Ted Underwood
Impact in
- General Social Sciences top 0.2%
- Computational and Text Analysis Methods
-
- Digital Humanities and Scholarship
Papers in
-
- Digital Humanities and Scholarship 16
- Literature: history, themes, analysis 3
-
- Natural Language Processing Techniques 4
- Topic Modeling 4
- Advanced Text Analysis Techniques 3
- Co-authors
- David Bamman (2 shared papers)Noah A. Smith (1 shared paper)Andrew B. Goldstone (1 shared paper)Sabrina Lee (1 shared paper)James F. English (2 shared papers)J. Stephen Downie (8 shared papers)Stephen Vaisey (1 shared paper)Kevin Kiley (1 shared paper)
- Journals
- Modern Language Quarterly (4 papers)Studies in Romanticism (2 papers)Representations (2 papers)New Literary History (2 papers)PMLA/Publications of the Modern Language Association of America (2 papers)
- Partner nations
- United StatesSouth KoreaGermany
In The Last Decade
Ted Underwood
38 papers receiving 445 citations
Peers
Comparison fields: 5 of 75
- General Social Sciences 126
- Literature and Literary Theory 220
- Artificial Intelligence 234
- Conservation 23
- Communication 31
Countries citing papers authored by Ted Underwood
This map shows the geographic impact of Ted Underwood'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 Ted Underwood with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ted Underwood more than expected).
Fields of papers citing papers by Ted Underwood
This network shows the impact of papers produced by Ted Underwood. 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 Ted Underwood. The network helps show where Ted Underwood may publish in the future.
Co-authors
The 24 scholars most cited alongside Ted Underwood, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 53 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2014 | 116 | |
| 2 | 2014 | 64 | |
| 3 | 2019 | 56 | |
| 4 | 2017 | 49 | |
| 5 | 2014 | 48 | |
| 6 | 2018 | 26 | |
| 7 | 2016 | 21 | |
| 8 | 2013 | 20 | |
| 9 | 2016 | 18 | |
| 10 | 2002 | 14 | |
| 11 | 2016 | 14 | |
| 12 | 2020 | 13 | |
| 13 | 2021 | 12 | |
| 14 | 2022 | 12 | |
| 15 | Understanding Genre in a Collection of a Million Volumes | 2014 | 11 |
| 16 | 2020 | 8 | |
| 17 | 2018 | 8 | |
| 18 | 2014 | 7 | |
| 19 | 2017 | 7 | |
| 20 | 2015 | 7 |
About Ted Underwood
Ted Underwood is a scholar working on Literature and Literary Theory, Artificial Intelligence, Sociology and Political Science, General Social Sciences and Information Systems, having authored 53 papers that have together received 586 indexed citations. Recurring topics across this work include Digital Humanities and Scholarship (16 papers), Computational and Text Analysis Methods (7 papers), Natural Language Processing Techniques (4 papers), Topic Modeling (4 papers), Philosophy, History, and Historiography (3 papers), Literature: history, themes, analysis (3 papers), Advanced Text Analysis Techniques (3 papers) and Social and Cultural Dynamics (3 papers). The work is most often cited by research in General Social Sciences (126 citations), Literature and Literary Theory (220 citations), Artificial Intelligence (234 citations), Conservation (23 citations) and Communication (31 citations). Ted Underwood has collaborated with scholars based in United States, South Korea and Germany. Frequent co-authors include David Bamman, Noah A. Smith, Andrew B. Goldstone, Sabrina Lee, James F. English, J. Stephen Downie, Stephen Vaisey, Kevin Kiley, Yuerong Hu and Boris Capitanu. Their work appears in journals such as Modern Language Quarterly, Studies in Romanticism, Representations, New Literary History and PMLA/Publications of the Modern Language Association of America.
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.