Piotr Przybyła
- Health Informatics top 5%
- Artificial Intelligence top 5%
- Topic Modeling 16
- Natural Language Processing Techniques 5
- Text Readability and Simplification 4
- Machine Learning in Healthcare 3
- Authorship Attribution and Profiling 3
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- Scientific Computing and Data Management 5
- Information Systems top 5%
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- Biomedical Text Mining and Ontologies 9
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- Misinformation and Its Impacts 6
- Co-authors
- Sophia AnaniadouAustin J. BrockmeierAxel J. SotoJohn McNaughtGeorgios KontonatsiosAndrew P. RiceJing LiaoAlexandra Bannach‐Brown
- Journals
- Bioinformatics (1 paper)PLoS ONE (1 paper)Journal of the American Medical Informatics Association (1 paper)
- Partner nations
- PolandUnited KingdomCzechia
In The Last Decade
Piotr Przybyła
23 papers receiving 541 citations
Peers
Comparison fields: 5 of 118
- Health Informatics 44
- Statistics, Probability and Uncertainty 105
- Artificial Intelligence 249
- Information Systems and Management 36
- Information Systems 115
Countries citing papers authored by Piotr Przybyła
This map shows the geographic impact of Piotr Przybyła'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 Piotr Przybyła with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Piotr Przybyła more than expected).
Fields of papers citing papers by Piotr Przybyła
This network shows the impact of papers produced by Piotr Przybyła. 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 Piotr Przybyła. The network helps show where Piotr Przybyła may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Piotr Przybyła, 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 | 2025 | 0 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 2 | |
| 4 | 2024 | 3 | |
| 5 | 2022 | 3 | |
| 6 | 2022 | 3 | |
| 7 | 2021 | 6 | |
| 8 | The Role of Text Simplification Operations in Evaluation. | 2021 | 3 |
| 9 | 2020 | 92 | |
| 10 | Detecting Bot Accounts on Twitter by Measuring Message Predictability. | 2019 | 1 |
| 11 | The IPIPAN Team Participation in the Check-Worthiness Task of the CLEF2019 CheckThat! Lab. | 2019 | 1 |
| 12 | 2019 | 37 | |
| 13 | 2019 | 109 | |
| 14 | 2019 | 1 | |
| 15 | 2018 | 90 | |
| 16 | 2017 | 33 | |
| 17 | 2016 | 38 | |
| 18 | What Do Your Look-alikes Say about You? Exploiting Strong and Weak Similarities for Author Profiling. | 2015 | 3 |
| 19 | Question Analysis for Polish Question Answering | 2013 | 0 |
| 20 | 2011 | 27 |
About Piotr Przybyła
Piotr Przybyła is a scholar working on Information Systems and Management, Artificial Intelligence and Management Science and Operations Research, having authored 28 papers that have together received 554 indexed citations. Recurring topics across this work include Topic Modeling (16 papers), Biomedical Text Mining and Ontologies (9 papers), Misinformation and Its Impacts (6 papers), Natural Language Processing Techniques (5 papers), Scientific Computing and Data Management (5 papers), Text Readability and Simplification (4 papers), Machine Learning in Healthcare (3 papers) and Authorship Attribution and Profiling (3 papers). The work is most often cited by research in Health Informatics (44 citations), Statistics, Probability and Uncertainty (105 citations) and Artificial Intelligence (249 citations). Piotr Przybyła has collaborated with scholars based in Poland, United Kingdom and Czechia. Frequent co-authors include Sophia Ananiadou, Austin J. Brockmeier, Axel J. Soto, John McNaught, Georgios Kontonatsios, Andrew P. Rice, Jing Liao, Alexandra Bannach‐Brown, Malcolm Macleod and James Thomas. Their work appears in journals such as Bioinformatics, PLoS ONE and Journal of the American Medical Informatics Association.
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.