Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Countries citing papers authored by Michael J. Paul
Since
Specialization
Citations
This map shows the geographic impact of Michael J. Paul's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Michael J. Paul with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Michael J. Paul more than expected).
This network shows the impact of papers produced by Michael J. Paul. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Michael J. Paul. The network helps show where Michael J. Paul may publish in the future.
Co-authorship network of co-authors of Michael J. Paul
This figure shows the co-authorship network connecting the top 25 collaborators of Michael J. Paul.
A scholar is included among the top collaborators of Michael J. Paul based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Michael J. Paul. Michael J. Paul is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Paul, Michael J., et al.. (2018). Learning Multilingual Topics from Incomparable Corpora. International Conference on Computational Linguistics. 2595–2609.6 indexed citations
Huang, Xiaolei, Michael C. Smith, Michael J. Paul, et al.. (2017). Examining Patterns of Influenza Vaccination in Social Media.. National Conference on Artificial Intelligence.31 indexed citations
Nakhasi, Atul, et al.. (2012). Twitter as a Source for Learning about Patient Safety Events.. AMIA.1 indexed citations
13.
Paul, Michael J.. (2012). Mixed Membership Markov Models for Unsupervised Conversation Modeling. Empirical Methods in Natural Language Processing. 94–104.21 indexed citations
14.
Paul, Michael J. & Mark Dredze. (2012). Factorial LDA: Sparse Multi-Dimensional Text Models. Neural Information Processing Systems. 25. 2582–2590.26 indexed citations
15.
Paul, Michael J. & Jason Eisner. (2012). Implicitly Intersecting Weighted Automata using Dual Decomposition. North American Chapter of the Association for Computational Linguistics. 232–242.7 indexed citations
16.
Paul, Michael J. & Roxana Gîrju. (2010). Comparative scientific research analysis with a language-independent cross-collection model. Procesamiento del lenguaje natural. 45. 153–160.1 indexed citations
17.
Paul, Michael J., ChengXiang Zhai, & Roxana Gîrju. (2010). Summarizing Contrastive Viewpoints in Opinionated Text. Empirical Methods in Natural Language Processing. 66–76.91 indexed citations
Richards, James R., Ilona Rodan, Thomas Elston, et al.. (2001). Feline Vaccine Selection and Administration. Compendium on Continuing Education for The Practicing Veterinarian. 23(1). 71–80.9 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.