1077 results about "Social web" patented technology

The invention relates to the technical field of social information, in particular to a social network influence prediction method and system. According to the method, brand-related users, content and interaction data are obtained from multiple platforms, a multi-dimensional influence scoring model is constructed, and initial influence scores of the users are calculated. And in combination with a social network cascade propagation theory, constructing a propagation probability calculation model, and predicting an information propagation range and user influence changes. And finally, generating an influence prediction report containing KOL ranking, hot topic analysis and optimal intervention opportunity suggestions. According to the method and system, through organic combination of a plurality of innovation points, the accuracy, comprehensiveness and practicability of prediction are remarkably improved, the existing technical problems are effectively solved, and a powerful support tool is provided for a brand to formulate a precision marketing strategy in a complex social media environment.

The invention relates to the technical field of artificial intelligence, and discloses a multi-agent social network simulation method and system based on a cognitive inference chain, and the method comprises the steps: initializing a multi-agent system comprising a social environment engine, a user portrait engine and a cognitive inference engine, executing a multi-agent social network simulation cycle of a preset round of iteration, in each iteration round, the social environment engine pushes social information to the intelligent agent as external stimulation and activates the intelligent agent to execute an independent decision, the cognitive state of each dimension in the cognitive reasoning chain is updated through large language model reasoning, corresponding social behaviors are generated, and the social behaviors and corresponding cognitive state tracks are recorded; and periodically analyzing historical records to optimize influence coefficients among all cognitive dimensions of the cognitive inference chain, and adjusting an inference strategy of a preset large language model. The simulation of the cognitive process of the intelligent agent is a transparent and traceable evolutionary process, and the complete and understandable simulation of the'observation-cognition-behavior 'cycle is realized.

The invention relates to the technical field of social network analysis and generative artificial intelligence, and discloses a multi-agent-based complex social behavior simulation and public opinion deduction method, which comprises the following steps of: dividing social groups, and dividing users in a social network into key opinion leader communities and common user communities; respectively constructing a key opinion leader agent and a common user agent which are driven by the large language model; constructing a dynamic weighted directed network, taking each key opinion leader agent model as a node, calculating a network node neighborhood through opinion index and node influence weighting, calculating an emotion score based on a text emotion index, dynamically adjusting the network node neighborhood based on the emotion score, and performing loop iteration for several times to complete deduction and simulation of network public opinions; the problem that public opinion propagation modeling is difficult under the cross-domain dynamic background is solved, the difference of public opinion individuals and the complex social interaction relation can be simulated more accurately, and the method is feasible.

The invention relates to a multi-modal false information detection method based on semantic consistency, and aims to solve the problem of semantic interaction between modals in multi-modal false information detection. The method specifically comprises the following steps: firstly, acquiring multi-modal data of news in a social network, and preprocessing a text and an image; then respectively constructing global and local feature extractors of the text and the image; local semantic consistency detection is carried out through the extracted text local features and the image local features, global semantic consistency detection is carried out on the obtained global features, the two features are combined to generate a joint semantic consistency result, and the attention mechanism text and image features are adopted to carry out multi-modal fusion to obtain a joint semantic consistency result; the false information is distinguished from multiple dimensions and a loss function is established to optimize the model by comprehensively analyzing the multi-modal features after the text features and the image features are fused and the united semantic consistency result, so that the detection of the false information is realized. According to the method, the accuracy of multi-modal false information detection can be effectively improved, and the multi-modal false information can be automatically detected.

An apparatus, including a processor which provides an electronic forum which provides a video or audio broadcast of a sporting event and allows users to communicate with one another, post comments, place a bet or bets, receive information regarding bets available, betting odds, changes in betting odds, or analytics information, or report an instance of suspected game fixing, match fixing, or cheating, before, during, or after, the sporting event; a transmitter, which transmits the electronic forum to a user communication device; and a receiver which receives information transmitted from the user communication device. The processor processes an outcome of a bet on the sporting event and determines if the bet is a winning bet or losing bet. The apparatus generates a report and transmits the report to a computer associated with a sport governing body, governmental entity, gaming facility, analytics provider, social network, financial institution, or escrow agent.

