Modeling Debate within a Scientific Community

Abstract

There is growing interest in automating the detection and tracking of new and significant developments in science and technology, as they emerge within a given community. A significant component of detecting such patterns of emergence is identifying the presence of a debate in the scientific community. This often reflects disagreements or uncertainties over technologies or concepts as they are actively being discussed and developed. In this paper, we present an algorithm for recognizing debate in large document collections. We distinguish three distinct styles of debate over a document collection: (i) silent debate, (ii) active disagreement, and (iii) topical uncertainty. Our algorithm employs a number of indicators found in the metadata and full text of publications and patents to identify the presence of these types of debate in the community. The paper outlines the details of these features and indicators and reports on the results of applying these indicators to data from several fields classified by subject matter experts, which show that system outputs have high agreement with SME's judgments.