Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora

Joint Modeling of Topics, Citations, and Topical Authority in Academic Corpora Jooyeon Kim KAIST jooyeon.kim@kaist.ac.kr Dongwoo Kim Australian National University dongwoo.kim@anu.edu.au Alice Oh KAIST alice.oh@kaist.edu Abstract Much of scientific progress stems from pre- viously published findings, but searching through the vast sea of scientific publications is difficult. We often rely on metrics of schol- arly authority to find the prominent authors but these authority indices do not differentiate au- thority based on research topics. We present Latent Topical-Authority Indexing (LTAI) for jointly modeling the topics, citations, and top- ical authority in a corpus of academic papers. Compared to previous models, LTAI differs in two main aspects. First, it explicitly models the generative process of the citations, rather than treating the citations as given. Second, it models each author’s influence on citations of a paper based on the topics of the cited pa- pers, as well as the citing papers. We fit LTAI into four academic corpora: CORA, Arxiv Physics, PNAS, and Citeseer. We compare the performance of LTAI against various base- lines, starting with the latent Dirichlet alloca- tion, to the more advanced models including author-link topic model and dynamic author citation topic model. The results show that LTAI achieves improved accuracy over other similar models when predicting words, cita- tions and authors of publications. 1 Introduction With a corpus of scientific literature, we can ob- serve the complex and intricate process of scientific progress. We can learn the major topics in journal articles and conference proceedings, follow authors who are prolific and influential, and find papers that are highly cited. The huge number of publications Topic Reinforcement Learning Image Processing Statistical Learning Information Retrieval Authority 2.76 0.14 10.29 0.10 highly cited M. Jordan Topic Reinforcement Learning Image Processing Statistical Learning Information Retrieval Authority 12.86 0.37 4.83 0.04 highly cited R. Sutton R. Sutton M. Jordan Citation Network Inferred Authority Figure 1: Overview of Latent Topical Authority In- dexing (LTAI). Based on content, citation, and au- thorship information (top), the LTAI discovers the topical authority of authors; it increases when a pa- per with certain topics gets cited (bottom). Topical authority examples are the results of the LTAI with CORA dataset and 100 topics. and authors, however, makes it practically impossi- ble to attain any deep or detailed understanding be- yond the very broad trends. For example, if we want to identify authors who are particularly influential in a specific research field, it is difficult to do so with- out the aid of automatic analysis. Online publication archives, such as Google Scholar, provide near real-time metrics of schol- 191 Transactions of the Association for Computational Linguistics, vol. 5, pp. 191–204, 2017. Action Editor: Noah Smith. Submission batch: 11/2016; Revision batch: 2/2017; Published 7/2017. c⃝2017 Association for Computational Linguistics. Distributed under a CC-BY 4.0 license. arly impact, such as the h-index (Hirsch, 2005), the journal impact factor (Garfield, 2006), and citation count. Those indices, however, are still at a coarse level of granularity. For example, both Michael Jor- dan and Richard …

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