Abstract: Techniques for linguistic personalization of messages for targeted campaigns are described. In one or more implementations, dependencies between keywords and modifiers are extracted, from one or more segment-specific texts and a product-specific text, to build language models for the one or more segment specific texts and the product specific text. Modifiers with a desired sentiment are extracted from the product specific text and transformation points are identified in a message skeleton. Then one or more of the extracted modifiers are inserted to modify one or more identified keywords in the message skeleton to create a personalized message for a target segment of the targeted marketing campaign.

Abstract: Techniques are disclosed for identifying the same online user across different communication networks, and further creating a unified profile for that user. The unified profile is an aggregation of publicly available user profile attributes across the different networks. In an embodiment, the techniques are implemented as a computer implemented methodology, including: (1) feature space analysis to identify relevant user features that allows for clusterization of the given target network(s), (2) unsupervised candidate selection to identify one or more candidate user profiles from each target network and that are likely belonging to a target user or so-called queried user, and (3) supervised user identification to identify a likely matching user profile for that target user from each target network. A unified user profile can then be built from data taken from all matched user profiles, and effectively allows a marketer to better understand that user and hence execute more informed targeting.

Abstract: Techniques and systems for auditing and augmenting user-generated tags for digital content are described. A corpus is generated for each tag associated with digital content to represent different aspects of the tag's definition. A hierarchy of semantic relationships between the tags is created based a corpus co-occurrence between the tags to relate at least two tags having different levels of specificity. Some of the tags are verified with the digital content based on feature detection, and a correspondence to the digital content is determined for each verified tag. The correspondence of the verified tags is then propagated up the hierarchy to others of the tags that are semantically related to the verified tags but which were not verified by the feature detection. The verified tags and the verified other tags are then assigned to the digital content to control how the digital content is retrieved when subject to a search.

Abstract: This document describes apparatuses and techniques for monitoring social media for breach of organizational privacy. In some aspects, these techniques receive social media content from a social media site or social media stream. The social media content is compared with organizational information that includes private information and public information to determine an amount of private data disclosed by the social media content. Based on a ratio of amounts of the private data and public data disclosed, a score is provided that indicates a degree to which the social media content breaches organizational privacy.

Abstract: Social media posts related to a topic are analyzed over time by parsing the posts to identify terms and by statistically analyzing occurrences and co-occurrences of the terms in the posts to derive metrics. A relationship-based structure is updated over time based on the metrics. A relationship-based structure is updated over time based on the metrics. In an example, the relationship-based structure includes weighted nodes and edges. The nodes represent terms in the posts and the edges represent co-occurrences of the terms. The weights of the nodes depend on frequencies of the occurrences, while as the weights of the edges depend on frequencies of the co-occurrences. A trend in the social media posts is detected by identifying a change over time in the relationship-based data structure.

Abstract: This document describes apparatuses and techniques for monitoring social media for breach of organizational privacy. In some aspects, these techniques receive social media content from a social media site or social media stream. The social media content is compared with organizational information that includes private information and public information to determine an amount of private data disclosed by the social media content. Based on a ratio of amounts of the private data and public data disclosed, a score is provided that indicates a degree to which the social media content breaches organizational privacy.

Abstract: Techniques and systems for auditing and augmenting user-generated tags for digital content are described. A corpus is generated for each tag associated with digital content to represent different aspects of the tag's definition. A hierarchy of semantic relationships between the tags is created based a corpus co-occurrence between the tags to relate at least two tags having different levels of specificity. Some of the tags are verified with the digital content based on feature detection, and a correspondence to the digital content is determined for each verified tag. The correspondence of the verified tags is then propagated up the hierarchy to others of the tags that are semantically related to the verified tags but which were not verified by the feature detection. The verified tags and the verified other tags are then assigned to the digital content to control how the digital content is retrieved when subject to a search.

Abstract: Social media posts related to a topic are analyzed over time by parsing the posts to identify terms and by statistically analyzing occurrences and co-occurrences of the terms in the posts to derive metrics. A relationship-based structure is updated over time based on the metrics. A relationship-based structure is updated over time based on the metrics. In an example, the relationship-based structure includes weighted nodes and edges. The nodes represent terms in the posts and the edges represent co-occurrences of the terms. The weights of the nodes depend on frequencies of the occurrences, while as the weights of the edges depend on frequencies of the co-occurrences. A trend in the social media posts is detected by identifying a change over time in the relationship-based data structure.

Abstract: Techniques for linguistic personalization of messages for targeted campaigns are described. In one or more implementations, dependencies between keywords and modifiers are extracted, from one or more segment-specific texts and a product-specific text, to build language models for the one or more segment specific texts and the product specific text. Modifiers with a desired sentiment are extracted from the product specific text and transformation points are identified in a message skeleton. Then one or more of the extracted modifiers are inserted to modify one or more identified keywords in the message skeleton to create a personalized message for a target segment of the targeted marketing campaign.

Abstract: Techniques are disclosed for identifying the same online user across different communication networks, and further creating a unified profile for that user. The unified profile is an aggregation of publicly available user profile attributes across the different networks. In an embodiment, the techniques are implemented as a computer implemented methodology, including: (1) feature space analysis to identify relevant user features that allows for clusterization of the given target network(s), (2) unsupervised candidate selection to identify one or more candidate user profiles from each target network and that are likely belonging to a target user or so-called queried user, and (3) supervised user identification to identify a likely matching user profile for that target user from each target network. A unified user profile can then be built from data taken from all matched user profiles, and effectively allows a marketer to better understand that user and hence execute more informed targeting.