1. A method for visually identifying spam in a text-based dataset, comprising the steps of: calculating, via a lexicon engine of a data processing module, a correlation K ij between combinations of words, wherein K ij is a coefficient between a correlation parameter C ij of lemmas i with lemmas j, over F i F j , where F i is frequency of word i and F j is frequency of word j; concatenating lemmas that have a correlation K ij above a predetermined level into an artificial word via the lexicon engine; recalculating, via the lexicon engine, a correlation K ij between combinations of words in an expanded dataset comprised of said text-based dataset and artificial words obtained by concatenation; repeating the steps above for N lemmas; and using a force directed graph to visually display, via a display in communication with the data processing module, the results obtained above into separate conversations, wherein each node in the graph has N outgoing partners, and the N co-words with the highest value of K ij above a minimum value are connected together.