ValuesML: A new multilingual dataset for values detection in news and political manifestos

Values are important building blocks of political ideologies and are frequently invoked in political debates. Yet, because values are abstract, understanding how they are expressed in real-world discourse, and how this varies across contexts, remains a challenge. In this paper, we introduce a large-scale, expert-annotated dataset of value expression in political text, comprising news articles and political manifestos across nine languages. The dataset is grounded in the refined theory of human values (Schwartz et al., 2012) and was developed through an iterative process of annotation guideline development, annotator training, and expert curation. Value annotations capture both the type of value expressed and its evaluative framing, distinguishing whether values are expressed as attained or constrained. The final dataset comprises 2648 texts and 74,231 sentences across nine languages. This resource enables systematic, cross-linguistic analysis of value expression in political communication and provides a benchmark for developing and evaluating computational models of value detection.

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Journal
Behavior Research Methods
Published
2026-08-20
DOI
https://doi.org/10.3758/s13428-026-03092-z
Citations
1
Primary Topic
Hate Speech and Cyberbullying Detection
Type
article
Field-Weighted Citation Impact
0.00

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article

ValuesML: A new multilingual dataset for values detection in news and political manifestos

Maria Francesca Romano, Torven Schalk, Luigi Izzo, Fiorella Battaglia et al.
1 citations
Behavior Research Methods
Hate Speech and Cyberbullying Detection
article

ValuesML: A new multilingual dataset for values detection in news and political manifestos

Maria Francesca Romano, Torven Schalk, Luigi Izzo, Fiorella Battaglia, Shani Oppenheim‐Weller, Maya Benish‐Weisman, Anat Bardi, Mina Hristova, Mafalda Dâmaso, Emilia Zankina, Christin‐Melanie Vauclair, Joshua Lake, Giuseppe Di Vetta, Giuliano Bobba, Irene Coppola, Elif Sandal‐Önal, Stefanie Habermann, Lucilla Gatt, Petra Auer, Maria Dagioglou, Nicolas Stefanovitch, Livia Aulino, Johannes Kiesel, Maria Cristina Gaeta, Henrik Dobewall, Antonella Seddone, Ricarda Scholz-Kuhn, Luana Russo, Frédéric Gonthier, Ingmar Leijen, Sandrine Astor, H. van Herk, Thomas P. Oeschger, Oscar Smallenbroek, Sharon Arieli, Meike Morren, Nazan Avcı, Aurelia Tamò‐Larrieux, Ahmet Çoymak, Reşit Kışlıoğlu, Mario Scharfbillig, Constanze Beierlein, Patricia R. Collins, Vladimir Ponizovskiy, Ella Daniel, Georgios Petasis, Einat Elizarov, Tim Reeskens, Dora Katsamori, Ömer Topuz, Theresa Reitis-Münstermann, Sheng Ye, P. Brock, Ioannis Elissaios Paparrigopoulos, naama erlich, Peculiar Ezeigwe-Ephraim, Noam Gerera, Demet Islambay Yapali, Panos Kapetanakis, Chaya Koleva, Mael Mesplou, Vanina Ninova, Johannes Karl, Ronald Fischer, Roberta Koleva, Pierre Chiron, Duygu Ozturk, Emmanuel Cartier, Sjoukje Goldman, Berna Öney, Murat Ardag, Anna Krasteva, Alina Starovolsky-Shitrit, Christina Christodoulou, Joanne Sneddon
article en
1 citations

Abstract

Values are important building blocks of political ideologies and are frequently invoked in political debates. Yet, because values are abstract, understanding how they are expressed in real-world discourse, and how this varies across contexts, remains a challenge. In this paper, we introduce a large-scale, expert-annotated dataset of value expression in political text, comprising news articles and political manifestos across nine languages. The dataset is grounded in the refined theory of human values (Schwartz et al., 2012) and was developed through an iterative process of annotation guideline development, annotator training, and expert curation. Value annotations capture both the type of value expressed and its evaluative framing, distinguishing whether values are expressed as attained or constrained. The final dataset comprises 2648 texts and 74,231 sentences across nine languages. This resource enables systematic, cross-linguistic analysis of value expression in political communication and provides a benchmark for developing and evaluating computational models of value detection.

Behavior Research MethodsVol. 58(10)
Edith Cowan University (AU), New Bulgarian University (BG), Ben-Gurion University of the Negev (IL), Carl von Ossietzky Universität Oldenburg (DE), University of Salento (IT), East China University of Science and Technology (CN), Scuola Superiore Sant'Anna (IT), Tel Aviv University (IL), Free University of Bozen-Bolzano (IT), The University of Western Australia (AU), Royal Holloway University of London (GB), University of Basel (CH), Başkent University (TR), Durham University (GB), Jerusalem College of Technology (IL), Tilburg University (NL), Hebrew University of Jerusalem (IL), Central European University (AT), Bielefeld University (DE), National Centre of Scientific Research "Demokritos" (GR), Bulgarian Academy of Sciences (BG), European Commission (BE), Università degli Studi Suor Orsola Benincasa (IT), Istanbul Medipol University (TR), Maastricht University (NL), University of Cyprus (CY), Finnish Institute for Health and Welfare (FI), Victoria University of Wellington (NZ), Hamm-Lippstadt University of Applied Sciences (DE), Iscte – Instituto Universitário de Lisboa (PT), GESIS - Leibniz Institute for the Social Sciences (DE), D’Or Institute for Research and Education (BR), Arcadia (US), Laboratoire Pacte (FR), Joint Research Centre (IT), Dublin City University (IE), Amsterdam University of Applied Sciences (NL), University of Turin (IT), Ludwig-Maximilians-Universität München (DE), Temple University (US), Vrije Universiteit Amsterdam (NL), University of Amsterdam (NL), Université Grenoble Alpes (FR), Ruhr University Bochum (DE), University of Haifa (IL), Erasmus University Rotterdam (NL), Abdullah Gül University (TR)
Università degli Studi di Napoli Federico II, Universität Basel, Temple University, Carl von Ossietzky Universität Oldenburg, İstanbul Medipol Üniversitesi, Instituto D'Or de Pesquisa e Ensino, European Commission, Durham University, Victoria University of Wellington, Dublin City University, Victoria University, Edith Cowan University, Erasmus Universiteit Rotterdam, Vrije Universiteit Amsterdam, Universiteit Maastricht, East China University of Science and Technology, Hebrew University of Jerusalem, Orta Doğu Teknik Üniversitesi, Centre National de la Recherche Scientifique, Ben-Gurion University of the Negev, University of Haifa, Universität Bielefeld, Bulgarian Academy of Sciences, Central European University, Ruhr-Universität Bochum, Università degli Studi di Torino, Scuola Superiore Sant'Anna, Universiteit van Tilburg, Libera Università di Bolzano, New Bulgarian University, Faculty of Education, Victoria University of Wellington
Openalex Percentile: Top 99%
Hate Speech and Cyberbullying Detection
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