1. An information spread scale prediction device, including at least one computer device, which acquires text data from a specific website via the Internet, predicts number of future contributions made to the website based on the text data, and outputs a prediction result thereof, the information spread scale prediction device comprising: a learning text data input unit including a processor which acquires the text data from the specific website as learning text data; a group generating unit including a processor which classifies nodes into groups based on information regarding attributes of each of the nodes indicating a specific user; a node influence learning unit including a processor which classifies the learning text data by each topic, calculates influence for the number of contributions by each of the groups to which the node belongs for the topic from the number of contributions of each of the classified topics, and stores a result thereof as learning data to a storage module; a prediction text data input unit including a processor which acquires the text data from the specific website as prediction data after storing the learning data; a group/time information tabulation unit including a processor which performs cross tabulation regarding the number of contributions from the learning text data and the prediction text data by each time and each of the groups, and outputs a result thereof to the node influence learning unit; and a future contribution number prediction unit including a processor which classifies the prediction text data by each topic, predicts the number of contributions of the topics at a specific future time based on the number of contributions by each of the classified topics, the learning data, and the number of contributions cross-tabulated, and outputs a result thereof to an output module provided in advance, wherein: provided that the number of contributions cross-tabulated by each time and each of the groups is a matrix X, the number of contributions of a group j at time i of the matrix X is an element xij, a submatrix extracted from a first row of the matrix X to an s-th row is Xs, a total sum for all the nodes of the number of contributions at each time is ys, and a value of a density function at x in Poisson distribution of average αis P0(x, α), the node influence learning unit acquires influence βs of the group at time s as a value when minimizing a numerical value of f(ys, Xs, βs) shown as 
 ƒ( y s , X s ,β s ) +λP (β s ), s= 1,. .. , S.