David Wadden
Impact in
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- Digital Mental Health Interventions
Papers in
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- Topic Modeling 7
- Natural Language Processing Techniques 3
- Advanced Text Analysis Techniques 2
- Intelligent Tutoring Systems and Adaptive Learning 1
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- Biomedical Text Mining and Ontologies 4
- Co-authors
- David L. Lahr (2 shared papers)Rajiv Narayan (2 shared papers)Aravind Subramanian (2 shared papers)Itay Tirosh (1 shared paper)David E. Root (1 shared paper)Ted Natoli (1 shared paper)Ian C. P. Smith (1 shared paper)John G. Doench (1 shared paper)
- Journals
- Bioinformatics (1 paper)PLoS Biology (1 paper)Proceedings of the AAAI Symposium Series (1 paper)Proceedings of the International AAAI Conference on Web and Social Media (1 paper)Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (1 paper)
- Partner nations
- United StatesNew ZealandIsrael
In The Last Decade
David Wadden
12 papers receiving 316 citations
Peers
Comparison fields: 5 of 77
- Applied Psychology 22
- Health Informatics 6
- Aging 6
- Artificial Intelligence 95
- Molecular Biology 150
Countries citing papers authored by David Wadden
This map shows the geographic impact of David Wadden'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 David Wadden with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Wadden more than expected).
Fields of papers citing papers by David Wadden
This network shows the impact of papers produced by David Wadden. 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 David Wadden. The network helps show where David Wadden may publish in the future.
Co-authors
The 25 scholars most cited alongside David Wadden, 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 | 2017 | 130 | |
| 2 | 2018 | 36 | |
| 3 | 2021 | 29 | |
| 4 | 2022 | 27 | |
| 5 | 2022 | 25 | |
| 6 | 2023 | 24 | |
| 7 | 2022 | 17 | |
| 8 | 2021 | 16 | |
| 9 | 2024 | 8 | |
| 10 | 2024 | 3 | |
| 11 | 2020 | 1 | |
| 12 | 2024 | 1 | |
| 13 | 2025 | 0 | |
| 14 | 2024 | 0 |
About David Wadden
David Wadden is a scholar working on Artificial Intelligence, Molecular Biology, Sociology and Political Science, Communication and Social Psychology, having authored 14 papers that have together received 317 indexed citations. Recurring topics across this work include Topic Modeling (7 papers), Biomedical Text Mining and Ontologies (4 papers), Natural Language Processing Techniques (3 papers), Advanced Text Analysis Techniques (2 papers), Impact of Technology on Adolescents (2 papers), Social Media and Politics (2 papers), Intelligent Tutoring Systems and Adaptive Learning (1 paper) and Mental Health via Writing (1 paper). The work is most often cited by research in Applied Psychology (22 citations), Health Informatics (6 citations), Aging (6 citations), Artificial Intelligence (95 citations) and Molecular Biology (150 citations). David Wadden has collaborated with scholars based in United States, New Zealand and Israel. Frequent co-authors include David L. Lahr, Rajiv Narayan, Aravind Subramanian, Itay Tirosh, David E. Root, Ted Natoli, Ian C. P. Smith, John G. Doench, Peyton Greenside and Todd R. Golub. Their work appears in journals such as Bioinformatics, PLoS Biology, Proceedings of the AAAI Symposium Series, Proceedings of the International AAAI Conference on Web and Social Media and Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).
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.