficiency in large-scale networks. Specifically, it includes the following sub-steps: Step 401: Designing stratified sampling and approximate calculation strategies. This substep uses network stratification and importance sampling techniques to reduce the computational complexity of large-scale network simulations. Specifically, the social network is first stratified: ; in, represents the hierarchical social network set, 、 、 Respectively represent 、 、 layer network, Indicates the total number of layers in the network.

[0092] Different granularity simulation strategies are adopted for different layers: fine-grained individual-level simulation is adopted for the core layer, and coarse-grained group-level simulation is adopted for the edge layer, and the overall propagation process is connected through inter-layer influence transmission.

[0093] Step 402: Introduce parallel computing and incremental update mechanisms; This substep utilizes parallel computing technology and incremental update strategies to further improve simulation efficiency. Parallel computing divides the network into relatively independent blocks, allowing simultaneous simulations on multiple processing units. Incremental updates calculate only the parts of the network that have changed, avoiding repeated calculations across the entire network.

[0094] Step 403: construct an adaptive precision control mechanism; This sub-step establishes an adaptive precision control mechanism to dynamically adjust simulation parameters and computing resource allocation based on the real-time requirements and precision requirements of the prediction task. Specifically, the precision-efficiency balance function is defined as: ; in, represents the accuracy-efficiency balance function, Represents the simulation parameter set, including sampling rate, number of iterations, convergence threshold and other parameters; and Represents parameter sets The prediction accuracy and computational efficiency under is the trade-off coefficient. , achieving the best balance between precision and efficiency.

[0095] Step 500: Implement a cross-platform comprehensive influence assessment and prediction system; This step is based on the precise impact assessment of implementation method 1 and is expanded to support a prediction system that supports cross-platform comprehensive analysis. It specifically includes the following sub-steps: Step 501: construct a multi-level cascade model for platform feature perception; Based on the multi-level cascade model of implementation method 1, a platform conversion layer is introduced to simulate the flow and conversion of information between different platforms. Specifically, the probability of information transmission between platforms is defined as: ; in, Represents cultural genes From the platform Spread to the platform The probability of is the platform conversion function, 、 Respectively represent the platform 、 The characteristic vector includes dimensions such as user group characteristics, content display mechanism, and interaction method.

[0096] Step 502: Design a platform synergy effect evaluation model; This sub-step analyzes the synergistic effects of content when it is disseminated simultaneously on multiple platforms, including various modes such as mutual promotion, mutual inhibition, and mutual independence. Specifically, we construct a platform synergy effect function: ; in, Display content Gather on the platform The synergistic effect on Display content On a single platform The influence on is the synergistic effect function, Represents the set of all platforms involved in the dissemination.

[0097] Step 503: Implement cross-platform influence attribution analysis; This sub-step uses influence attribution analysis to identify the contribution of different platforms, different user groups, and different communication paths to the final influence. Specifically, the Shapley value method is used to calculate the contribution of each factor: ; in, Representation factors The contribution of represents the set of all influencing factors, Representing a collection The number of elements of Representation factor subset The impact value generated, express Medium Any subset outside 、 Represents a set Factorial sum of the number of elements Medium and The factorial of the number of elements outside, Represents the factorial of the number of elements in the set of all influencing factors, Indicates adding factors The marginal increment of the post-impact value, used to quantify the factors independent contribution.

[0098] Based on the attribution analysis results, key impact paths and platform nodes are identified to provide decision support for the optimization of multi-platform communication strategies.

[0099] Step 504, supporting interactive hypothesis testing and strategy simulation; This sub-step expands upon the visualization and interactive analysis in Implementation 1 to support cross-platform hypothesis testing and strategy simulation. Users can simulate hypothetical scenarios by adjusting the following parameters: Content publishing strategy: choosing the best combination of publishing platforms, publishing time, and content format; Platform parameters: adjust the algorithm mechanism and user activity of different platforms; User group: Select the culture and interest characteristics of the target user group; Communication path: Set up key opinion leader activation strategies and information flow rules between platforms; The system uses rapid simulation calculations to display the expected impact results and optimal strategy recommendations under different strategies.

