A Graphical Model for Topical Impact over Time

After being published, a document, whether it is a research paper or an online post, can make an impact when readers cite, share, or endorse it. A document may not make its greatest impact right after its publication, and some documents' impact can last a long period of time. This study develops a graphical model to capture the temporal dynamics in the impact of latent topics from a corpus of documents. Specifically, we modeled citation counts using Poisson distributions with Gamma priors. We conducted experiments on papers published in (i) D-Lib Magazine and (ii) The Library Quarterly from 2007 to 2017. Comparing with ToT, we found that our model produced more robust results on topical trends over time. The results also showed that prevalence and impact of the same topic are not correlated. Enabling better understanding and modeling of topical impact over time, this model can be used for the design of digital libraries and social media platforms, as well as evaluation of scientific contributions and policies.