Mikaela Irene Fudolig: h-index, Total Citations, and Citation Map
Mikaela Irene Fudolig's h-index is 9 (8 i10-index, 214+ total citations across 3+ publications) according to Google Scholar as of May 2026. Mikaela Irene Fudolig is affiliated with Lecturer, Adelaide University.
Mikaela Irene Fudolig is a researcher affiliated with Lecturer, Adelaide University, specializing in computational social science, digital humanities, data science. Their work has been cited 214 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Mikaela Irene Fudolig's Citation Metrics
Bibliometric impact based on 3 indexed publications.
- H-Index
- 9
- i10-Index
- 8
- Total Citations
- 214
- Citing Countries
- 8
As of May 2026.
Mikaela Irene Fudolig has an h-index of 9 and 214 total citations across 3 publications, with research cited by institutions in 8 countries.
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Top Cited Works
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Sentiment and structure in word co-occurrence networks on Twitter
202237
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Top Citing Institutions
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 advanced the analysis of Twitter data by integrating sentiment and structural properties within word co-occurrence networks, as demonstrated in their 2022 publication in Applied Network Science.
The researcher pioneered a wearable-based clinical trial framework to assess and improve student well-being during the critical transition to college.
The researcher pioneered the application of wearable sleep data to predict stress in first-year college students, establishing a novel methodological framework for digital health monitoring.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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