Abstract: A method includes receiving a first post from an electronic source including first content; determining a source identifier; determining an attribute for the source by broadcasting the first post to a first plurality of filter graph definitions configured to identify attributes of sources according to the respective filter graph definition; and storing in memory, as a source profile identified by the source identifier for the source, the attribute for the source; receiving a second post from the source including second content; determining a source identifier; using the source identifier, querying the memory to access the source profile; correlating the second post with attributes of the source stored in the source profile to produce a correlated second post; and broadcasting the correlated second post to a second plurality of filter graph definitions configured to identify posts with high value information according to the respective filter graph definition.

Abstract: A method includes receiving a first post from an electronic source including first content; determining a source identifier; determining an attribute for the source by broadcasting the first post to a first plurality of filter graph definitions configured to identify attributes of sources according to the respective filter graph definition; and storing in memory, as a source profile identified by the source identifier for the source, the attribute for the source; receiving a second post from the source including second content; determining a source identifier; using the source identifier, querying the memory to access the source profile; correlating the second post with attributes of the source stored in the source profile to produce a correlated second post; and broadcasting the correlated second post to a second plurality of filter graph definitions configured to identify posts with high value information according to the respective filter graph definition.

Abstract: A method is provided for automatically discovering topics in electronic posts, such as social media posts. The method includes receiving a corpus that includes a plurality of electronic posts. The method further includes identifying a plurality of candidate terms within the corpus and selecting, as a trimmed lexicon, a subset of the plurality of candidate terms using predefined criteria. The method further includes clustering at least a subset of the plurality of electric posts according to a plurality of clusters using the lexicon to produce a plurality of statistical topic models. The method further includes storing information corresponding to the statistical topic models.

Abstract: A method includes receiving a first post from an electronic source including first content; determining a source identifier; determining an attribute for the source by broadcasting the first post to a first plurality of filter graph definitions configured to identify attributes of sources according to the respective filter graph definition; and storing in memory, as a source profile identified by the source identifier for the source, the attribute for the source; receiving a second post from the source including second content; determining a source identifier; using the source identifier, querying the memory to access the source profile; correlating the second post with attributes of the source stored in the source profile to produce a correlated second post; and broadcasting the correlated second post to a second plurality of filter graph definitions configured to identify posts with high value information according to the respective filter graph definition.

Abstract: A method includes receiving a first post from an electronic source including first content; determining a source identifier; determining an attribute for the source by broadcasting the first post to a first plurality of filter graph definitions configured to identify attributes of sources according to the respective filter graph definition; and storing in memory, as a source profile identified by the source identifier for the source, the attribute for the source; receiving a second post from the source including second content; determining a source identifier; using the source identifier, querying the memory to access the source profile; correlating the second post with attributes of the source stored in the source profile to produce a correlated second post; and broadcasting the correlated second post to a second plurality of filter graph definitions configured to identify posts with high value information according to the respective filter graph definition.

Abstract: A method is provided for automatically discovering topics in electronic posts, such as social media posts. The method includes receiving a corpus that includes a plurality of electronic posts. The method further includes identifying a plurality of candidate terms within the corpus and selecting, as a trimmed lexicon, a subset of the plurality of candidate terms using predefined criteria. The method further includes clustering at least a subset of the plurality of electric posts according to a plurality of clusters using the lexicon to produce a plurality of statistical topic models. The method further includes storing information corresponding to the statistical topic models.

Abstract: A method includes receiving a first post from an electronic source including first content; determining a source identifier; determining an attribute for the source by broadcasting the first post to a first plurality of filter graph definitions configured to identify attributes of sources according to the respective filter graph definition; and storing in memory, as a source profile identified by the source identifier for the source, the attribute for the source; receiving a second post from the source including second content; determining a source identifier; using the source identifier, querying the memory to access the source profile; correlating the second post with attributes of the source stored in the source profile to produce a correlated second post; and broadcasting the correlated second post to a second plurality of filter graph definitions configured to identify posts with high value information according to the respective filter graph definition.

Abstract: A method is provided for automatically discovering topics in electronic posts, such as social media posts. The method includes receiving a corpus that includes a plurality of electronic posts. The method further includes identifying a plurality of candidate terms within the corpus and selecting, as a trimmed lexicon, a subset of the plurality of candidate terms using predefined criteria. The method further includes clustering at least a subset of the plurality of electronic posts according to a plurality of clusters using the lexicon to produce a plurality of statistical topic models. The method further includes storing information corresponding to the statistical topic models.

Abstract: A method is provided for automatically discovering topics in electronic posts, such as social media posts. The method includes receiving a corpus that includes a plurality of electronic posts. The method further includes identifying a plurality of candidate terms within the corpus and selecting, as a trimmed lexicon, a subset of the plurality of candidate terms using predefined criteria. The method further includes clustering at least a subset of the plurality of electronic posts according to a plurality of clusters using the lexicon to produce a plurality of statistical topic models. The method further includes storing information corresponding to the statistical topic models.

Abstract: A method is provided for automatically discovering topics in electronic posts, such as social media posts. The method includes receiving a corpus that includes a plurality of electronic posts. The method further includes identifying a plurality of candidate terms within the corpus and selecting, as a trimmed lexicon, a subset of the plurality of candidate terms using predefined criteria. The method further includes clustering at least a subset of the plurality of electronic posts according to a plurality of clusters using the lexicon to produce a plurality of statistical topic models. The method further includes storing information corresponding to the statistical topic models.

Abstract: A method is provided for automatically discovering topics in electronic posts, such as social media posts. The method includes receiving a corpus that includes a plurality of electronic posts. The method further includes identifying a plurality of candidate terms within the corpus and selecting, as a trimmed lexicon, a subset of the plurality of candidate terms using predefined criteria. The method further includes clustering at least a subset of the plurality of electronic posts according to a plurality of clusters using the lexicon to produce a plurality of statistical topic models. The method further includes storing information corresponding to the statistical topic models.

Abstract: A method is provided for automatically discovering topics in electronic posts, such as social media posts. The method includes receiving a corpus that includes a plurality of electronic posts. The method further includes identifying a plurality of candidate terms within the corpus and selecting, as a trimmed lexicon, a subset of the plurality of candidate terms using predefined criteria. The method further includes clustering at least a subset of the plurality of electronic posts according to a plurality of clusters using the lexicon to produce a plurality of statistical topic models. The method further includes storing information corresponding to the statistical topic models.

Abstract: A method is provided for automatically discovering topics in electronic posts, such as social media posts. The method includes receiving a corpus that includes a plurality of electronic posts. The method further includes identifying a plurality of candidate terms within the corpus and selecting, as a trimmed lexicon, a subset of the plurality of candidate terms using predefined criteria. The method further includes clustering at least a subset of the plurality of electronic posts according to a plurality of clusters using the lexicon to produce a plurality of statistical topic models. The method further includes storing information corresponding to the statistical topic models.