The invention relates to the technical field of data processing, and provides a social network privacy data protection method and system, and the method comprises the steps: obtaining to-be-encrypted privacy data, and obtaining a keyword popularity coefficient and a sensitivity weight coefficient according to the privacy data; acquiring behavior characteristics and basic information of a user group, and acquiring a group portrait index according to the behavior characteristics and the basic information; acquiring network behavior activeness, equipment characteristics and historical behavior information of the target user, and acquiring personal characteristics of the target user according to the network behavior activeness, the equipment characteristics and the historical behavior information; according to the keyword popularity coefficient, the sensitivity weight coefficient, the group portrait index and the personal characteristics of the target user, obtaining the encryption level of the privacy data; and calling different encryption algorithms to encrypt the privacy data according to the encryption level. According to the method, different encryption algorithms are called for encryption for the privacy data of different encryption levels, and the dynamic adaptability of encryption protection of the privacy data of the social network is improved.

The invention relates to the technical field of network information propagation prediction, and discloses a topic propagation prediction method and system based on multi-dimensional feature fusion, and the method comprises the steps: extracting multi-dimensional features, inputting the multi-dimensional features to a decoder layer of Transform for fusion, and constructing a prediction model for topic propagation prediction; the multi-dimensional features comprise user comprehensive influence, text emotion features, time dynamic features and user interaction behaviors. According to the method, through collaborative optimization of the dynamic user modeling module, the multi-modal feature fusion module and the intelligent time sequence analysis module, the social network propagation prediction accuracy is remarkably improved, and accurate prediction of the topic propagation trend in the social network is achieved.

The invention relates to the technical field of enterprise risk assessment, and provides an enterprise risk assessment method based on a dynamic graph neural network and related equipment. The method comprises the following steps: acquiring relational data of a plurality of target enterprises; constructing a multi-layer enterprise social network based on all relational data; performing feature extraction on the multilayer enterprise social network by using a dynamic graph neural network to obtain short-term feature embedding, long-term feature embedding and topological feature embedding of the multilayer enterprise social network; based on the multi-layer enterprise social network, obtaining persistent features, and fusing the persistent features, the short-term feature embedding, the long-term feature embedding and the topological feature embedding to obtain fused features; and performing risk assessment on each target enterprise according to the fusion features and the multi-layer enterprise social network to obtain a risk assessment result of each target enterprise. According to the method, the accuracy of enterprise risk assessment can be improved.

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.

The invention provides an intelligent message pushing method and system, and the method comprises the steps: collecting the social behavior data and personal attribute data of a user in a social network; dividing the users into a plurality of groups by using the social behavior data and the personal attribute data based on an ant colony algorithm; analyzing a social relation and an interaction mode among users in the group; collecting message resources in the social network, and classifying and labeling messages; performing collaborative filtering processing on the to-be-pushed message, determining a target user group, and generating a message recommendation list; and pushing the recommended message to users in the target user group according to a preset pushing strategy. The user social behavior data and the personal attribute data are converted into the ant feature vectors based on the ant colony algorithm, similar feature vector ants are gathered through pheromone updating and path selection mechanisms, different user groups are formed, the user group division accuracy is improved, and user requirements are more accurately grasped.

The invention discloses a bank flow risk assessment method and an application system. The method comprises the following steps: acquiring first multi-modal data; wherein the first multi-modal data comprises a bank statement, equipment information corresponding to the bank statement, a user portrait corresponding to the equipment information, a social network relationship and a geographic position; inputting the first multi-modal data into a risk scoring model to obtain a risk level corresponding to the first multi-modal data; and performing risk monitoring according to the risk level. In this way, the accuracy and efficiency of a financial institution for identifying the enterprise fund flow state and the potential risk can be improved.

The invention relates to the technical field of computer software, and discloses a large-scale social network simulation method and system based on a dynamic topological structure. The method comprises the following steps: distributing a user portrait and a memory to a corresponding agent, and constructing a social network for the agent; distinguishing a core agent from a conventional agent according to the influence measurement; the core agent is driven by a large language model and interacts with other core agents through a natural language, so that the attitude of a conventional agent to an event is influenced; the conventional agent updates the attitude through the trust weight, and adjusts the social network by using a dynamic link prediction engine; updating the memory of the intelligent agent, pushing a library and a social network; and continuously performing a dynamic evolution process at each time step until the deduction of the event is completed. According to the method, detailed description of individual behaviors is ensured, the expandability of the whole system is also considered, and the decision-making efficiency and the simulation precision of the super-large-scale social network simulator are remarkably improved.