[0100] Application Example 1: Prediction of the influence of cross-platform marketing activities; In a cross-platform marketing campaign for a global consumer brand, the brand needed to publish new product promotional content in multiple countries, multiple languages, and multiple social media platforms. It also needed to evaluate and predict the dissemination effects of different content formats in different regions and platforms in real time to optimize resource allocation and content strategy.

[0101] Based on the large-scale social network influence prediction method of this invention, the system has built prediction models covering five major languages (English, Chinese, Japanese, French, and Spanish) and four major social platforms (Facebook, Twitter, Instagram, and Weibo). The application process is as follows: Multilingual Knowledge Graph Alignment: The system first builds a multilingual knowledge graph encompassing core concepts such as product features, marketing terms, and consumer sentiment. Using an entity alignment algorithm, it achieves precise cross-lingual concept mapping. For example, the system can identify expressions of the concept "environmentally friendly materials" in different languages and understand the nuances of regional cultural contexts, such as the emphasis on "renewable" in some regions and "non-polluting" in others.

[0102] Cultural Vector Space Construction: Based on historical social behavior data from target market users, a 12-dimensional cultural vector space was systematically constructed, encompassing dimensions such as individualism / collectivism, uncertainty avoidance, and long-term orientation. Analysis revealed that Asian users scored higher in collectivism and brand loyalty, while Western users scored higher in personal expression and innovation acceptance. These cultural differences directly impacted the dissemination of product information.

[0103] Resonance Strength Calculation: The system calculated the resonance strength of 15 different content formats prepared by brands (including text, images, videos, and other formats) across various cultural groups. For example, content emphasizing the social attributes of a product had a resonance strength of 0.82 (out of a maximum score of 1) in collectivist cultures, but only 0.51 in individualist cultures. Content emphasizing individual uniqueness showed the opposite trend.

[0104] Meme Diffusion Dynamics Simulation: The system breaks down marketing content into 43 basic meme units and simulates the diffusion paths of these units across different cultural networks. The simulation results show that certain memes emphasizing environmental protection spread 2.7 times faster in the European market than in the Asian market, but persist longer in the Asian market. Meanwhile, memes related to product functionality exhibit relatively consistent diffusion characteristics globally.

[0105] Accurate Impact Assessment: By integrating multi-dimensional indicators, the system comprehensively evaluates and visualizes the impact of different content across platforms and regions. Based on these results, brands adjust their content distribution strategies, allocating resources to the content formats and platform combinations predicted to perform best.

[0106] Practical application validation: Post-campaign performance analysis showed that the system's predicted content dissemination trends matched actual trends by an average of 87.6%, significantly higher than the brand's previous prediction model (62.3%). The optimized marketing strategy based on the system's recommendations increased user engagement by 31.5%, boosted conversion rates by 23.8%, and reduced marketing costs by 26.7% compared to the original plan.

[0107] Application Example 2: Analysis and prediction of public opinion on global public emergencies; In the event of a global public health emergency, relevant agencies need to monitor and predict the dissemination paths, speeds, and impact ranges of different information around the world, identify potential public opinion risk points, and develop differentiated information release strategies for audiences in different regions and cultural backgrounds.

[0108] Based on the large-scale social network influence prediction method of this invention, the system has built a public opinion analysis and prediction model covering major regions around the world, multiple languages, and multiple platforms. The application process is as follows: Multi-source heterogeneous knowledge fusion: The system integrates data from multiple sources, including news media, social platforms, and professional forums, to construct a large-scale knowledge graph centered around the core concepts of the event. This allows for the alignment and association of the same concepts across different languages and expressions. For example, the system can identify and associate various descriptions of the same epidemic prevention measure in different language contexts, capturing their semantic connections within the discussion.

[0109] Cultural Interest Dual-Space Representation: This systematically analyzes the attention preferences and response patterns of users worldwide regarding relevant topics, constructing a dual-space representation encompassing both cultural and topical interest dimensions. Data shows that users from different cultural backgrounds exhibit significant differences in information acquisition preferences, risk perception, and trust in authority, which directly impacts the path and effectiveness of information dissemination.

