596 results about "Social media" patented technology

The invention discloses a public opinion event multi-mode semantic fusion modeling and abstract generation method and system, and relates to the field of natural language processing and social network analysis. Through the multi-mode semantic fusion technology, the short text understanding ability is improved, and the problems of semantic fuzziness and network language diversification are solved. Meanwhile, through a cross-window event cluster matching technology, an event evolution path with time continuity is constructed, and comprehensive capture of event dynamic characteristics is realized. Besides, the structured event abstract is automatically generated by utilizing the generative model, so that the consistency and the information density of the abstract are improved, and the actual application requirements are met. Through the innovations, the defects in the aspects of semantic comprehension, dynamic modeling and abstract generation in the prior art can be effectively overcome, a more efficient and accurate solution is provided for monitoring and analysis of public opinion events, and the method has wide application prospects in the fields of public opinion monitoring, emergency early warning, social media data analysis and the like.

The invention discloses a data-enhanced fine-grained multi-mode false information detection method and system, and aims to improve the accuracy of image-text false news detection. The method comprises the following steps: acquiring a news text and associated pictures thereof, extracting a text core entity semantic sequence and a visual entity semantic sequence by respectively utilizing a pre-training language model and a visual entity recognition model, and performing knowledge enhancement on original word-level text representation and low-level visual features through an attention mechanism; calculating a correlation matrix between the enhanced text and the visual features, dividing consistent and inconsistent regions, and respectively extracting consistent and inconsistent features; and finally, fusing the two types of features and global text representation to generate classification features so as to judge the authenticity of the news. Through double-channel knowledge enhancement and fine-grained cross-modal consistency analysis, the detection performance is remarkably improved, and the method is suitable for scenes such as social media and news platforms.

A social media platform provides a map-based graphical user interface (GUI) for accessing social media content submitted for public accessibility via the social media platform supported by the map-based GUI. The GUI includes a map providing interactive location-based searching functionality in that selection of a target location by the user in the GUI, such as by tapping or clicking at the target location, triggers a search for social media content having geo-tag data indicating geographic locations within a geographical search area centered on the target location. A search period for which content is returned is dynamically variable based on the duration for which the tap or click is held.

The invention relates to a multi-dimensional user grouping matching method and device, equipment and a medium. The method comprises the following steps: firstly, acquiring real-time behavior data, consumption data, interest data, historical conversion data and social media data of a user group, and integrating to form a user feature data set; performing multi-dimensional data fusion and cross-dimensional correlation analysis on the data set to generate a user group feature set; the value weight of the user group is updated based on the feature set, grouping levels are divided in combination with service accuracy requirements, and a dynamic grouping mechanism is constructed; and finally, receiving bidding requests of the service providers, constructing a dynamic bidding model in combination with a dynamic grouping mechanism to calculate bidding scores of the service providers, sorting the service providers according to the scores, and generating an accurate matching result with the target user group. According to the method, the limitation of single-dimensional grouping is overcome, the timeliness and flexibility of grouping are improved, and the accuracy and efficiency of matching between the service provider and the target user group are effectively improved.

The invention discloses a user participation degree prediction method based on distillation multi-modal retrieval enhancement, and belongs to the technical field of social media data mining and user behavior analysis. According to the method, the correlation of the UGC is evaluated by introducing the self-enhanced distillation module, and the top-K related UGC is selected in combination with the selected retriever, so that the interference of irrelevant information on the related UGC is effectively avoided, and the noise caused by irrelevant artifacts can be filtered out when the related UGC is retrieved, thereby keeping the original feature representation of the related UGC. The heterogeneous graph construction module enhances the interactive representation capability between UGCs through multi-relation modeling, and can more accurately optimize a prediction result in user participation prediction. According to the method, information retrieval between the related UGC and the unrelated UGC can be better balanced, excessive diffusion of unrelated information is avoided, and the performance of the model in user participation degree prediction is improved. Particularly, when complex multi-modal data containing a large number of irrelevant UGCs is processed, the mechanism can effectively enhance the distinction degree of the relevant UGCs, and the prediction accuracy is improved.

