Dan Lahav
Impact in
- Health Informatics top 10%
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Sentiment Analysis and Opinion Mining
- Multi-Agent Systems and Negotiation
- Advanced Text Analysis Techniques
Papers in
-
- Topic Modeling 6
- Natural Language Processing Techniques 3
- Hate Speech and Cyberbullying Detection 2
- Domain Adaptation and Few-Shot Learning 1
-
- Biomedical Text Mining and Ontologies 2
- Co-authors
- Noam Slonim (6 shared papers)Assaf Toledo (5 shared papers)Shai Gretz (5 shared papers)Edo Cohen-Karlik (2 shared papers)Ranit Aharonov (2 shared papers)Noam Shomron (2 shared papers)Orli Kehat (1 shared paper)Yazeed Zoabi (1 shared paper)
- Journals
- Journal of Medical Internet Research (1 paper)The Veterinary Journal (1 paper)Scientific Reports (1 paper)Vector-Borne and Zoonotic Diseases (1 paper)JMIR Human Factors (1 paper)
- Partner nations
- IsraelUnited StatesUnited Kingdom
In The Last Decade
Dan Lahav
12 papers receiving 227 citations
Peers
Comparison fields: 5 of 80
- Health Informatics 13
- Artificial Intelligence 131
- Information Systems 54
- Applied Microbiology and Biotechnology 3
- Infectious Diseases 24
Countries citing papers authored by Dan Lahav
This map shows the geographic impact of Dan Lahav'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 Dan Lahav with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dan Lahav more than expected).
Fields of papers citing papers by Dan Lahav
This network shows the impact of papers produced by Dan Lahav. 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 Dan Lahav. The network helps show where Dan Lahav may publish in the future.
Co-authors
The 25 scholars most cited alongside Dan Lahav, 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 | 2020 | 49 | |
| 2 | 2019 | 34 | |
| 3 | 2006 | 25 | |
| 4 | 2021 | 25 | |
| 5 | 2022 | 21 | |
| 6 | 2007 | 20 | |
| 7 | 2022 | 18 | |
| 8 | 2022 | 17 | |
| 9 | 2019 | 14 | |
| 10 | 2020 | 14 | |
| 11 | 2021 | 5 | |
| 12 | 2023 | 1 | |
| 13 | 2023 | 0 |
About Dan Lahav
Dan Lahav is a scholar working on Artificial Intelligence, Molecular Biology, Information Systems, Infectious Diseases and Surgery, having authored 13 papers that have together received 243 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Natural Language Processing Techniques (3 papers), Hate Speech and Cyberbullying Detection (2 papers), Biomedical Text Mining and Ontologies (2 papers), Software Engineering Research (2 papers), Sarcoma Diagnosis and Treatment (1 paper), Domain Adaptation and Few-Shot Learning (1 paper) and Mosquito-borne diseases and control (1 paper). The work is most often cited by research in Health Informatics (13 citations), Artificial Intelligence (131 citations), Information Systems (54 citations), Applied Microbiology and Biotechnology (3 citations) and Infectious Diseases (24 citations). Dan Lahav has collaborated with scholars based in Israel, United States and United Kingdom. Frequent co-authors include Noam Slonim, Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Ranit Aharonov, Noam Shomron, Orli Kehat, Yazeed Zoabi, Amos Adler and Elad Venezian. Their work appears in journals such as Journal of Medical Internet Research, The Veterinary Journal, Scientific Reports, Vector-Borne and Zoonotic Diseases and JMIR Human Factors.
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.