The invention discloses a social network false message detection method based on large model and multi-modal fusion. According to the method, based on a given multi-modal message, for an image and a text, an embedding vector and aligned embedding are obtained respectively; utilizing the large model to obtain a background knowledge embedding vector; splicing and inputting the two aligned embedding pieces into a full connection layer to obtain a multi-modal hybrid aligned embedding vector; inputting image embedding, text embedding, hybrid alignment embedding and background knowledge embedding into an N-layer stacked progressive Transform model, and outputting an embedded vector after multi-modal fusion; and inputting the fused embedded vector into a classifier, and setting a cross entropy loss function at the same time. According to the detection method, texts, images, text-image alignment characteristics and background knowledge assisted by a large model are fused, false message detection is assisted by the background knowledge generated by the large language model, and different modal information is effectively fused by a designed stacking progressive model; and the accuracy of false message detection is improved from the perspective of multi-modal fusion.

Embodiments provide a social networking platform offering various services, such as, facilitating aggregation and management of a user's interaction on one or more social networking platforms, offering enhanced control over the level of privacy associated with the flow of user data, offering tools to customize the user's exposure to advertisement-related content on the social networking platform(s), integrating features to control aspects of how data / content is presented to and visualized by the user, empowering the user to multicast direct messages to other users without the other users having to meet certain constraints, empowering the user to create and / or join a group based on messaging threads, and the like. One or more of these enhanced services / features are associated with a powerful framework of authentication / permission model for access control.

The invention discloses a multi-scale space-time perception recommendation method and system based on hypergraph convolution, and relates to the field of recommendation of social network applications, and the method comprises the steps: obtaining sample user sign-in data, carrying out the time scale segmentation of the sample user sign-in data, obtaining sample multi-scale sign-in sequence data, taking a sign-in point of a user as a node, and carrying out the time scale segmentation of the sample multi-scale sign-in sequence data; constructing a hypergraph under each time scale by taking the time scale as a hyperedge; adopting node features and hyperedge features of the hypergraph to train the recommendation model; the recommendation model updates node features and hyperedge features by using hypergraph convolution and an attention mechanism and taking node loss and hyperedge loss as constraints, performs multi-scale spatio-temporal information interaction fusion based on the updated node features and hyperedge features to obtain a prediction recommendation result, and takes cross entropy loss as a prediction evaluation constraint; and the trained recommendation model is adopted to predict the to-be-predicted information, so that the recommendation accuracy and the personalized degree are improved.

Systems and methods for enhancing a first user's video gaming experience by enabling a second user to join as a mercenary. The systems and methods comprise transmitting an invitation to the second user to join the first user's gaming session, and receiving an acceptance from the second user. The systems and methods then include determining whether a current point in the first user's gaming session corresponds to an insertion point for the second user to be added. In response, the systems and method modify the gaming session to be a multiplayer gaming session and add the second user. The systems and methods then identify an end trigger point in the modified gaming session, and responsively remove the second user from the modified gaming session and revert the gaming session back to a single player mode.

The invention provides a key user identification method and system in a social network, and relates to the technical field of user identification, and the method comprises the steps: constructing a social network graph model according to social network data; calculating a basic centrality index through a graph theory algorithm; dividing the social network data into a plurality of communities, and identifying local roles of user nodes in the plurality of communities; calculating a local centrality index of the local role; generating a global score and a local score; and performing score ranking on the global scores and the local scores, and screening key user nodes. According to the method, through two-dimensional analysis fusing global centrality indexes and local role features, the problem that cross-community bridging nodes and local core users are insufficient in recognition in a traditional method is effectively solved, and the comprehensiveness and accuracy of key user recognition are remarkably improved; and through dual verification of time sequence feature analysis and rule engine verification, the abnormal state of the disguised high-influence user is effectively identified, and the result reliability is remarkably improved.

A modular system designed for privacy-preserving content recognition and supplemental content delivery across web and mobile environments. The system employs lightweight character sampling and vision-based recognition to generate unique content fingerprints without storing or replicating original data. It features a hybrid processing architecture, using local computing resources for intensive tasks while optimizing performance on resource-constrained devices. Core functionalities include multi-method content fingerprinting, real-time monitoring with adaptive sampling, and secure supplemental content association. Operating entirely on the client-side, it complies with website terms of service and privacy regulations. Advanced features include AI-driven content recognition, blockchain-based verification, and granular content targeting through resizable selection interfaces. This technology enables seamless delivery of supplemental content while preserving privacy, reducing resource usage, and ensuring scalability across browsers, mobile applications, and edge devices. It is particularly applicable in industries such as education, retail, and secure data sharing.

