Yuning Ding
Impact in
- Modeling and Simulation top 10%
- COVID-19 epidemiological studies
Papers in
-
- Topic Modeling 9
- Natural Language Processing Techniques 7
- Text Readability and Simplification 4
- Adversarial Robustness in Machine Learning 1
-
- Software Engineering Research 3
- Co-authors
- Juan M. Banda (1 shared paper)Jingyuan Yu (1 shared paper)Tuo Liu (1 shared paper)Ekaterina Artemova (1 shared paper)Gerardo Chowell (1 shared paper)Elena Tutubalina (1 shared paper)Ramya Tekumalla (1 shared paper)Andrea Horbach (10 shared papers)
- Journals
- International Journal of Artificial Intelligence in Education (1 paper)DuEPublico (University of Duisburg-Essen) (1 paper)Universitätsbibliographie, Universität Duisburg-Essen (2 papers)Epidemiologia (1 paper)
- Partner nations
- GermanyUnited StatesRussia
In The Last Decade
Yuning Ding
10 papers receiving 211 citations
Yuning Ding's Hit Papers
Peers
Comparison fields: 5 of 51
- Modeling and Simulation 21
- Health Informatics 5
- Artificial Intelligence 121
- Health 20
- Communication 17
Countries citing papers authored by Yuning Ding
This map shows the geographic impact of Yuning Ding'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 Yuning Ding with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Yuning Ding more than expected).
Fields of papers citing papers by Yuning Ding
This network shows the impact of papers produced by Yuning Ding. 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 Yuning Ding. The network helps show where Yuning Ding may publish in the future.
Co-authors
The 12 scholars most cited alongside Yuning Ding, 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 | A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration Hit paper breakdown → | 2021 | 170 |
| 2 | 2020 | 14 | |
| 3 | 2017 | 11 | |
| 4 | 2017 | 7 | |
| 5 | 2023 | 6 | |
| 6 | 2022 | 4 | |
| 7 | 2023 | 3 | |
| 8 | 2020 | 3 | |
| 9 | 2025 | 1 | |
| 10 | 2023 | 1 | |
| 11 | 2024 | 0 |
About Yuning Ding
Yuning Ding is a scholar working on Artificial Intelligence, Information Systems, Social Psychology, Sociology and Political Science and Epidemiology, having authored 11 papers that have together received 220 indexed citations. Recurring topics across this work include Topic Modeling (9 papers), Natural Language Processing Techniques (7 papers), Text Readability and Simplification (4 papers), Software Engineering Research (3 papers), Mental Health via Writing (1 paper), Data-Driven Disease Surveillance (1 paper), Adversarial Robustness in Machine Learning (1 paper) and Misinformation and Its Impacts (1 paper). The work is most often cited by research in Modeling and Simulation (21 citations), Health Informatics (5 citations), Artificial Intelligence (121 citations), Health (20 citations) and Communication (17 citations). Yuning Ding has collaborated with scholars based in Germany, United States and Russia. Frequent co-authors include Juan M. Banda, Jingyuan Yu, Tuo Liu, Ekaterina Artemova, Gerardo Chowell, Elena Tutubalina, Ramya Tekumalla, Andrea Horbach, Torsten Zesch and Aoife Cahill. Their work appears in journals such as International Journal of Artificial Intelligence in Education, DuEPublico (University of Duisburg-Essen), Universitätsbibliographie, Universität Duisburg-Essen and Epidemiologia.
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.