[0110] Application of the Triple Resonance Model: The system precisely calculates the resonance strength between official information and audiences from diverse cultural backgrounds and interest groups. The analysis found that messages emphasizing collective responsibility resonated 3.2 times more strongly in East Asia than in the West, while messages emphasizing personal protection were more widely accepted in the West. Furthermore, the amplification effect of the same content on different platforms varied by 2-5 times.

[0111] Hierarchical Contagion Dynamics Simulation: To improve computational efficiency, the system layers the global social network, applying simulation strategies of varying precision to user nodes at different levels of influence. Through parallel computing and incremental updates, the system can complete contagion predictions covering 200 million user nodes in just 20 minutes, 15 times faster than traditional models.

[0112] Cross-platform collaborative influence assessment: A systematic analysis of the synergistic effects of the same information across different platforms revealed that when certain information is disseminated simultaneously on mainstream social media and professional platforms, it will produce a significant amplification effect, with the influence increasing by 2.7 times compared to dissemination on a single platform; whereas certain information will produce a counteracting effect across different platforms, weakening its overall influence.

[0113] Practical Application Verification: Based on the system's predictions, relevant organizations adjusted their information release strategies and channel selection for different regions and cultural contexts. Subsequent evaluations showed that the optimized information release strategy increased the reach of official information by 42.6%, boosted public trust by 31.9%, and reduced the spread of misinformation by 37.4%. The system achieved an 89.2% accuracy rate in warning key public opinion risk points, providing strong support for timely intervention and guidance of public opinion.

[0114] Application Example 3: Optimization of dissemination of cross-cultural academic research results; An international academic institution needs to improve the global dissemination of its interdisciplinary research results, including in-depth dissemination within professional academic circles and popular science dissemination to the general public, while also considering the differences in knowledge acceptance and dissemination in different cultural backgrounds.

[0115] Based on the large-scale social network influence prediction method of the present invention, the system constructs a professional prediction model for the dissemination of academic achievements. The application process is as follows: Construction and alignment of disciplinary knowledge graphs: The system first builds a multilingual knowledge graph covering core concepts in relevant disciplines and accurately maps specialized terminology across different languages. Furthermore, the system identifies and connects conceptual relationships across disciplines, providing a semantic bridge for interdisciplinary knowledge dissemination. For example, the system can link the concept of "neural network" in computer science with its corresponding concept in biology, maintaining consistent semantic understanding across different language environments.

[0116] Analysis of Academic Culture Groups: The system analyzed the cultural vectors of scholars from different regions and disciplinary backgrounds around the world. The system found commonalities in academic culture in dimensions such as truth-seeking, openness, and collaboration, but significant differences in research methodology, knowledge system construction, and communication methods. In particular, the system identified cultural differences between Eastern and Western academic communities in citation patterns, critical expression, and recognition of innovation, factors that directly influence the dissemination of research results.

[0117] Optimizing Content-Audience Matching: For the same research findings, the system calculated the resonance strength between different presentation formats and target audiences. This analysis revealed that for academics, content emphasizing methodological innovation and theoretical contributions achieved the highest resonance strength (0.89); while for the general public, content emphasizing practical applications and societal impact was most effective (0.76). Furthermore, the system discovered that audiences from different cultural backgrounds have varying preferences for narrative structure. For example, Western audiences prefer a structure where the conclusion is presented first and the argument is presented later, while Asian audiences are more receptive to a step-by-step approach to argumentation.

[0118] Simulating the dissemination paths of academic influence: The system breaks down research findings into basic knowledge units and simulates the dissemination paths of these units within academic and public networks. The simulation results show that using key opinion leaders (including renowned scholars and popular science authors) as bridges can significantly accelerate the spread of knowledge from professional fields to the public. At the same time, the dissemination paths vary significantly across different cultural contexts. For example, in some cultures, institutional authority is a key node in dissemination, while in others, peer review is more important.

[0119] Communication Strategy Optimization and Implementation: Based on the simulation results, the system provides differentiated communication strategy recommendations, including content customization for different regions and audiences, optimal distribution platform combinations, and key influencer activation strategies. Based on these recommendations, academic institutions can adjust the format and channels for disseminating research findings, preparing culturally appropriate content for different regions and audiences.

