Natali Ruchansky
Impact in
- Information Systems top 5%
- Spam and Phishing Detection
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- Misinformation and Its Impacts
Papers in
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- Internet Traffic Analysis and Secure E-voting 2
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- Spam and Phishing Detection 2
- Co-authors
- Yan Liu (2 shared papers)He Jiang (1 shared paper)Karishma Sharma (1 shared paper)Feng Qian (1 shared paper)Ming Zhang (1 shared paper)Davide Proserpio (2 shared papers)Sungyong Seo (1 shared paper)Evimaria Terzi (3 shared papers)
- Journals
- ACM SIGCOMM Computer Communication Review (2 papers)ACM Transactions on Intelligent Systems and Technology (1 paper)Lecture notes in computer science (1 paper)arXiv (Cornell University) (1 paper)
- Partner nations
- United StatesItalyChina
In The Last Decade
Natali Ruchansky
9 papers receiving 371 citations
Natali Ruchansky's Hit Papers
Peers
Comparison fields: 5 of 46
- Information Systems 205
- Sociology and Political Science 299
- Communication 44
- Artificial Intelligence 175
- Signal Processing 57
Countries citing papers authored by Natali Ruchansky
This map shows the geographic impact of Natali Ruchansky'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 Natali Ruchansky with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Natali Ruchansky more than expected).
Fields of papers citing papers by Natali Ruchansky
This network shows the impact of papers produced by Natali Ruchansky. 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 Natali Ruchansky. The network helps show where Natali Ruchansky may publish in the future.
Co-authors
The 19 scholars most cited alongside Natali Ruchansky, 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 | Combating Fake News Hit paper breakdown → | 2019 | 324 |
| 2 | CSI: A Hybrid Deep Model for Fake News. | 2017 | 16 |
| 3 | 2013 | 16 | |
| 4 | 2012 | 9 | |
| 5 | 2012 | 7 | |
| 6 | 2012 | 4 | |
| 7 | 2011 | 4 | |
| 8 | 2013 | 4 | |
| 9 | 2023 | 3 |
About Natali Ruchansky
Natali Ruchansky is a scholar working on Artificial Intelligence, Information Systems, Signal Processing, Infectious Diseases and Sociology and Political Science, having authored 9 papers that have together received 387 indexed citations. Recurring topics across this work include Spam and Phishing Detection (2 papers), SARS-CoV-2 detection and testing (2 papers), Misinformation and Its Impacts (2 papers), Internet Traffic Analysis and Secure E-voting (2 papers), Sparse and Compressive Sensing Techniques (2 papers), Advanced Malware Detection Techniques (2 papers), Game Theory and Applications (1 paper) and Embedded Systems Design Techniques (1 paper). The work is most often cited by research in Information Systems (205 citations), Sociology and Political Science (299 citations), Communication (44 citations), Artificial Intelligence (175 citations) and Signal Processing (57 citations). Natali Ruchansky has collaborated with scholars based in United States, Italy and China. Frequent co-authors include Yan Liu, He Jiang, Karishma Sharma, Feng Qian, Ming Zhang, Davide Proserpio, Sungyong Seo, Evimaria Terzi, Mark Crovella and Francesco Bonchi. Their work appears in journals such as ACM SIGCOMM Computer Communication Review, ACM Transactions on Intelligent Systems and Technology, Lecture notes in computer science and arXiv (Cornell University).
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.