The confirmation of scientific theories using Bayesian causal networks and citation sentiments

Abstract The confirmation of scientific theories is approached by combining Bayesian probabilistic methods, in particular Bayesian causal networks, and the analysis of citing sentences for highly cited papers. It is assumed that causes and their effects can be identified by linguistic methods from the citing sentences and that the cause-and-effect pairs can be equated with theories and their evidence. Further, it is proposed that citation context sentiments for “evidence” and “uncertainty” can be used to supply the required conditional probabilities for Bayesian analysis where data is drawn from citing sentences for highly cited papers from various fields. Hence, the approach combines citation and linguistic methods in a probabilistic framework and, given the small sample of papers, should be considered a feasibility study. Special attention is given to the case of nociception in medicine, and analogies are drawn with various episodes from the history of science, such as the Watson and Crick discovery of the structure of DNA and other discoveries where a striking and improbable fit between theory and evidence leads to a sense of confirmation.