[0120] Practical application results demonstrate that the optimized communication strategy has increased citation rates for research findings within professional academic fields by 34.8% and interdisciplinary citations by 46.2%. In the public sphere, media coverage has expanded by 68.5%, and public engagement has increased by 53.7%. The most significant improvement in dissemination of research findings has been in cultural areas traditionally difficult to reach, reaching 2.6 times the previous level. The system's prediction accuracy reached 84.3%, providing strong support for scientific decision-making in academic communication strategies.

[0121] The above describes an embodiment of the present invention, but this embodiment is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Ordinary technicians in this field can also make more forms of equivalent embodiments based on the inspiration of this embodiment, all of which are protected by this embodiment.

Claims

1. A large-scale social network influence prediction method, characterized by: The method comprises: Build a multilingual knowledge graph alignment system to achieve accurate mapping and semantic bridging of cross-language concept nodes; Based on multilingual knowledge graphs, a cultural vector space representation system is constructed to extract cultural features from social network user behavior data and form interpretable cultural dimensions. Using the cultural vector space, we can calculate the resonance strength between content and cultural vectors, and quantify the resonance strength of content in a specific cultural environment. Based on the results of resonance intensity calculation, the dynamics of cultural gene transmission is simulated, the content is decomposed into spreadable cultural gene units and their transmission process among different cultural groups is simulated; Integrate simulation results and fuse prediction results to achieve accurate impact assessment, and provide high-precision prediction results through multi-dimensional impact indicator integration and visual analysis.

2. A large-scale social network influence prediction method according to claim 1, characterized in that: The multilingual knowledge graph alignment system is constructed as follows: Use multilingual entity representation learning algorithms to process multi-source language data and construct an initial multilingual knowledge graph; The entity alignment algorithm is applied to achieve accurate mapping of cross-language concept nodes. The similarity calculation formula of the entity alignment algorithm is: ； in, Represents the similarity of the entity alignment algorithm, and Represents the concept nodes in the first language and the second language respectively, and Represent entities separately and The corresponding entity embedding vector, represents the cosine similarity function, 、 Represent entities separately 、 The set of neighbor nodes of 、 Control the importance of vector similarity and structural similarity in the total similarity respectively, represents the intersection, represents a union; Build a semantic bridging layer to unify the conceptual expressions in different languages.

3. A large-scale social network influence prediction method according to claim 1, characterized in that: The constructing of the cultural vector space representation system includes: The user behavior data is processed using a multimodal data fusion algorithm to extract implicit cultural features. The user behavior data includes text content, interaction patterns, content preferences, time patterns, and social network structure. The implicit cultural features are expressed as: ； in, Represents a user The cultural representation vector, represents the feature vector extracted from the user text content, represents the feature vector extracted from user interaction behavior, represents the feature vector extracted from the user's social structure, represents the feature vector extracted from the user's time pattern, represents the multimodal feature fusion function, which is used to integrate feature vectors from different sources into a unified cultural representation; Apply unsupervised clustering algorithm to construct cultural vector space; Extract core cultural dimensions to form an interpretable cultural vector space.

4. A large-scale social network influence prediction method according to claim 1, characterized in that: The calculation of the resonance strength between the implementation content and the cultural vector includes: Use multimodal content feature extraction algorithms to process social network content and generate content feature vectors; Apply attention mechanism algorithm to achieve matching of content features and cultural dimensions; A cultural resonance intensity function is constructed to calculate the degree of resonance between the content and a specific cultural environment. The cultural resonance intensity function is defined as: ； in, represents the cultural resonance intensity function, Represents the projection component of the content in each cultural dimension, A vector representation of a specific cultural environment, is the similarity function, is the weight parameter, indicating the The importance of each cultural dimension reflects the differentiated influence of different cultural dimensions in resonance calculation. Indicates the total number of cultural characteristics considered.

