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Odkrywanie reprezentacji demokracji w Big Data: semantyczny dobór celowy próby do badań jakościowych i mieszanych

Autor

Hubert Plisiecki Polish Academy of Sciences, Poland; Uniwersytet SWPS https://orcid.org/0000-0002-5273-1716
Agnieszka Kwiatkowska European University Institute, Italy; SWPS University, Poland https://orcid.org/0000-0002-2241-0831

DOI:

https://doi.org/10.18778/1733-8069.20.4.02

Słowa kluczowe:

dobór próby, dobór celowy, badania jakościowe, word embeddings, demokracja

Abstrakt

Wzrastająca 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.

Pobrania

Brak dostępnych danych do wyświetlenia.

Biogramy autorów

Hubert Plisiecki - Polish Academy of Sciences, Poland; Uniwersytet SWPS

A PhD Candidate in Psychology at the Polish Academy of Sciences (PAN), works at the intersection of political science, psychology, sociology, linguistics, and machine learning. Granted MSc in Psychological Research Methods with Data Science. Research assistant at the Digital Social Sciences lab at PAN and in a project “Institutionalization of political parties in the parliaments of Central Europe – data mining of parliamentary debates” at SWPS University. His research interests include Machine Learning in Social Sciences, Meta-analytic Bias Detection.

Agnieszka Kwiatkowska - European University Institute, Italy; SWPS University, Poland

Assistant Professor at the SWPS University and Jean Monnet Fellow at the European University Institute, holds a PhD in sociology and an MA in political science. Her research focuses on political discourse: how issues are politicized, introduced into parliamentary competition, and become determinants of political behavior. Principal Investigator in the project “Institutionalization of political parties in the parliaments of Central Europe – data mining of parliamentary debates” (funding: National Science Centre), which investigates mixed methods of analyzing parliamentary speeches and voting.

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2024-11-30

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Plisiecki, H., & Kwiatkowska, A. (2024). Odkrywanie reprezentacji demokracji w Big Data: semantyczny dobór celowy próby do badań jakościowych i mieszanych. Przegląd Socjologii Jakościowej, 20(4), 18–43. https://doi.org/10.18778/1733-8069.20.4.02