Geoffrey Barbier
- Sociology and Political Science top 5%
- Communication top 2%
- Artificial Intelligence top 10%
- Computer Science Applications top 2%
- Information Systems top 5%
- Co-authors
- Huiji GaoRebecca GoolsbyHuan LiuPritam GundechaReza ZafaraniGabriel Pui Cheong FungZhuo FengShamanth Kumar
- Topics
- Complex Network Analysis Techniques (3 papers)Scientific Computing and Data Management (3 papers)Spam and Phishing Detection (2 papers)
- Journals
- IEEE Intelligent SystemsWiley Interdisciplinary Reviews Data Mining and Knowledge DiscoveryACM Transactions on Knowledge Discovery from Data
- Partner nations
- United States
In The Last Decade
Geoffrey Barbier
9 papers receiving 643 citations
Hit Papers
Peers
Comparison fields: 5 of 80
- Sociology and Political Science 279
- Communication 261
- Artificial Intelligence 174
- Computer Science Applications 130
- Information Systems 109
Countries citing papers authored by Geoffrey Barbier
This map shows the geographic impact of Geoffrey Barbier'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 Geoffrey Barbier with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Geoffrey Barbier more than expected).
Fields of papers citing papers by Geoffrey Barbier
This network shows the impact of papers produced by Geoffrey Barbier. 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 Geoffrey Barbier. The network helps show where Geoffrey Barbier may publish in the future.
Co-authorship network of co-authors of Geoffrey Barbier
This figure shows the co-authorship network connecting the top 25 collaborators of Geoffrey Barbier. A scholar is included among the top collaborators of Geoffrey Barbier based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Geoffrey Barbier. Geoffrey Barbier is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 25 | |
| 2 | 11 | |
| 3 | 25 | |
| 4 | 3 | |
| 5 | 55 | |
| 6 | 39 | |
| 7 | Finding provenance data in social media | 3 |
| 8 | 9 | |
| 9 | Harnessing the Crowdsourcing Power of Social Media for Disaster Reliefbreakdown → | 532 |
About Geoffrey Barbier
Geoffrey Barbier is a scholar working on Information Systems and Management, Communication and Statistical and Nonlinear Physics, having authored 9 papers that have together received 702 indexed citations. Recurring topics across this work include Complex Network Analysis Techniques (3 papers), Scientific Computing and Data Management (3 papers) and Spam and Phishing Detection (2 papers). The work is most often cited by research in Communication (261 citations), Computer Science Applications (130 citations) and Geography, Planning and Development (87 citations). Geoffrey Barbier has collaborated with scholars based in United States. Frequent co-authors include Huiji Gao, Rebecca Goolsby, Huan Liu, Pritam Gundecha, Reza Zafarani, Gabriel Pui Cheong Fung, Huan Liu, Zhuo Feng, Shamanth Kumar and Mohammad Ali Abbasi. Their work appears in journals such as IEEE Intelligent Systems, Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery and ACM Transactions on Knowledge Discovery from Data.
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.