Abstract: A method is disclosed wherein a social media feed is provided comprising messages related to communication about topics, the communication about the topics linked in a structure for forming a conversation. The social media feed is clustered to identify messages within the social media feed that are related one to another to form clusters of the messages. Sets of partisans are provided relating to a first cluster from the clusters of the messages. Then, for the first cluster, a ratio of messages transmitted by each of the at least two sets of partisans is determined and the ratio indicates a winner of the first cluster. Based on the winner and a ratio of non-partisan actors who transmitted messages within the first cluster, an effect of the first cluster upon sentiment of a population is measured.

Abstract: A method and system for evaluating likely causal correlations between input data and output results of an artificial intelligence (AI) system identifies a plurality of features within each of a set of input data provided to the AI system. Each of the determined plurality of features is mapped to each input value of a set of input values and to the output result associated with each input value to form a matrix. From the matrix are removed at least some of the plurality of features that are not causal for the AI system determining the output results. Removal of at least some of the plurality of features that are not causal for the AI system determining the output results is repeated. When an end condition occurs, a resulting set of features that are each more likely to be causal than the plurality of features is provided as output causal data.