Unveiling Gender-Based Needs: A Mixed-method, Multi-Dimensional Analysis of Feminist Literature

Despite the advancements achieved by the feminist movement spanning over a hundred years, the recent decades have seen a growing backlash against feminist perspectives from a diverse spectrum of actors worldwide. Yet, gender issues and demands, far from being resolved, persist as influential forces shaping various aspects of life and politics. To gain a deeper understanding and contextualize feminist engagement with contemporary issues and demands amid these polarizing times, we conducted an extensive literature review of feminist works. This review spans 406 publications issued between 2007 and 2023, covering six European country contexts (each representing distinct regional traditions), and was employed to derive a typology of what we call 'gender-based needs' – i.e., political issues and needs that have been interpreted, discussed, and criticized through the lens of gender in the feminist literature. While the initial typology was developed using manual qualitative coding techniques, in this paper we combine this with an innovative use of NLP techniques to derive a co-occurrence network, which enables us to computationally assess the connections between various theoretical tools and dimensions of relevance to 'gender-based needs'. The methodological contribution of the analysis to discussions surrounding text-as-data is threefold: first, we test the compatibility of NLP methods with complex qualitative concept-based research questions at a concrete example. Second, we compare the insights generated by the qualitative and the NLP-based approaches, identifying both the advantages and disadvantages of text-as-data approaches vis-a-vis manual analysis. Third, based on this comparison/these insights, we derive an innovative mixed methods approach to large-scale literature reviews and theory-building, which utilizes k-core analysis to visualize the interconnections among concepts, thus showing how qualitative and computational tools can be synergistically employed to offer a more insightful and holistic understanding of gender-related debates and perspectives, especially when assessing a large amount of textual data in a multi-dimensional way.