Abstract: A method of detecting interest in a topic by user devices of a discoverable network includes building by a target server a vector index of articles accessible on a digital data network, querying for an item the vector index over the digital data network by a device, obtaining a set of articles by the target server corresponding to the item, feeding back by the target server over the digital data network, identifiers of the set of articles, to a network server of the discoverable network communicatively connected to the digital data network. The method may also include making accessible by the network server a set of addresses for user devices interacting with articles of the set of articles. The set of addresses of the user devices may be used for retargeting advertisements relevant to the topic.

Abstract: A method for identifying respective networked devices in a social communications network, which networked devices communicate messages relevant to an article. The messages may include posts, shares, likes, votes, ranks, and other comments. A server of the social communications network maintains a respective identity (e.g., user name or handle) for each connected networked device, respectively, of the social communications network. The server includes an API for requests.

Abstract: A system for building sets of user devices communicatively connected to a communications network, having a common or related topical interest in items available via communications over the network by the user devices with an item server. The user devices, respectively, have respective network identifiers and each exhibits related topical activity of particular articles in a subnetwork of the communications network, for example, in a social communications network. A server communicatively connected to the subnetwork detects communicative interactions over the subnetwork of the user devices with the particular articles.

Abstract: A method for identifying respective networked devices in a social communications network, which networked devices communicate messages relevant to an article. The messages may include posts, shares, likes, votes, ranks, and other comments. A server of the social communications network maintains a respective identity (e.g., user name or handle) for each connected networked device, respectively, of the social communications network. The server includes an API for requests.

Abstract: A method for identifying respective networked devices in a social communications network, which networked devices communicate messages relevant to an article. The messages may include posts, shares, likes, votes, ranks, and other comments. A server of the social communications network maintains a respective identity (e.g., user name or handle) for each connected networked device, respectively, of the social communications network. The server includes an API for requests.

Abstract: A method of detecting interest in a topic by user devices of a discoverable network includes building by a target server a vector index of articles accessible on a digital data network, querying for an item the vector index over the digital data network by a device, obtaining a set of articles by the target server corresponding to the item, feeding back by the target server over the digital data network, identifiers of the set of articles, to a network server of the discoverable network communicatively connected to the digital data network. The method may also include making accessible by the network server a set of addresses for user devices interacting with articles of the set of articles. The set of addresses of the user devices may be used for retargeting advertisements relevant to the topic.

Abstract: A method for identifying respective networked devices in a social communications network, which networked devices communicate messages relevant to an article. The messages may include posts, shares, likes, votes, ranks, and other comments. A server of the social communications network maintains a respective identity (e.g., user name or handle) for each connected networked device, respectively, of the social communications network. The server includes an API for requests.

Abstract: A method for identifying respective networked devices in a social communications network, which networked devices communicate messages relevant to an article. The messages may include posts, shares, likes, votes, ranks, and other comments. A server of the social communications network maintains a respective identity (e.g., user name or handle) for each connected networked device, respectively, of the social communications network. The server includes an API for requests.

Abstract: A system for building sets of user devices communicatively connected to a communications network, having a common or related topical interest in items available via communications over the network by the user devices with an item server. The user devices, respectively, have respective network identifiers and each exhibits related topical activity of particular articles in a subnetwork of the communications network, for example, in a social communications network. A server communicatively connected to the subnetwork detects communicative interactions over the subnetwork of the user devices with the particular articles.

Abstract: A method for identifying respective networked devices in a social communications network, which networked devices communicate messages relevant to an article. The messages may include posts, shares, likes, votes, ranks, and other comments. A server of the social communications network maintains a respective identity (e.g., user name or handle) for each connected networked device, respectively, of the social communications network. The server includes an API for requests.

Abstract: A method for identifying respective networked devices in a social communications network, which networked devices communicate messages relevant to an article. The messages may include posts, shares, likes, votes, ranks, and other comments. A server of the social communications network maintains a respective identity (e.g., user name or handle) for each connected networked device, respectively, of the social communications network. The server includes an API for requests.

