Sean Gerrish
- General Social Sciences top 0.02%
- Computational and Text Analysis Methods 3
- Artificial Intelligence top 1%
- Bayesian Methods and Mixture Models 2
- Topic Modeling 2
- Advanced Text Analysis Techniques 2
- Communication top 5%
- Knowledge Management and Sharing 1
- Information Systems top 5%
- Expert finding and Q&A systems 1
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- Electoral Systems and Political Participation 2
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- Judicial and Constitutional Studies 1
- Co-authors
- David M. BleiJonathan ChangChong WangJordan Boyd‐GraberPrem GopalanDavid MimnoMichael J. FreedmanWei Xiao
- Journals
- First Monday (1 paper)International Conference on Machine Learning (2 papers)Neural Information Processing Systems (3 papers)
- Partner nations
- United States
In The Last Decade
Sean Gerrish
7 papers receiving 1.4k citations
Hit Papers
Peers
Comparison fields: 5 of 127
- General Social Sciences 369
- Artificial Intelligence 869
- Communication 129
- Statistical and Nonlinear Physics 202
- Information Systems 234
Countries citing papers authored by Sean Gerrish
This map shows the geographic impact of Sean Gerrish'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 Sean Gerrish with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sean Gerrish more than expected).
Fields of papers citing papers by Sean Gerrish
This network shows the impact of papers produced by Sean Gerrish. 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 Sean Gerrish. The network helps show where Sean Gerrish may publish in the future.
Co-authorship network
The 12 scholars most cited alongside Sean Gerrish, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Applications of latent variable models in modeling influence and decision making | 2013 | 2 |
| 2 | How They Vote: Issue-Adjusted Models of Legislative Behavior | 2012 | 58 |
| 3 | Scalable Inference of Overlapping Communities | 2012 | 50 |
| 4 | Predicting Legislative Roll Calls from Text | 2011 | 102 |
| 5 | A Language-based Approach to Measuring Scholarly Impact | 2010 | 89 |
| 6 | 2010 | 15 | |
| 7 | Reading Tea Leaves: How Humans Interpret Topic Modelsbreakdown → | 2009 | 1239 |
About Sean Gerrish
Sean Gerrish is a scholar working on General Social Sciences, Artificial Intelligence, Communication, Law and Political Science and International Relations, having authored 7 papers that have together received 1.6k indexed citations. Recurring topics across this work include Computational and Text Analysis Methods (3 papers), Electoral Systems and Political Participation (2 papers), Bayesian Methods and Mixture Models (2 papers), Topic Modeling (2 papers), Advanced Text Analysis Techniques (2 papers), Knowledge Management and Sharing (1 paper), Expert finding and Q&A systems (1 paper) and Judicial and Constitutional Studies (1 paper). The work is most often cited by research in General Social Sciences (369 citations), Artificial Intelligence (869 citations), Communication (129 citations), Statistical and Nonlinear Physics (202 citations) and Information Systems (234 citations). Sean Gerrish has collaborated with scholars based in United States. Frequent co-authors include David M. Blei, Jonathan Chang, Chong Wang, Jordan Boyd‐Graber, Prem Gopalan, David Mimno, Michael J. Freedman, Wei Xiao, Lada A. Adamic and Gavin Clarkson. Their work appears in journals such as First Monday, International Conference on Machine Learning and Neural Information Processing Systems.
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.