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Los Angeles, CA, United States

Techniques are disclosed for dynamically placing, scheduling, and adjusting promotional items based on momentum of activities of a targeted audience in a network. An example method comprises selecting a group of users from a user population in a network. The selection can be based on a degree of association between (i) multiple key words and (ii) profile data or past activities from the user population. The method further comprises monitoring activities performed in the network by the group of users. The method further comprises identifying a topic from the activities. The method further comprises determining momentum for the topic. The momentum can be proportional to (i) a number of how many users whose activities mention the topic and (ii) a frequency of how often the topic is mentioned. The method further comprises selectively delivering promotional content to the group of users based on the momentum for the identified topic.


Techniques are disclosed for predicting momentum of topic in the future in automatically optimizing placement of promotional items or content (e.g., advertisements) in a network. An example method comprises selecting a group of users from a user population in a network based on, for example, a degree of association between (i) multiple key words and (ii) profile data or past activities from the user population. The method further comprises monitoring activities performed in the network by the group of users. The method further comprises identifying a topic from the activities, and determining momentum for the topic. The method further comprises predicting a future momentum for the identified topic based on observing additional topics which are related to the identified topic and have momentums with a rate of growth that exceeds a positive or negative threshold.


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 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.

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