Bikun Chen
Impact in
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- scientometrics and bibliometrics research
- Meta-analysis and systematic reviews
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- Academic Publishing and Open Access
Papers in
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- scientometrics and bibliometrics research 4
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- Advanced Text Analysis Techniques 2
- Topic Modeling 2
- Sentiment Analysis and Opinion Mining 1
- Text and Document Classification Technologies 1
- Co-authors
- Fei Shu (3 shared papers)Vincent Larivière (1 shared paper)Cassidy R. Sugimoto (1 shared paper)Junping Qiu (1 shared paper)Chengzhi Zhang (2 shared papers)Star X. Zhao (1 shared paper)Sijia Wang (1 shared paper)
- Journals
- Scientometrics (3 papers)Aslib Journal of Information Management (1 paper)Papyrus : Institutional Repository (Université de Montréal) (1 paper)ISSI (1 paper)
- Partner nations
- ChinaCanadaUnited States
In The Last Decade
Bikun Chen
7 papers receiving 369 citations
Peers
Comparison fields: 5 of 79
- Statistics, Probability and Uncertainty 201
- Information Systems and Management 82
- Health Informatics 14
- Safety Research 49
- History and Philosophy of Science 29
Countries citing papers authored by Bikun Chen
This map shows the geographic impact of Bikun Chen'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 Bikun Chen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Bikun Chen more than expected).
Fields of papers citing papers by Bikun Chen
This network shows the impact of papers produced by Bikun Chen. 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 Bikun Chen. The network helps show where Bikun Chen may publish in the future.
Co-authors
The 7 scholars most cited alongside Bikun Chen, 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 | 182 | |
| 2 | 2017 | 113 | |
| 3 | 2020 | 53 | |
| 4 | 2016 | 24 | |
| 5 | 2020 | 13 | |
| 6 | 2024 | 2 | |
| 7 | Usage Pattern Analysis of Academic Articles from Two Chinese Journals. | 2017 | 1 |
About Bikun Chen
Bikun Chen is a scholar working on Statistics, Probability and Uncertainty, Artificial Intelligence, Information Systems, Sociology and Political Science and Information Systems and Management, having authored 7 papers that have together received 388 indexed citations. Recurring topics across this work include scientometrics and bibliometrics research (4 papers), Advanced Text Analysis Techniques (2 papers), Topic Modeling (2 papers), Research Data Management Practices (1 paper), Sentiment Analysis and Opinion Mining (1 paper), Academic Publishing and Open Access (1 paper), Digital Marketing and Social Media (1 paper) and Text and Document Classification Technologies (1 paper). The work is most often cited by research in Statistics, Probability and Uncertainty (201 citations), Information Systems and Management (82 citations), Health Informatics (14 citations), Safety Research (49 citations) and History and Philosophy of Science (29 citations). Bikun Chen has collaborated with scholars based in China, Canada and United States. Frequent co-authors include Fei Shu, Vincent Larivière, Cassidy R. Sugimoto, Junping Qiu, Chengzhi Zhang, Star X. Zhao and Sijia Wang. Their work appears in journals such as Scientometrics, Aslib Journal of Information Management, Papyrus : Institutional Repository (Université de Montréal) and ISSI.
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.