Pedro Calais: h-index, Total Citations, and Citation Map
Pedro Calais's h-index is 13 (17 i10-index, 1,555+ total citations across 5+ publications) according to Google Scholar as of May 2026. Pedro Calais is affiliated with Software Engineer, Researcher in Federal University of Minas Gerais.
Pedro Calais is a researcher affiliated with Software Engineer, Researcher in Federal University of Minas Gerais, specializing in Data Mining, Machine Learning, Computational Social Sciences. Their work has been cited 1,555 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Pedro Calais's Citation Metrics
Bibliometric impact based on 5 indexed publications.
- H-Index
- 13
- i10-Index
- 17
- Total Citations
- 1,555
- Citing Countries
- 11
As of May 2026.
Pedro Calais has an h-index of 13 and 1,555 total citations across 5 publications, with research cited by institutions in 11 countries.
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Top Cited Works
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A measure of polarization on social media networks based on community boundaries
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Visa Evidence Package
Views and exports tuned for EB-1A, O-1A, and EB-2 NIW petitions. Sustained acclaim, geographic reach, and independent-citation filtering are the strongest evidence categories immigration adjudicators look for.
Significant Contributions
Auto-detected research lines — a seminal paper and the follow-up work building on it. Review and edit before using in a petition. Each Free PDF opens in a new tab — EB-1A organises this into the structure USCIS applies to Criterion 5 of 8 CFR § 204.5(h)(3)(v); EB-1B re-frames it under § 204.5(i)(3) (outstanding researcher); NIW presents it under prong 2 of Matter of Dhanasar.
The researcher developed a transfer-learning framework for real-time sentiment analysis and extended it to measure social media polarization, establishing a foundational approach to computational opinion dynamics.
The researcher characterized broadband user behavior and e-business activities, establishing a foundational framework for understanding digital consumer patterns that has been widely adopted by independent scholars.
The researcher developed a framework for characterizing and detecting hateful users on Twitter, establishing a foundational approach for identifying online toxicity.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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