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: 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: 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.

Abstract: Data infrastructure systems and methods are provided for updating a continuously evolving social network. The social network is representable by a social network graph. A client application and graph data are retrieved from a database that stores the social network graph, in order to determine current activity in the social network. The current activity is used to determine one or more priority nodes in the social network graph to be updated. Social network updates are obtained for each of the one or more priority nodes. The one or more priority nodes in the social network graph are updated using the social network updates.

Abstract: A system and method performed by a server for determining at least one user account that is influential for a topic: obtaining the topic; determining a plurality of user accounts within a social data network that are related to the topic; representing each of the user accounts as a node in a connected graph and determining an existence of a relationship between each of the user accounts; computing a topic network graph using each of the user accounts as nodes and the corresponding relationships as edges between each of the nodes; ranking the user accounts within the topic network graph to filter outlier nodes within the topic network graph; identifying at least two distinct communities amongst the user accounts within the filtered topic network graph, each community associated with a subset of the user accounts; identifying attributes associated with each community; outputting each community associated with the corresponding attributes.

Abstract: There is provided methods and systems for analyzing data from a plurality of users within a social networking platform, comprising: receiving a query for a topic associated with the social networking platform; determining a set of users having at least one social networking behavior on the social networking platform related to the topic; selecting, for each user from the set of users, a pre-defined number of posts and associating each of the pre-defined number of posts with the respective user; segmenting the selected posts for each user to determine a likelihood of each of the selected posts among the set of users; and, clustering the selected posts for each user to define a plurality of clusters and determining a mapping from each user to at least one of the plurality of clusters, each cluster comprising representative topics indicating frequently used topics within the cluster for the pre-defined number of posts between the set of users.

Abstract: A system and a method are provided for continuously analysing and procuring advertisements. Social data is obtained and is used to identify one or more relationships. The operations further include: modifying or determining a target set based on the one or more relationships, the target set comprising a combination of inputs and a target audience, the inputs comprising a search algorithm for identifying the target audience; presenting the target set when a proposed advertising campaign is detected; procuring the proposed advertising campaign using the target audience to generate a procured advertisement; obtaining feedback about the procured advertisement; and further modifying the target set based on the feedback.

Abstract: System and methods performed by a server for determining a target group of users in a social data network, including: obtaining identities of friends from an initial group of users, where a user in the group follows one or more of the friends; determining N number friends that are most frequently occurring amongst the identities of friends from the initial group; for each of the N number friends, obtaining identities of followers following a given one of the N friends; filtering out one or more followers from the identities of the followers that follow less than X number of the N number of friends, where X?N; and including remaining ones of the identities of the followers in the target group of users

Abstract: System and methods performed by a server for determining weighted influence in social networks, including: determining posts related to the topic; characterizing each post as one or more of: a reply post, a mention post, and a re-posting; generating a group of user accounts comprising any user account that authored a posting to which is replied, being mentioned in the mention post, that posted content being re-posted, and/or that authored one or more posts that are related to the topic; representing each of the user accounts as a node in the group in a connected graph and establishing an edge between one or more pair of nodes when there is a follower-followee relationship between the nodes; and for each edge between nodes, determining a weighting that is a function of one or more of: a number of mention posts, a number of reply posts, and a number of re-posts.