The invention discloses a teenager psychological sub-health intelligent early warning system based on multi-source heterogeneous data fusion, and the system comprises a data collection layer which collects the behavior, physiological and social three-dimensional data of teenagers through the multi-source channels of campus cards, wearable devices, social media and questionnaires, and builds an original data pool through cleaning, denoising and standardization; the data fusion layer is used for integrating multi-source data by adopting weighted average and Kalman filtering, mining psychological sub-health key indexes in combination with a feature selection and extraction technology, and forming a three-dimensional psychological portrait; the deep learning layer is used for constructing a multi-modal fusion early warning model based on a Transform architecture, and carrying out real-time prediction and dynamic tracking of psychological sub-health risks through large-scale data training and cross validation optimization; and the intelligent early warning layer is used for visually displaying an early warning result, integrating three-party linkage of a management end, a teacher end and a parent end, providing 24-hour online intervention by a built-in AI psychological counseling module, and automatically transferring high-risk cases to professional psychological consultants.

Disclosed embodiments provide techniques for computerized moderation, authorship recording, and distribution of social media content. Moderator-supplied tags are associated with content and supplied to a machine learning system as training data. Using blockchain, authorship is authenticated and can be converted from anonymous to non-anonymous. Collaboration among authors on content is supported with authorship lists that can contain a mix of anonymous and non-anonymous authors. Contribution limits are established to determine royalty payments for sale and rent of content.

The invention provides an intelligent marketing decision analysis method and system for a real-time competitive product strategy, and the method comprises the steps: collecting the marketing content of a competitive product published on a social media platform, extracting an account number, an arriving person portrait feature, a content structure label, a platform identifier and publishing time, and constructing a standardized strategy behavior vector; performing time sequence alignment on the strategy behavior vectors and our marketing indexes, and identifying competing product strategy behavior subsets which have significant influence on the our indexes; based on the subset, generating a structured response strategy containing a recommended person type, a content structure label, a delivery platform and a time period; and performing multi-dimensional matching on the marketing resource library and the content template library, and outputting an executable marketing resource combination and scheduling instruction. According to the method, a closed loop from competitive product behavior perception and causal influence identification to response strategy automatic generation and landing execution is realized, and the real-time performance, the accuracy and the automation level of marketing response are improved.

The invention discloses an intelligent media content association propagation and influence analysis method based on a knowledge graph, and the method comprises the following steps: collecting multi-source media data from social media, a news website, a video platform and a forum, and constructing and forming a heterogeneous knowledge graph; performing feature initialization and embedding on nodes in the heterogeneous knowledge graph to generate initial node embedding representation; constructing a dynamic heterogeneous graph attention network based on the node initial embedding representation to obtain a node dynamic propagation state representation; calculating a propagation influence score based on the node dynamic propagation state representation to obtain a node influence sorting result and a core propagation path; and introducing a causal consistency training mechanism based on the core propagation path, optimizing parameters of the dynamic heterogeneous graph attention network, and outputting a propagation influence evaluation result. According to the method, the dynamic heterogeneous graph attention network is adopted, and intelligent analysis of the media content propagation influence is realized.

The invention discloses a pathogen transmission rapid early warning and traceability analysis method based on multi-source data fusion. The method comprises the steps of S1, collecting multi-source data such as drug sales, network behaviors, social media, outpatient diagnosis and traffic time and space; s2, through cleaning and standardized preprocessing, social text disease features are extracted by adopting BERT; s3, mining a comprehensive weak signal through single-source anomaly detection and multi-source correlation analysis; s4, fusing the features by using an Attention-LSTM model, and outputting a regional risk index; s5, training an early warning model based on historical data, and setting a three-level threshold to trigger early warning; and S6, positioning a propagation starting point in combination with the spatio-temporal data, and constructing a propagation chain through a graph neural network. The early warning is advanced by 3-7 days, the traceability precision reaches the community level, and the prevention and control precision is improved.