5. A large-scale social network influence prediction method according to claim 1, characterized in that: The implementation of the cultural gene propagation dynamics simulation includes: Applying meme decomposition algorithms to break down content into basic, spreadable units; Construct a meme fitness function to evaluate the potential of memes to spread in a specific cultural environment; The replication mutation selection algorithm is applied to simulate the process of meme propagation among different groups. The replication phase simulates the process of users contacting and sharing memes. The replication probability is expressed as: ； in, Represents a user Copying cultural genes The probability of is the sigmoid function, Represents cultural genes In the user adaptability in the cultural context, Represents a user The cultural vector representation of It indicates the inherent sharing behavior habits of users independent of cultural factors; Construct a cultural immunity mechanism model to quantify the acceptance threshold of different cultures for external information.

6. A large-scale social network influence prediction method according to claim 1, characterized in that: The large-scale social network influence prediction method further includes: Build a platform-aware multi-source heterogeneous knowledge fusion system to achieve cross-platform knowledge representation and semantic alignment; Construct a hierarchical cultural interest dual-space representation system to couple user cultural characteristics with interest preferences; Implementing a platform-aware content-culture-interest ternary resonance model to quantify the resonance intensity of content in a specific platform and cultural environment. The ternary resonance intensity function is defined as: ； in, represents the triple resonance intensity, represents the content feature vector, represents the culture vector, represents the interest vector, represents the platform feature vector, 、 、 Represent the weight coefficients of content culture resonance relationship, content interest resonance relationship, and cultural interest resonance relationship respectively, 、 、 Respectively represent the resonance strength between content and culture, content and interest, and culture and interest; Build an accelerated hierarchical propagation dynamics simulation system that improves the efficiency of large-scale simulations through network layering and approximate computing; Implement a comprehensive cross-platform influence assessment and prediction system that analyzes the synergistic effects of content dissemination across multiple platforms.

7. A large-scale social network influence prediction method according to claim 6, characterized in that: The construction of an accelerated hierarchical propagation dynamics simulation system includes: Design stratified sampling and approximate calculation strategies to divide social networks into multiple levels and adopt simulation strategies of different granularity for different levels; Introducing parallel computing and incremental update mechanisms, the network is divided into relatively independent blocks for parallel simulation, and only the part where the network state changes is calculated; Build an adaptive precision control mechanism to dynamically adjust simulation parameters and computing resource allocation according to the real-time requirements and precision requirements of the prediction task.

8. A large-scale social network influence prediction method according to claim 6, characterized in that: The cross-platform comprehensive influence evaluation and prediction system includes: Build a multi-level cascade model that perceives platform characteristics and simulates the flow and transformation of information between different platforms; Design a platform synergy effect evaluation model to analyze the interaction pattern when content is disseminated simultaneously on multiple platforms; Achieve cross-platform influence attribution analysis to identify the contribution of different platforms, different user groups, and different communication paths to the final influence; It supports interactive hypothesis testing and strategy simulation to assist users in planning and optimizing communication strategies.

9. A large-scale social network influence prediction system, used to execute a large-scale social network influence prediction method according to any one of claims 1 to 8, characterized in that: The system comprises: Multilingual knowledge graph alignment module, used to achieve accurate mapping and semantic bridging of cross-language concept nodes; Cultural vector space representation module, which is used to extract cultural features from social network user behavior data and form interpretable cultural dimensions; Resonance intensity calculation module, used to quantify the resonance intensity of content in a specific cultural environment; A module for simulating the dynamics of cultural gene dissemination, which is used to decompose content into disseminable cultural gene units and simulate their dissemination process among different cultural groups; The precise impact assessment module is used to provide high-precision prediction results through the integration and visual analysis of multi-dimensional impact indicators.

10. A large-scale social network influence prediction system according to claim 9, characterized in that: The large-scale social network influence prediction system further includes: A platform-aware multi-source heterogeneous knowledge fusion module for cross-platform knowledge representation and semantic alignment; A hierarchical cultural interest dual-space representation module is used to couple user cultural characteristics with interest preferences; The platform-perceived content-culture-interest triadic resonance module is used to quantify the resonance intensity of content in a specific platform and cultural environment; An accelerated hierarchical propagation dynamics simulation module, which is used to improve the efficiency of large-scale simulations through network layering and approximate computing; The cross-platform influence comprehensive evaluation and prediction module is used to analyze the synergistic effect of content dissemination on multiple platforms.

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