1. A computer-implemented method of detecting new events comprising the steps of: determining at least one story characteristic based on an average story similarity story characteristic and a same event-same source story characteristic; determining a source-identified story corpus, each story associated with at least one event; determining a source-identified new story associated with at least one event; determining story-pairs based on the source-identified new-story and each story in the source-identified story corpus; determining at least one inter-story similarity metric for the story-pairs; wherein the inter-story similarity metrics are comprised of at least one story frequency model and at least one story characteristic frequency model combined using terms weights; and wherein an event frequency is determined based on term t and rule of interpretation (ROI) category rmax from the formula: ef r ⁢ ⁢ max ⁡ ( t ) = max r ∈ R ⁢ ( ef ⁡ ( r , t ) ) wherein r is an ROI category, R is the set of all possible ROIs, ef(rt) is the frequency of term t in ROI category r: determining at least one adjustment to the inter-story similarity metrics based on at least one story characteristic; and outputting a new story event indicator if the event associated with the new story is of a determined similarity or dissimilarity to the events associated with the source-identified story corpus based on the inter-similarity metrics and adjustments.
11. The method of claim 1 further comprising the step of determining a subset of stories from the source-identified story corpus and the source-identified new story based on at least one story characteristic.