Systems and methods for multi-application audio splicing may include (1) receiving, from a third-party application installed on a user's device, an audio segment of an audio file selected by the user via an audio-segment-selection interface presented by the third-party application in association with the audio file, (2) loading the audio segment into a post-creation interface of a social media application installed on the user's device and prompting the user to add visual content via the post-creation interface, (3) creating a social media post that includes (i) the audio segment and (ii) visual content added by the user via the post-creation interface, and (4) posting the social media post to a social media consumption channel. Various other methods, systems, and computer-readable media are also disclosed.

The invention belongs to the technical field of disaster information processing, and relates to a flood narrative expression method fusing social media data and geographic scene knowledge, and the method achieves the precise semantic analysis of social media disaster information through the cooperation of a structured rule of a geographic scene ontology and a large language model. Converting the non-standardized disaster description into a structured disaster scene element; constructing a dynamic expression mechanism of a disaster causal chain in combination with disaster semantic knowledge reasoning and a spatio-temporal evolution narrative framework; based on a collaborative architecture of a knowledge graph and a narrative visualization module, multi-level dynamic rendering and hierarchical display of disaster information are realized, and hierarchical presentation of the disaster information is realized by a time axis linkage and space zooming technology; according to the method, disaster information expression is changed from static display to dynamic narration and from macroscopic description to causal reasoning, real-time cognition and autonomous response energy of the public to disaster risks are enhanced, and scientificity and social collaboration of disaster risk management are remarkably enhanced.

The invention provides a social listening data noise reduction method and device based on a large language model, and belongs to the technical field of social media data processing. The method comprises the steps that social media data are collected and preliminarily filtered to obtain preliminarily filtered data; generating a semantic embedding vector by using a pre-trained large language model, and screening semantic related data in combination with cosine similarity and context; identifying and filtering duplicate and variant contents through an embedded model and a generative adversarial network to obtain duplicate-removed data; performing multi-modal feature extraction on the de-duplicated data, and screening credible data in combination with generative AI detection and sentiment analysis; and filtering semantic inconsistency and non-topic continuation texts by adopting a clustering algorithm and semantic relationship analysis to finish noise reduction. According to the method, through a multi-stage and multi-dimensional noise reduction strategy, the purity and credibility of social listening data are effectively improved, and high-quality data support is provided for subsequent topic analysis and decision making.

A map-based graphical user interface (GUI) for a social media platform provides a map window that displays an interactive map that is movable within the map window responsive to user input. User icons for respective friend users of a user associated with a client device on which the map-based GUI is generated are displayed in the map window at respective locations on the interactive map. A predefined portion of the map window defines a focus area. Context information for respective friend users are automatically surface for those friend icons located within the focus area, while such context information are automatically omitted or de-surfaced for those friend icons outside of the focus area.

A graphical user interface for a computer for displaying sports imagery, comprising: a visual screen; in computer memory at least one virtual object viewable within the screen, the virtual object having at least a virtual first external content surface and a virtual second external content surface which face different directions from each other. The virtual first external content surface has a first sports imagery. The virtual second external content surface has second sport imagery; virtual object is rotatable back and forth at least on a Z-axis and on an X-axis by the user in 3D within said viewable area to view different surfaces; wherein in response to user interface movement back and forth generally horizontally said virtual object rotates proportionally around said Z-axis; and, wherein in response to user interface movement back and forth generally vertically said virtual object rotates proportionally around said X-axis.

