James Caverlee
- Computational Mathematics top 0.5%
- Information Systems top 0.1%
- Recommender Systems and Techniques 49
- Spam and Phishing Detection 42
- Web Data Mining and Analysis 24
- Transportation top 0.5%
- Human Mobility and Location-Based Analysis 16
- Statistical and Nonlinear Physics top 0.5%
- Complex Network Analysis Techniques 39
- Artificial Intelligence top 0.5%
- Topic Modeling 33
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- Mobile Crowdsensing and Crowdsourcing 18
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- Advanced Bandit Algorithms Research 17
James Caverlee
156 papers receiving 4.4k citations
Hit Papers
Peers
Comparison fields: 5 of 135
- Computational Mathematics 168
- Information Systems 2.5k
- Transportation 668
- Statistical and Nonlinear Physics 867
- Artificial Intelligence 2.1k
Countries citing papers authored by James Caverlee
This map shows the geographic impact of James Caverlee'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 James Caverlee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Caverlee more than expected).
Fields of papers citing papers by James Caverlee
This network shows the impact of papers produced by James Caverlee. 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 James Caverlee. The network helps show where James Caverlee may publish in the future.
Co-authorship network
The 25 scholars most cited alongside James Caverlee, 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 | 2024 | 14 | |
| 2 | 2024 | 0 | |
| 3 | 2024 | 0 | |
| 4 | 2024 | 1 | |
| 5 | 2023 | 9 | |
| 6 | 2023 | 4 | |
| 7 | 2023 | 6 | |
| 8 | 2023 | 0 | |
| 9 | 2023 | 1 | |
| 10 | 2021 | 47 | |
| 11 | 2020 | 59 | |
| 12 | 2018 | 7 | |
| 13 | 2017 | 5 | |
| 14 | 2016 | 1 | |
| 15 | Towards Geo-Social Intelligence: Mining, Analyzing, and Leveraging Geospatial Footprints in Social Media. | 2013 | 11 |
| 16 | 2013 | 12 | |
| 17 | 2009 | 8 | |
| 18 | Social Honeypots: Making Friends With A Spammer Near You. | 2008 | 77 |
| 19 | Characterizing Web Spam Using Content and HTTP Session Analysis. | 2007 | 21 |
| 20 | Introducing the Webb Spam Corpus: Using Email Spam to Identify Web Spam Automatically | 2006 | 66 |
About James Caverlee
James Caverlee is a scholar working on Computational Mathematics, Information Systems and Computer Science Applications, having authored 166 papers that have together received 4.6k indexed citations. Recurring topics across this work include Recommender Systems and Techniques (49 papers), Spam and Phishing Detection (42 papers), Complex Network Analysis Techniques (39 papers), Topic Modeling (33 papers), Web Data Mining and Analysis (24 papers), Mobile Crowdsensing and Crowdsourcing (18 papers), Advanced Bandit Algorithms Research (17 papers) and Human Mobility and Location-Based Analysis (16 papers). The work is most often cited by research in Computational Mathematics (168 citations), Information Systems (2.5k citations) and Transportation (668 citations). James Caverlee has collaborated with scholars based in United States, China and South Korea. Frequent co-authors include Zhiyuan Cheng, Steve Webb, Kyumin Lee, Kyumin Lee, Ziwei Zhu, Jianling Wang, Xia Hu, Ying Ding, A. E. Frazho and Erjia Yan. Their work appears in journals such as ACM Transactions on Intelligent Systems and Technology, ACM Transactions on Knowledge Discovery from Data, Information Sciences, IEEE Internet Computing and International Journal of Web and Grid Services.
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.