The invention provides a knowledge graph link reasoning method, and belongs to the field of artificial intelligence and knowledge graphs. The method comprises the steps that a local structure is reserved through a social triple integrity hypergraph, global association is captured through a social relation semantic hypergraph, and complementary social network double hypergraph representation is constructed; based on composite feature fusion of heterogeneous interaction, homogeneous parallel interaction and overall interaction, and in combination with a hypergraph attention network, relationship-guided dynamic semantic propagation is realized; designing multi-channel comparative learning based on social data; the social triple score is optimized based on a marginal sorting loss function, and a knowledge graph link prediction task and a self-supervision comparison task are fused through a joint learning framework. The problems of insufficient local structure modeling, global semantic information splitting, weak representation generalization ability, high dependence on labeled data and the like existing in knowledge graph link prediction in an existing social network are solved, and link reasoning tasks in a dynamic, sparse and high-noise-disturbance social scene are difficult to effectively deal with.

The invention discloses an online social network harmful node identification and community division method and system. The method comprises the following steps: acquiring a social platform user speaking and interactive information data set; on the basis of the data set, a social network directed graph structure is constructed with users as nodes and interaction relations as edges; based on the social network directed graph structure, introducing evaluation indexes of edges for calculation, and determining weights of the edges; based on the directed graph structure of the social network, introducing degree centrality, betweenness centrality and a Kshell value to carry out total graph node calculation, and determining key nodes in social network propagation; and based on the weight of the edge and the key node, using a Louvain algorithm to realize community division of the optimal modularity. According to the method, the potential manipulation behaviors are identified by analyzing the graph structure of the dynamic social network, so that the identification capability of the manipulation behaviors of the social network can be improved.

The invention discloses a social network public opinion monitoring method and system based on artificial intelligence. The method comprises the following steps of obtaining public opinion data from a social network platform and performing data preprocessing; hot events are automatically extracted from the public opinion data, and a density peak clustering algorithm is utilized to identify a high-density area and determine the popularity of the hot events. A BERT model is adopted to carry out sentiment classification on a text, context features are extracted in combination with bidirectional LSTM, and sentiment tendency is output through a Softmax classifier. For a specific target event, a multi-head attention mechanism and a convolutional neural network are used for specific target emotion classification. And performing noise reduction and decomposition on the time sequence data, constructing a plurality of basic prediction models, generating a combined prediction model in combination with weighted average, and monitoring and predicting the public opinion development trend in real time. Accurate public opinion analysis and decision support are provided for governments and enterprises, public opinion risks are reduced, social stability is maintained, and social construction is promoted.

The invention provides a social network key node identification method and system fusing propagation characteristics, and relates to the technical field of node identification, and the method comprises the steps: collecting various data in real time; the method comprises the following steps: identifying network propagation data to obtain key information data, constructing an enhanced recurrent neural network optimized based on a sodat swarm optimization algorithm, inputting the key information data, outputting to obtain propagation network characteristics, and analyzing the key information data to obtain node propagation performance data. And extracting a multi-factor influence coefficient from the external factor data, constructing a node evaluation system, and screening the preliminary nodes according to the multi-factor influence coefficient to obtain key nodes. According to the method, the recurrent neural network is enhanced, the propagation network features are extracted, the node evaluation system is constructed, the topological structure data are classified, and the key nodes are determined in combination with the multi-factor influence coefficient, so that the understanding and prediction capabilities of the propagation process are improved, and the key nodes are identified more accurately.

Systems and methods are disclosed for market research and social media categorization solutions and methods that are capable of using data from existing social networks, news or reference resources, and other information systems to automate the process of grouping data items into labeled categories by topic. These embodiments can extract greater market insight from available data sources, provide greater predictive value to marketing or sales strategies, and save money by being more efficient or easier to implement into existing systems

The invention discloses a social network denoising method based on a graph neural network, which belongs to the technical field of social networks and comprises the following steps: acquiring social network data; a dual trainable denoising module is constructed, the dual trainable denoising module comprises a structural noise corrector, a feature noise filter and a fusion layer, the structural noise corrector is used for generating mixed features, the feature noise filter is used for generating graph convolution output, and the fusion layer is used for fusing and obtaining a purified user node feature matrix; constructing a loss function L and training to obtain a dual denoising model; obtaining social network data to be denoised, and obtaining a corresponding purified user node feature matrix through the dual denoising model; according to the method, feature-topology non-unification can be effectively reconciled, so that the model can neglect inconsistent noise in features and structures, and the characterization quality in a social network with complex noise is remarkably improved. The purified user node feature matrix has good performance in the classification task.