The invention discloses an agricultural tourism post site selection method, system and device and a medium, and the method comprises the steps: firstly constructing an agricultural tourism attraction-post bearing capacity comprehensive evaluation model, and then carrying out the preprocessing of POI data, mobile phone signaling data, agricultural tourism statistical data and social media data in a to-be-planned region; generating multiple items of index data in the index layer, calculating an agricultural tourism attraction index and a station bearing capacity index based on the multiple items of index data after preprocessing and the model, and according to the agricultural tourism attraction index and the station bearing capacity index, calculating the station bearing capacity index according to the agricultural tourism attraction index and the station bearing capacity index. And calculating an agricultural tourism attraction-post bearing capacity comprehensive evaluation index of each candidate post address in the to-be-planned area, finally identifying the grade of each candidate post address according to the agricultural tourism attraction-post bearing capacity comprehensive evaluation index, and outputting a site selection result. According to the invention, the site selection precision of the agricultural tourist station can be improved.

The invention discloses a social media intelligent interaction data processing method and system based on multi-modal analysis, and relates to the field of multi-modal analysis, and the method comprises the steps: collecting an original interaction data stream from a target social media platform in real time, the original interactive data stream at least comprises text modal data, image modal data and user behavior sequence modal data; and performing synchronous alignment and cleaning on the original interactive data stream to generate a standardized multi-modal data unit with aligned timestamps. According to the method, through a cross-modal attention fusion mechanism, the defects of shallow multi-modal information fusion and one-sided analysis dimension in the prior art are overcome, unified representation and collaborative understanding of the text, the image and the user behavior sequence on the deep semantic level are realized, and the recognition accuracy of the real intention and the interactive context of the user is remarkably improved.

Systems, apparatuses, and methods for more effectively monitoring social network and social media posts to assist in identifying posted video, audio, or textual content that may be of interest or concern to a specific entity. This may comprise implementation of a process or technique to select a platform to extract content from, selecting one or more channels or sub-channels on that platform from which to extract content of interest or concern, identifying and subsequently extracting posts expected to contain content of interest or concern, post-processing the extracted content to place it into a form in which it can better be evaluated, and based on the extracted and processed content, causing one or more actions or events to occur.

The invention discloses a financial futures market condition analysis method and system based on multi-agent collaboration. The method comprises the steps that structured data including market data, news information and social media emotion are collected in real time; carrying out technical index technology and trend analysis and supporting resistance potential identification by adopting indexes including a moving average line, a relative strength index RSI, an exponential smoothing different and similar moving average line MACD and a Boforest belt, and outputting a technical analysis report; a basic analysis report is output by analyzing the financial statement, the macroeconomic data and the industry dynamics; the method comprises the following steps: analyzing market emotion, news emotion and social media public opinions by adopting a natural language processing method and an emotion analysis method to obtain market emotion indexes; and generating executable prediction and investment suggestions by integrating the technical analysis report, the basic analysis report and the market emotion indexes. According to the method, the problems of limitation of a single model, data islands, insufficient real-time performance and the like in the prior art are solved.

The present application relates to the field of food and beverage technology, and in particular to a food and beverage network sales trend prediction model construction system and method based on multi-source data fusion and deep learning. It includes a data acquisition unit, a data processing unit, a model construction unit, the model construction unit is used for constructing a deep learning prediction model, adopts an improved LSTM-Transformer fusion algorithm, realizes nonlinear mapping modeling of food and beverage sales trend through integrating time sequence feature modeling and global dependence relationship analysis technology, a model training and verification unit, and a prediction output unit. The present application integrates network sales platform data, social media sentiment text, meteorological information and industry information and other multi-source data, extracts time dimension features, text sentiment features and price elasticity-weather influence and other cross-correlation features by combining feature engineering technology, fully covers the influencing factors of food and beverage sales, and solves the problem of single data dimension in the traditional scheme.

