10. A method for distributing messages to individuals in a communication system, comprising a. accumulating messages from individuals in at least one hub comprising a processor; b. processing with the processor and reorganizing accumulated messages and groups of accumulated messages with similar contents into short representations without or with insignificant information loss for said single messages or said groups; and c. distributing, by the processor, messages containing short representations in place of accumulates messages to individuals according to one or more distribution lists, each distribution list specifying which messages are to be delivered to each individual, wherein the one or more distribution lists are chosen by finding a maximum of a goodness function that depends on the distribution lists and the parameters describing the individuals and the messages, wherein the goodness function is defined as a linear function, ƒ(d)=Σ X,DεG(X) PI X (D), subject to a linear constraint of the form Σ X,DεG(X) q X (D)≦r, where X identifies different individuals in the group, G(X) identifies the list of messages to be sent to individual X, D identifies one of the messages in the system, PI x (D) q X (D) are real valued coefficients depending on the features of the message D and individual X, r is an acceptability constraint and the sum f(d) is over all individuals X and messages D in G(X) jointly and wherein the coefficient PI X (D) uses a probabilistic model of individuals' responses to messages, PI X (D)=1−Π YεA(D) Π c (1−RC D (c)VI X Y (c)), where PI X (D) is identified with the probability that individual X will respond positively, in a certain sense, to message D, and where Y identifies different individuals in the group, c identifies different categories to which a message can be related, A(D) identifies a list of authors of message D, RC D (c) for all c identifies a set of features, valued between 0 and 1, describing the message D in relation to the set of categories c, and VI X Y (c) for all Y and c is a set of features, valued between 0 and 1, describing the individual X in relation to the set of categories c and message authors Y.
10. A method for distributing messages to individuals in a communication system, comprising a. accumulating messages from individuals in at least one hub comprising a processor; b. processing with the processor and reorganizing accumulated messages and groups of accumulated messages with similar contents into short representations without or with insignificant information loss for said single messages or said groups; and c. distributing, by the processor, messages containing short representations in place of accumulates messages to individuals according to one or more distribution lists, each distribution list specifying which messages are to be delivered to each individual, wherein the one or more distribution lists are chosen by finding a maximum of a goodness function that depends on the distribution lists and the parameters describing the individuals and the messages, wherein the goodness function is defined as a linear function, ƒ(d)=Σ X,DεG(X) PI X (D), subject to a linear constraint of the form Σ X,DεG(X) q X (D)≦r, where X identifies different individuals in the group, G(X) identifies the list of messages to be sent to individual X, D identifies one of the messages in the system, PI x (D) q X (D) are real valued coefficients depending on the features of the message D and individual X, r is an acceptability constraint and the sum f(d) is over all individuals X and messages D in G(X) jointly and wherein the coefficient PI X (D) uses a probabilistic model of individuals' responses to messages, PI X (D)=1−Π YεA(D) Π c (1−RC D (c)VI X Y (c)), where PI X (D) is identified with the probability that individual X will respond positively, in a certain sense, to message D, and where Y identifies different individuals in the group, c identifies different categories to which a message can be related, A(D) identifies a list of authors of message D, RC D (c) for all c identifies a set of features, valued between 0 and 1, describing the message D in relation to the set of categories c, and VI X Y (c) for all Y and c is a set of features, valued between 0 and 1, describing the individual X in relation to the set of categories c and message authors Y. 12. The method of claim 10 , comprising generating a probabilistic model of individuals' responses to distributed messages, that depends both on features describing the individuals and the messages, wherein this model is used to determine the goodness function.