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Svitlana Volkova

Svitlana Volkova has 2 papers in our library, in 2013–2017, most of them on Sentiment & emotion, Multilingual & cross-lingual NLP; often with David Yarowsky, Eunsol Choi, Hannah Rashkin.

2 papers in our library · 2013–2017

OpenAlex · ORCID · h-index 22 · 120 works · 3,142 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 272 times in our library · ⭐ Paper Stars ranking: #1786

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢AptimaWoburn · United States2024–2026 · 21 paperscurrent🔬Pacific Northwest National LaboratoryRichland · United States2016–2025 · 59 papers🎓Johns Hopkins UniversityBaltimore · United States2013–2016 · 15 papers🎓Kansas State UniversityManhattan · United States2010 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksSocial Sciences · 1 workInformation Systems · 1 workSociology and Political Science · 1 work

🧭 Topics 2Research subjects and languages the work is about.Multilingual & cross-lingual NLP · 1 workSentiment & emotion · 1 work

 Works by year

2010s

20171 paperTruth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking20131 paperExploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media

 Papers

The most cited first (OpenAlex and our library).

  1. Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking (2017 · 1,074 citations)
    The paper looks at whether the wording of a text reveals how trustworthy it is. It treats truth as graded rather than as a simple true/false split. The authors built a corpus contrasting trusted English Gigaword articles with articles from seven unreliable sites, grouped as satire, hoax, or propaganda. They compared…
  2. Exploring Demographic Language Variations to Improve Multilingual Sentiment Analysis in Social Media (2013 · 250 citations)
    The paper asks whether men and women express opinions differently on Twitter, and whether a sentiment classifier can use those differences.

 Co-authors here

6 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 272 times (of the 141,840 papers whose bibliographies we read); 36 citing papers with a page here →.

Updated

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