The invention discloses an online social network unreal public opinion information early study and judgment method and device, belongs to the technical field of unreal public opinion information spreading, and particularly relates to spreading modeling of online social network information. The problem that according to an existing judgment method based on public opinion information content, the response time of public opinion managers to public opinion events in an online social network cannot be shortened is solved. The method comprises the following steps of: constructing a detection framework of an online social network unreal public opinion information early study and judgment model; and obtaining a final research and judgment result by obtaining the configured early research and judgment model of the unreal public opinion information of the online social network according to the steps. The online social network unreal public opinion information early research and judgment method and device are suitable for carrying out early research and judgment on online social network unreal public opinion information.

The invention discloses a social blog management system and method based on Vue and SpringBoot, belongs to the technical field of social networks, and solves the problems that an existing system only focuses on user information collection, activity registration and basic data storage and cannot meet the complex requirements of a blog platform for content creation, personalized recommendation and multi-dimensional social interaction, and the social blog management efficiency is improved. The system comprises a front-end module, a rear-end module and a database module, and the front-end module and the rear-end module carry out data interaction through AJAX and RESTful API (Application Program Interface); according to the system, a front-end and back-end separation framework of Vue.js and SpringBoot is adopted, and a relational database MySQL, a memory database Redis and OSS storage are combined, so that efficient storage and cache management of data are realized, the system performance is optimized, a back-end module can generate personalized recommendation information by analyzing user blog information, the user experience is improved, and the user experience is improved. Rich user management and social contact functions are provided, and interaction between users is enhanced.

A social networking system is provided. The system encompasses a robust processing subsystem hosted on a server, comprises various modules including a registration module linking social media accounts, a blockchain-backed bio page module storing audience metrics, a payment gateway with a reward system, and a CRM module offering dashboards for influencers, brands, agencies, and followers. An analysis module integrates natural language processing to assess content tone, engagement, and sentiment. A button module generates emoticon categorization and auto-suggestions based on metadata, leveraging machine learning for predictions. A compliance analysis module ensures adherence to regulations in paid social media content, and a royalty distribution module calculates and allocates royalties based on engagement, location, compliance, and fan type via smart contracts, This comprehensive subsystem integrates functionalities to elevate user interaction, content analysis, and monetization opportunities within the social media platforms.

The invention provides a transaction customer social network construction and expansion method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining an original data set of a target customer; pre-processing the unstructured data in the original data set; according to the feature vector sets of the structured data and the unstructured data, respectively calculating association indexes between the target customer and other customers; according to the association indexes, constructing a multi-dimensional association matrix of the target customer and other customers; element values in the multi-dimensional incidence matrix are converted into edge weights, according to a preset edge weight threshold value, the relation that the edge weights are lower than the threshold value is eliminated, a graph structure containing nodes and edges is constructed, and a customer social network graph is generated; calculating a centrality index of each client node according to the client social network graph; and sorting according to the centrality indexes, and selecting client nodes with the top N ranks as high-value potential clients. According to the invention, the autonomy and accuracy of social network management are improved.

A system and method whereby a group of people can privately, or in an online location with a singular purpose, share a website, social network, or other communications for a particular event. A personalized social network can be generated for the group to enhance privacy, security, and ease of interaction. The personalized social network can be associated with a place, time, or other conditions, such that the social network can effectively address a singular purpose, such as a wedding, and all group members can easily communicate information relative to the event.

The invention discloses a target portrait digitization method and device in combination with virtual and real space knowledge, and belongs to the field of artificial intelligence, and the method comprises the steps: constructing a hierarchical target portrait attribute tag from the aspects of basic features, psychological features and association relationships of a target person; target user information is collected and obtained from a social network platform and a real physical domain and then is processed, so that cross-domain virtual and real identity association verification between virtual space social accounts and between physical space target users is realized; respectively constructing attribute tag instantiation methods according to the obtained target information in combination with the constructed hierarchical target portrait attribute tags; and according to the target attribute features, different visualization modes are adopted for display. The target portrait dimension is enriched, the target attribute instantiation method is expanded, and the accuracy of the target portrait is improved.