The invention provides a multi-mode command entity recognition method based on course comparative learning, and belongs to the technical field of natural language processing and computer vision. The method comprises the following steps: respectively carrying out semantic representation modeling on text and image data to obtain text features and image features; multi-level semantics from coarse granularity to fine granularity between the text features and the image features are aligned through staged comparative learning, and association between the text features and the image features is enhanced; fusing the text features and the image features through a gating multi-interest fusion mechanism to obtain fusion features; and performing sequence labeling on the fusion features by using a dynamically weighted conditional random field model. By combining a learnable gated multi-interest fusion module and a category-sensitive dynamic decoding mechanism, the problems of modal noise interference and category imbalance are effectively relieved, and the accuracy and robustness of multi-modal entity recognition and the applicability and stability of the model in a real social media environment are improved.

The invention discloses a social robot false user identification method and system based on a knowledge graph, and the method comprises the steps: obtaining an initial knowledge graph based on user behavior data and interaction records; based on the initial knowledge graph, obtaining a topological feature set of the user nodes; identifying an abnormal node candidate set based on the topological feature set; based on the abnormal node candidate set, analyzing a group cooperation behavior; and completing false user identification based on group cooperation behaviors. According to the method, the hidden false users and group behaviors thereof can be accurately identified by constructing the dynamic knowledge graph, integrating the dynamic characteristics of the user behaviors and the relationship and combining topological structure analysis and semantic reasoning technologies. Compared with the prior art, the method can effectively deal with complex and disguised false users, capture the dynamic evolution and group cooperation behaviors of the relationship between the users, and reveal the hidden mode of the false user group from the global perspective, thereby improving the authenticity and security of the social media platform, and reducing the propagation of false information.

An object of a system according to an embodiment is to increase the value of a post on social media and propose a next posting theme.SOLUTION: A system according to an embodiment includes a post optimization unit, a reaction analysis unit, a response suggestion unit, and a multimodal support unit. The post optimizer optimizes the post using the RAG. The reaction analysis part analyzes the contribution contents optimized by the contribution optimization part. The response suggestion unit suggests a next posting theme on the basis of the reaction analyzed by the reaction analysis unit. The multimodal support unit supports multimodal posting based on the next posting theme suggested by the response suggestion unit.SELECTED DRAWING: Figure 1

A method and a system for performing domain adaptations of social media text by using lexical data transformations are provided. The method includes: receiving a first data set that is usable for training a machine learning (ML) model that is designed to perform natural language processing tasks; training the ML model by using the first data set; receiving a second data set that relates to a social media platform; transforming a subset of the first data set into a third data set that is suitable for the social media platform; and retraining the ML model by using a combination of the first data set, the second data set, and the third data set. The transformations may include injecting emojis, emoticons, user mention indicators, hashtags, retransmission indicators, URLs, and / or inverse lexical normalizations that are often used in social media posts.

The invention belongs to the technical field of natural language processing, and provides a social media sentiment analysis method, system, equipment and medium in order to solve the problems that existing low-resource language training data is less and robustness of multiple modes on alignment and fusion is poor. The sentiment analysis model comprises a plurality of single-mode encoders and a multi-mode fusion model; transferring the characterization capability of the fine-tuned large model to a single-mode encoder by using a knowledge distillation technology; in fusion model training, a multi-head attention mechanism is utilized to calculate an alignment relationship between a text and a video and an alignment relationship between audio and text-video alignment features, and an optimal transmission alignment constraint is introduced to force an attention matrix to be consistent with an optimal transmission scheme, so that feature alignment is realized, and sentiment analysis accuracy is improved.

