10. A computer implemented method for identifying propaganda, wherein a computer has a source dataset stored thereon, comprising: the computer tokenizing each document in the source dataset; the computer generating a term-by-document matrix; the computer applying weights to the term-by-document matrix to generate a weighted term-by-document matrix; the computer performing a matrix multiplication of a translation matrix and the weighted term-by-document matrix to generate a multilingualized weighted matrix; the computer factorizing the multilingualized weighted matrix to generate an ordered list of topics and a topic-by-document block matrix; and the computer calculating a contribution per user-specified grouping within the source dataset to each topic within the ordered list of topics; wherein the tokenization, generating of a matrix, applying weights, matrix multiplication, factorizing, calculating, and generating of output are performed regardless of the number, type, or size of documents, regardless of the number or type of terms associated with each document, and regardless of the provenance of the dataset.
10. A computer implemented method for identifying propaganda, wherein a computer has a source dataset stored thereon, comprising: the computer tokenizing each document in the source dataset; the computer generating a term-by-document matrix; the computer applying weights to the term-by-document matrix to generate a weighted term-by-document matrix; the computer performing a matrix multiplication of a translation matrix and the weighted term-by-document matrix to generate a multilingualized weighted matrix; the computer factorizing the multilingualized weighted matrix to generate an ordered list of topics and a topic-by-document block matrix; and the computer calculating a contribution per user-specified grouping within the source dataset to each topic within the ordered list of topics; wherein the tokenization, generating of a matrix, applying weights, matrix multiplication, factorizing, calculating, and generating of output are performed regardless of the number, type, or size of documents, regardless of the number or type of terms associated with each document, and regardless of the provenance of the dataset. 17. A computer implemented method of claim 10 , wherein factorizing the weighted term-by-document matrix comprises: performing a truncated or full singular value decomposition on the weighted term-by-document matrix; and generating a singular values matrix as an output of the singular value decomposition; generating a term-by-topic matrix as an output of the singular value decomposition; and generating a topic-by-document block matrix, in which each block corresponds to one of the user-specified groupings, as an output of the singular value decomposition.