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Discovering Representations of Democracy in Big Data: Purposive Semantic Sample Selection for Qualitative and Mixed-Methods Research

dc.contributor.authorPlisiecki, Hubert
dc.contributor.authorKwiatkowska, Agnieszka
dc.date.accessioned2024-12-03T07:17:57Z
dc.date.available2024-12-03T07:17:57Z
dc.date.issued2024-11-30
dc.identifier.urihttp://hdl.handle.net/11089/53844
dc.description.abstractThe increasing volume of large, multi-thematic text corpora in social sciences presents a challenge in selecting relevant documents for qualitative and mixed-methods research. Traditional sample selection methods require extensive manual coding or prior dataset knowledge, while unsupervised methods can yield inconsistent results with theory-driven coding. To address this, we propose purposive semantic sampling – a Natural Language Processing approach using document-level embeddings created by a weighted average of word vectors with term frequency-inverse document frequency (tf-idf). We demonstrate its effectiveness using the example of democracy, a complex topic difficult to retrieve from parliamentary corpora. This method applies to any multi-thematic research area within big data, offering a reliable, efficient sample selection method for social research texts. Our contribution includes validating this NLP approach for social sciences and humanities as well as providing a robust tool for researchers, facilitating deeper qualitative analysis and exploration of big data corpora within the computational grounded theory framework.en
dc.description.abstractWzrastająca liczba dużych, wielotematycznych korpusów tekstowych w naukach społecznych stwarza wyzwanie w doborze odpowiednich dokumentów do badań jakościowych i mieszanych. Tradycyjne metody doboru próby wymagają intensywnego kodowania manualnego lub uprzedniej wiedzy o zbiorze danych, podczas gdy metody nienadzorowane mogą dawać wyniki niespójne z kodowaniem opartym na teorii. Aby temu zaradzić, autorzy proponują semantyczny dobór celowy próby – podejście wykorzystujące przetwarzanie języka naturalnego z użyciem osadzeń dokumentów tworzonych przez średnią ważoną wektorów słów, z wagami określonymi współczynnikiem tf-idf (częstość terminu odwrotnie proporcjonalna do częstości dokumentu). Skuteczność podejścia zademonstrowano na przykładzie demokracji – złożonego tematu, trudnego do wydobycia z korpusów parlamentarnych. Proponowana metoda pozwala na niezawodny i efektywny dobór próby tekstów w dowolnej dziedzinie badań korzystającej z Big Data. Wkład autorów obejmuje walidację tego podejścia NLP dla nauk społecznych i humanistycznych oraz dostarczenie rzetelnego narzędzia dla badaczy, ułatwiającego pogłębioną analizę jakościową i eksplorację korpusów Big Data w ramach obliczeniowej teorii ugruntowanej.pl
dc.language.isoen
dc.publisherWydawnictwo Uniwersytetu Łódzkiegopl
dc.relation.ispartofseriesPrzegląd Socjologii Jakościowej;4pl
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0
dc.subjectsample selectionen
dc.subjectpurposive samplingen
dc.subjectqualitative researchen
dc.subjectword embeddingsen
dc.subjectdemocracyen
dc.subjectdobór próbypl
dc.subjectdobór celowypl
dc.subjectbadania jakościowepl
dc.subjectword embeddingspl
dc.subjectdemokracjapl
dc.titleDiscovering Representations of Democracy in Big Data: Purposive Semantic Sample Selection for Qualitative and Mixed-Methods Researchen
dc.title.alternativeOdkrywanie reprezentacji demokracji w Big Data: semantyczny dobór celowy próby do badań jakościowych i mieszanychpl
dc.typeArticle
dc.page.number18-43
dc.contributor.authorAffiliationPlisiecki, Hubert - Polish Academy of Sciences, Poland; Uniwersytet SWPSen
dc.contributor.authorAffiliationKwiatkowska, Agnieszka - European University Institute, Italy; SWPS University, Polanden
dc.identifier.eissn1733-8069
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dc.contributor.authorEmailPlisiecki, Hubert - hplisiecki@gmail.com
dc.contributor.authorEmailKwiatkowska, Agnieszka - agn.kwiatkowska@swps.edu.pl
dc.identifier.doi10.18778/1733-8069.20.4.02
dc.relation.volume20


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