Marcos Andre Goncalves: h-index, Total Citations, and Citation Map
Marcos Andre Goncalves's h-index is 62 (248 i10-index, 15,237+ total citations across 5+ publications) according to Google Scholar as of May 2026. Marcos Andre Goncalves is affiliated with Computer Science Professor, Universidade Federal de Minas Gerais, Brazil.
Marcos Andre Goncalves is a researcher affiliated with Computer Science Professor, Universidade Federal de Minas Gerais, Brazil, specializing in Information Retrieval, Machine Learning, Natural Language Processing. Their work has been cited 15,237 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Marcos Andre Goncalves's Citation Metrics
Bibliometric impact based on 5 indexed publications. Of these, 3 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 62
- i10-Index
- 248
- Total Citations
- 15,237
- Citing Countries
- 14
As of May 2026.
Marcos Andre Goncalves has an h-index of 62 and 15,237 total citations across 5 publications, with research cited by institutions in 14 countries.
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Top Cited Works
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Infodemics and health misinformation: a systematic review of reviews
2022671
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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 established a formal 5S model for digital libraries, providing a foundational theoretical framework that has been widely adopted by independent scholars.
The researcher established a standardized benchmark for comparing state-of-the-practice sentiment analysis methods, providing a critical reference point for evaluating algorithmic performance in natural language processing.
The researcher pioneered methods for predicting YouTube video popularity using early view patterns, establishing a foundational approach in viral content analysis.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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