17. A computer-implemented method according to claim 16 , further comprising: accepting designations by a user of a further subset of the non-matching articles in the corpus as topic-broadening positive training examples; and redefining the finite state patterns to match the on-topic positive training examples and the topic-broadening positive training examples and to not match the negative training examples prior to the filtering.
16. A computer-implemented method for providing topic broadening in interactive building of electronically-stored social indexes, comprising: accessing a corpus of articles each comprised of online textual materials; specifying a hierarchically-structured tree of topics; for each of the topics, designating a set of the articles in the corpus as on-topic positive training examples; finding a fine-grained topic model comprising a finite state pattern that matches the on-topic positive training examples, each finite state pattern comprising a pattern evaluable against the articles, wherein the pattern identifies such articles matching the on-topic positive training examples for the corresponding topic; for each of the topics, generating a coarse-grained topic model corresponding to a center of the topic comprising: randomly selecting a set of the articles in the corpus; identifying a set of characteristic words in each of the randomly-selected articles; determining a frequency of occurrence of each of the characteristic words identified in the set of randomly-selected articles; identifying a set of characteristic words in each of the articles in the on-topic positive training examples; determining a frequency of occurrence of each of the characteristic words identified in the articles in the on-topic training examples; and assigning a score to each characteristic word as a ratio of the respective frequencies of occurrence of the characteristic word in the articles in the on-topic training examples and in the set of randomly-selected articles; filtering new articles received into the corpus, comprising: matching the finite state patterns to each new article; identifying a set of characteristic words in each new article; determining a frequency of occurrence of each of the characteristic words identified in the each article; and assigning a similarity score to each characteristic word as a ratio of the respective frequencies of occurrence of the characteristic word in the new article and in the set of randomly-selected articles; and for each of the topics, ordering the new articles comprising: matching the new articles to the finite state pattern of the fine-grained topic model for the topic; for each new article that does not match the fine-grained topic model for the topic, comparing similarity scores for each of the characteristic words identified in the new article to the scores of the corresponding characteristic words in the coarse-grained topic model for the topic; and displaying each of the new articles that was not matched by the topic's fine-grained topic model and which has similarit scores close to the topic's coarse- t rained topic model's characteristic word scores articles as candidate articles for additional positive training examples.