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\section{Definitions}  A few definitions relating the application for Twitter domain are considered.  The graph vertices are labeled by a string representing the user id for example: "@John". The topology of the Twitter graph varies in time. In consequence the Twitter Graph is formalized by G(n) = ( V(n), E(n) ) where n denotes the graph at the discrete time sample n. A( n ) is the adjancency matrix and k( n ) is the degree vector.  Twitter NER:  Users @  RT  OpenNLP:  people,  places,  dates  $ stock market  # hash  http  youtube  Freebase  news  trends  Twitter2011 corpus  co-occurence matrix on spritzer or trends or search phrase or schema.org  tf-idf for concepts with cascading on accumulo  feedback loop on keywords for streaming api  semantic fields  cep  text patterns between entities  search entities  cascading in M3Data (Lingual) + ML (PMML)