With the rising popularity of interdisciplinary work, there is a growing need to understand how publications incorporate ideas from multiple disciplines. Existing computational approaches, such as affiliation diversity, keyword matching, and citation patterns, do not account for how individual citations are used to advance the citing work. Prior studies have proposed taxonomies to classify citation purpose. However, these frameworks are not well-suited to interdisciplinary research and do not provide general measures of engagement depth. To address these limitations, we propose a framework for evaluating citation engagement in scholarly publications. We introduce a citation purpose taxonomy tailored to interdisciplinary work, supported by an annotation study and model for automatically predicting a citation’s purpose. We then leverage signal from a citation’s purpose and the sections in which it appears to predict a single measure of how deeply a publication engages with a citation. We demonstrate the utility of our framework through a large-scale analysis of publications at the intersection of NLP and Computational Social Science.

The main challenge in cross-domain persuasion detection lies in the vast differences in vocabulary observed across different outlets and contexts. Superficially, an argument made on social media will look nothing like an opinion presented in the Supreme Court, but the latent factors that make an argument persuasive are common across all settings. Regardless of domain, persuasive arguments tend to use sound reasoning and present solid evidence, build on the credibility and authority of the source, or appeal to the emotions and beliefs of the audience. In this paper, we show that simply encoding the different argumentative components and their semantic types can significantly improve a language model’s ability to detect persuasion across vastly different domains.