Los Angeles, CA, United States
Los Angeles, CA, United States

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Systems and methods for analyzing messages in a network or across networks are disclosed. In one aspect, embodiments of the present disclosure include a method, which may be implemented on a system, for determining the interests of the user from online activity of the user. Thee online activity of the user is automatically detected from those activities of the user on or via the online media services without requiring additional interaction or input from the user. The method can further include increasing visibility of those incoming messages which are more interesting to the user among other incoming messages in the stream for presentation in a user interface. In one embodiment, the user interface is a part of a platform which is independent of any of the online media services.


A system and a method for microcontent natural language processing are presented. The method comprising steps of receiving a microcontent message from a social networking server, tokenizing the microcontent message into one or more text tokens, performing a topic extraction on the microcontent message to extract topic metadata, generating sentiment metadata for the microcontent message, analyzing co-occurrence of all available metadatas in the plurality of microcontent messages, producing a list that ranks the plurality of microcontent messages based on all available topic metadata, and compiling a trend database that reveals how perception of users of the social networking server on a given topic changes by tracking how the list changes over time.


An adaptive system architecture for identifying popular topics from messages are disclosed. In one aspect, one example of a system which includes a client device which determines, from the set of messages, commonly or frequently occurring topics and computes at least a portion of the analytics for the commonly or frequently occurring topics in the set of messages that indicate respective levels of popularity. Other portions of the analytics used to determine the respective levels of trendiness of the commonly or frequently occurring topics can be computed by other client devices. Such computation can take place in the web-browsers forming a web-based crowd computing platform for message stream analysis.


A system and a method for trending of aggregated personalized information streams and multi-dimensional graphical depiction thereof are disclosed. The method, which may be embodied on a system, includes retrieving a plurality of social media objects that relates to a focused social media object from social media sites; determining relationships between the social media objects; and/or presenting, at a graphic interface, a network diagram including nodes and lines. In one embodiment, each individual node of the nodes represents one of the social media objects. Each individual line of the lines represents one of the relationships between two of the social media objects that are represented by two of the nodes being connected by the individual line.


Systems and methods for presenting a graphical visualization of user related content in a network or across networks are disclosed. In one aspect, embodiments of the present disclosure include analyzing the content from within the network or across the networks, identifying trending topics, and customizing the graphical visualization based on a given topic. The given topic can be user specified and/or can be based on implicit and/or explicit user interests or preferences. The given topic can also be administrator specified. The graphical visualization can present the topics as being connected to the given topic, where the given topic is presented as a center node and the topics relating to that given topic are arranged radially from the center node. The trending topics can change based on a configurable timeframe (e.g., minutes, days, weeks, etc.).


A system and a method for trending of aggregated personalized information streams and multi-dimensional graphical depiction thereof are disclosed. The method, which may be embodied on a system, includes retrieving a plurality of social media objects that relates to a focused social media object from social media sites; determining relationships between the social media objects; and/or presenting, at a graphic interface, a network diagram including nodes and lines. In one embodiment, each individual node of the nodes represents one of the social media objects. Each individual line of the lines represents one of the relationships between two of the social media objects that are represented by two of the nodes being connected by the individual line.


Targeted advertising based on trending aggregated personalized information streams is disclosed. The method of targeted advertising, which can be implemented on a system includes receiving a topic from a user, wherein the user wants to present advertisements on social media platforms related to the topic, retrieving social media messages that relates to the topic from the social media platforms, identifying trending keywords associated with the social media messages that relates to the topic, and/or presenting the advertisement including the trending keywords on the social media platforms.


Systems and methods for analyzing messages in a network or across networks are disclosed. In one aspect, embodiments of the present disclosure include a system for processing incoming messages from multiple web-based services. The system can include, a processing unit, a memory unit having stored thereon instructions which when executed by the processing unit causes the processing unit to perform a method which can, perform an action on an incoming message in accordance with a rule set to process the incoming messages. The rule set can be determined as a result of machine learning. in one embodiment, the action is performed by an independent service, the independent service being independent from the multiple web-based services.


Systems and methods for analyzing messages in a network or across networks are disclosed. In one aspect, embodiments of the present disclosure include a method, which may be implemented on a system, for detecting trends from a set of messages in a network or across networks. The method can include, identifying, from the set of messages in the network or across networks, commonly or frequently occurring topics, computing statistical attributes for the commonly or frequently occurring topics in the set of messages that indicate respective levels of trendiness, and/or presenting, the commonly or frequently occurring topics as indicators in a user interface, the indicators being actionable to access additional information relating to a selected topic via the action. The set of messages include messages interacted with by humans or machines and interactions can include one or more of, posted a message, shared a message, liked a message, commented on a message, replied to a message, viewed a message, saved or bookmarked a message.


The disclosed techniques can provide users with a tool having an integrated, user-friendly interface and having automated mechanisms which can reveal correlations between data streams to the users in a clear and easily understandable way, thereby enabling the users to easily digest the vast amount of information contained in activities within one or more network, to understand the correlations among the activities, to stay informed and responsive to current or new trends, and even to predict future trends. Among other benefits, the disclosed techniques are especially useful in the context of discovering impacts of social networking activities on other types of commercial activities.

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