Modeling scholarly communications across heterogeneous corpora

Scholarly communication plays a pivotal role in science. Recently, the process of scholarly communication has been fundamentally changed by the emergence of digital scholarship and social media. These changes have great potential for the democratization of science and scholarship yet challenge existing scientific norms and processes that rely on careful and deliberate review of scholarly value.;In this dissertation we introduce novel methods to study scholarly communication from large-scale, heterogeneous data sets that are generated as a product of digital scholarship, making fundamental contributions in modeling scholarly communities, the flow and exchange of scientific knowledge in scholarly networks, and the role of social media in shaping scholarly impact and communication. We outline our research in four sections of this dissertation. First, we investigate the emergence of scholarly communities by probabilistically modeling research topics from large-scale bibliometric data to provide a dynamic perspective on the evolution of topic- and author-based communities. This work addresses the difficult question of ranking authors and publication venues according to their impact within the context of a community, while accounting for the dynamics of topic changes. Second, we investigate how scientific knowledge propagates through scholarly networks. By drawing an analogy between international trade and scientific communication, we measure the flow of ideas between scientific domains, i.e. intellectual trade, on the basis of the predominant direction of journal citations. Third, we analyze the online response of the scientific community to the publication of scholarly articles using pre-print downloads, Twitter mentions, and early citations data, using a single cohort of arXiv preprints. We find that Twitter mentions for the selected cohort ramp up within days of article submission and most intriguingly that they are correlated with later article downloads and early citations, indicating that social media attention may shape scientific impact. Fourth, we compare the impact of papers, scholars, and topics as measured by different measures, each derived from a different medium for the exchange of scientific knowledge, in this case scholarly citations vs. Wikipedia mentions. The results show that citation impact and Wikipedia impact are positively correlated.