Abstract: A computer-implemented method and a computer system for retrieving relevant records from a database. A project request is received from a user device over a computer network, the project request including a description of a project for which an expert consultation is desired. The request is parsed for categories and keywords. Relevant prediction models are determined based on the categories, each of the plurality of prediction models being based on a different aspect of expert search. Each of the relevant prediction models is applied to at least one of the keywords to obtain search criteria that is used to query the database.

Abstract: In social data networks, it is difficult for a computing system to automatically identify gender attributes associated with user accounts because of incorrect, incomplete or non-existent data associated with the user account profile. Therefore, a computing system is provided that retrieves user account data and related text data, and that uses classification to identify gender data. Label propagation computations based on the connections in the social data network are used to infer the gender information of many user accounts at the same time.

Abstract: In social data networks, it is difficult for a computing system to automatically identify age attributes associated with user accounts because of incorrect, incomplete or non-existent data associated with the user account profile. Therefore, a computing system is provided that retrieves user account data and related text data, and that uses classification to identify age data. Label propagation computations based on the connections in the social data network are used to infer the age information of many user accounts at the same time.

Abstract: The influence spread can be efficiently increased, or even maximized, by utilizing a multi-processing platform such as a GPU (e.g. Graphics Processing Unit), multi-core processor, etc. It is herein provided that a large graph of a social data network comprising nodes (e.g. the user accounts) and edges (e.g. the data links or relationships between the user accounts) may be separated into local subgraph trees, and that these local subgraph trees may be independently processed in parallel threads on the multi-processing platform. A particular example of such a platform is a GPU-based environment that includes many threads.

Abstract: Social media networks have become a primary source for news and opinions on topics ranging from sports to politics. Sentiment analysis is typically constrained to two classes—positive and negative. A computing system is herein described for building a multi-sentiment multi-label model for electronic data that uses emojis as class labels. The electronic messages are classified into six sentiment classes. The computing system collects and creates a large corpus of clean and processed training data with emoji-based sentiment classes using little-to-no manual intervention. A threshold-based formulation is used to assign one or two class labels (multi-label) to an electronic message. The multi-sentiment multi-label model produces a desirable cross validation accuracy.

Abstract: In social data networks, it is difficult for a computing system to automatically identify demographic attributes associated with user accounts because of incorrect, incomplete or non-existent data associated with the user account profile. Therefore, a computing system is provided that retrieves user account data and related text data, and that uses Deep Learning computations to infer demographic attributes about a given user based on the text data that they generate. The text is processed, and then inputted into a bi-gram neural network to generate an initial feature vector. This initial feature vector is inputted into a Deep Learning neural network in order to generate a secondary feature vector. The secondary feature vector is inputted into a forward neural network to generate one or more values indicating a specific demographic attribute associated with the given user account.

Abstract: Determining a location of a user on a social network platform is difficult due to incorrect information or lack of information associated with the user. A system and method are provided to compute contextual similarity. This includes, for example, computing content similarity between seed users and followers/friends, as well as computing an engagement score between seed users and followers/friends. The system also computes geo-social-spatial similarity. The similarity scores are used in any inference computation to infer the geo-locations of the followers of the seed users, and subject users who share common friends with the seed users. The user geo-location inference database is updated using the result. Other seed users are selected, and the process is repeated.

Abstract: A system and method are provided for determining influence spread in social networks. The method includes generating a plurality of samples using a computing device, each sample corresponding to a collection of all edge weights for a social network graph topology; and allocating, by the computing device, the plurality of samples into at least one batch, a size of which is being determined according to a number of threads and global memory space available in a multi-processor platform. For each batch, the method includes parallel processing the samples in that batch using the multi-processor platform to generate results corresponding to a spread of each graph node per sample in that batch; storing results of that batch in the global memory accessible to the multi-processor platform; and sending the results to the computing device.