Abstract: A method monitors topical activity among networked devices of social communications networks. Related messages, such as posts, shares, likes, and others, are communicated between devices of the social networks. Each social network maintains a share count of related messages. A data network, such as the Internet, includes sources of articles. The method includes receiving a search item from the data network, collecting articles related to the search item from article sources, storing a unique identifier for each collected article, comparing the search item to the unique identifiers to determine similarity, compiling a result set of any similar unique identifiers, communicatively detecting, for the result set, respective share counts of related messages for each social network, and deriving a collective score based on all share counts. The score represents topical activity for the search item. The score is reported, and relevant articles may also be reported.

Abstract: A method for identifying respective networked devices in a social communications network, which networked devices communicate messages relevant to an article. The messages may include posts, shares, likes, votes, ranks, and other comments. A server of the social communications network maintains a respective identity (e.g., user name or handle) for each connected networked device, respectively, of the social communications network. The server includes an API for requests.

Abstract: A method monitors topical activity among networked devices of social communications networks. Related messages, such as posts, shares, likes, and others, are communicated between devices of the social networks. Each social network maintains a share count of related messages. A data network, such as the Internet, includes sources of articles. The method includes receiving a search item from the data network, collecting articles related to the search item from article sources, storing a unique identifier for each collected article, comparing the search item to the unique identifiers to determine similarity, compiling a result set of any similar unique identifiers, communicatively detecting, for the result set, respective share counts of related messages for each social network, and deriving a collective score based on all share counts. The score represents topical activity for the search item. The score is reported, and relevant articles may also be reported.

Abstract: The sentimental significance of a group of historical documents related to a topic is assessed with respect to change in an extrinsic metric for the topic. A unique sentiment binding label is included to the content of actions documents that are determined to have sentimental significance and the group of documents is inserted into a historical document sentiment vector space for the topic. Action areas in the vector space are defined from the locations of action documents and singular sentiment vector may be created that describes the cumulative action area. Newly published documents are sentiment-scored by semantically comparing them to documents in the space and/or to the singular sentiment vector. The sentiment scores for the newly published documents are supplemented by human sentiment assessment of the documents and a sentiment time decay factor is applied to the supplemented sentiment score of each newly published documents.

Abstract: The sentimental significance of a group of historical documents related to a topic is assessed with respect to change in an extrinsic metric for the topic. A unique sentiment binding label is included to the content of actions documents that are determined to have sentimental significance and the group of documents is inserted into a historical document sentiment vector space for the topic. Action areas in the vector space are defined from the locations of action documents and singular sentiment vector may be created that describes the cumulative action area. Newly published documents are sentiment-scored by semantically comparing them to documents in the space and/or to the singular sentiment vector. The sentiment scores for the newly published documents are supplemented by human sentiment assessment of the documents and a sentiment time decay factor is applied to the supplemented sentiment score of each newly published documents.

Abstract: Disclosed information exploration system and method embodiments operate on a document set to determine a document cluster hierarchy. An exclusionary phrase index is determined for each cluster, and representative phrases are selected from the indexes. The selection process may enforce pathwise uniqueness and balanced sub-cluster representation. The representative phrases may be used as cluster labels in an interactive information exploration interface.

Abstract: A term-by-document matrix is compiled from a corpus of documents representative of a particular subject matter that represents the frequency of occurrence of each term per document. A weighted term dictionary is created using a global weighting algorithm and then applied to the term-by-document matrix forming a weighted term-by-document matrix. A term vector matrix and a singular value concept matrix are computed by singular value decomposition of the weighted term-document index. The k largest singular concept values are kept and all others are set to zero thereby reducing to the concept dimensions in the term vector matrix and a singular value concept matrix. The reduced term vector matrix, reduced singular value concept matrix and weighted term-document dictionary can be used to project pseudo-document vectors representing documents not appearing in the original document corpus in a representative semantic space.

Abstract: A term-by-document matrix is compiled from a corpus of documents representative of a particular subject matter that represents the frequency of occurrence of each term per document. A weighted term dictionary is created using a global weighting algorithm and then applied to the term-by-document matrix forming a weighted term-by-document matrix. A term vector matrix and a singular value concept matrix are computed by singular value decomposition of the weighted term-document index. The k largest singular concept values are kept and all others are set to zero thereby reducing to the concept dimensions in the term vector matrix and a singular value concept matrix. The reduced term vector matrix, reduced singular value concept matrix and weighted term-document dictionary can be used to project pseudo-document vectors representing documents not appearing in the original document corpus in a representative semantic space.