Ipek Baris Schlicht: h-index, Total Citations, and Citation Map
Ipek Baris Schlicht's h-index is 13 (15 i10-index, 305+ total citations across 26+ publications) according to Google Scholar as of May 2026. Ipek Baris Schlicht is affiliated with Universitat Politècnica de València, Deutsche Welle.
Ipek Baris Schlicht is a researcher affiliated with Universitat Politècnica de València, Deutsche Welle, specializing in Applied NLP, Misinformation Detection, Computational Journalism. Their work has been cited 305 times. This profile visualizes their global influence, highlighting strong citation networks in United States.
Ipek Baris Schlicht's Citation Metrics
Bibliometric impact based on 26 indexed publications. Of these, 18 are original research articles — the rest are literature highlights, conference abstracts or theses.
- H-Index
- 13
- i10-Index
- 15
- Total Citations
- 305
- Citing Countries
- 19
As of May 2026.
Ipek Baris Schlicht has an h-index of 13 and 305 total citations across 26 publications, with research cited by institutions in 19 countries.
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Top Cited Works
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Sexism prediction in spanish and english tweets using monolingual and multilingual bert and ensemble models
202143
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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.
236 citing papers could not be classified (no author data) — excluded from the percentages above.
The researcher developed methods to mitigate cultural differences in multilingual check-worthy claim detection, establishing a framework for cross-lingual credibility assessment.
The researcher developed methods for detecting misinformation by leveraging contextual embeddings and integrating content, prior knowledge, and source information.
The researcher established a systematic framework for detecting health misinformation and extended this work to analyze disparities in multilingual large language model healthcare applications.
Citation trend (last 10 years)Click to expand
Citation Trend (Last 10 Years)
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