The invention relates to the technical field of information retrieval, in particular to a social media text emotion-based user tendency prediction system, which comprises a literacy text multi-dimensional credit investigation module, a literacy text multi-dimensional credit investigation module, a literacy text multi-dimensional credit investigation module, a literacy text multi-dimensional credit investigation module, a literacy text multi-dimensional credit investigation module, a literacy text multi-dimensional credit investigation module and a literacy text multi-dimensional credit investigation module, and establishing a literacy tendency basic index unit. According to the method, the semantic vector is extracted based on the input social media text and mapped to the topic ontology library to obtain the topic code, a semantic and topic dual index structure is constructed, accurate matching of the retrieval view and the user request in the multi-dimensional feature space is achieved, and the fine granularity and the dynamic adaptive capacity of the index are improved; by means of incremental updating of a micro-cluster statistical summary, a macro-cluster is formed based on center point distance real-time aggregation, the real-time evolution trend of emotional tendency of a user is captured, and the problem that an existing retrieval technology lacks timely tracking of dynamic data is solved.

The invention discloses an urban park perception value evaluation method based on multi-modal data fusion, and the method comprises the steps: extracting a multi-dimensional perception tag and an emotion intensity score from park social media text comment data through a pre-trained language model, and carrying out the aggregation of the multi-dimensional perception tag and the emotion intensity score, and generating a subjective perception result; performing element identification on the image comment data of the park social media by using the three models to obtain different scene element coverage rates, character / facility counts and language representations of visual elements; constructing a plurality of objective environment indexes to describe different environment states, extracting variable information from three model output results, POI data, remote sensing land class proportion and road network information, and constructing mapping with the corresponding objective environment indexes to quantify each objective environment index; and aligning and outputting the subjective perception result and the objective environment index in the park and period dimensions. According to the method, automatic quantitative evaluation of subjective and objective characteristics of urban parks can be realized on the national scale, and standardized technical support is provided for urban renewal and ecological management.

The invention provides a vehicle public opinion intelligent analysis method and system based on the Internet of Vehicles, a product and a vehicle, and relates to the technical field of vehicle public opinion analysis. The emotion contribution degree of each data source is dynamically evaluated and weighted by using a cross-modal attention mechanism, so that the one-sidedness of single-modal analysis is effectively overcome, and the accuracy and robustness of public opinion emotion judgment are improved; further deeply mining public opinion propagation information by means of a space-time diagram neural network, constructing a propagation path model, generating an event-emotion-propagation force three-dimensional thermodynamic diagram, and realizing visual insight and trend prediction of a public opinion evolution situation; on the basis, a local and external collaborative grading emergency response mechanism is constructed, user anxiety is relieved, and diffusion of bad public opinions is effectively restrained.

A wearable augmented reality device enables rapid, personalized messaging through a body-anchored communication interface. The device captures image data via cameras, detects and tracks a body part of the wearer, and displays a user interface anchored to the detected body part. The interface comprises user interface elements representing social connections. Upon detecting selection of an element, the device activates a microphone to record an audio message for delivery to the corresponding social connection(s). The interface may be dynamically updated to reflect changes in relationship attributes, messaging activity, or connection status. User interface elements can be ordered based on social connection scores or communication recency, with additional graphical elements indicating device types or custom avatars. This system facilitates seamless integration of digital communication with the physical world, enhancing social media and instant messaging experiences in mixed reality environments.

The invention provides a method and device for acquiring OG data of multiple social media platforms through links, and belongs to the technical field of data acquisition and processing. The method comprises the following steps: acquiring URL links from a plurality of social media platforms and common webpages as source data, wherein the source data correspond to webpage OG data lt; carrying out metagt; the label form is embedded into the lt of the HTML; carrying out headt; a label; performing validity check and standardization on the URL link, capturing webpage content by using an asynchronous HTTP (Hyper Text Transport Protocol) request library, and loading a complete HTML (Hyper Text Markup Language) on a dynamic webpage through a headless browser; the HTML positioning result is analyzed; carrying out metagt; tag: extracting an OG standard field and a platform extension field, and deducing a missing field from other HTML tags; mapping each platform OG field into a unified standard field; and outputting the OG data containing fields such as title and the like through the standardized API interface. According to the method, the multi-platform OG data acquisition efficiency, accuracy and safety are improved, good expansibility is achieved, and the cross-platform data integration requirement is met.