Determine document novelty/similarity with the aid of Latent Dirichlet allocation (LDA) or Named Entities
Short answer: Topic Models and LDA aren't suited at all for novelty detection. I did some experiments with data that show a succession of topics in temporal order, and at the boundaries of the time interval one topic gets really high percentages in the document-topic matrix, i.e., the upcoming new trend (or the outrunning old trend) is not seen by LDA.

Topic Models are also not really suited for "online" processing—you read the whole corpus of documents once and than infer the topics.