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How do we study misogyny in the digital age? A systematic literature review using a computational linguistic approach

Author

Listed:
  • Lara Fontanella

    (G. d’Annunzio University of Chieti-Pescara)

  • Berta Chulvi

    (Universitat Politècnica de València
    Universitat de València)

  • Elisa Ignazzi

    (G. d’Annunzio University of Chieti-Pescara)

  • Annalina Sarra

    (G. d’Annunzio University of Chieti-Pescara)

  • Alice Tontodimamma

    (G. d’Annunzio University of Chieti-Pescara)

Abstract

Nowadays, despite centuries of striving for equality, women still face higher levels of discrimination compared to men in nearly every aspect of life. Recently, this systemic inequality has manifested in cyberspace through the proliferation of abusive content that is even more aggressive than what one would expect in the 21st century. Various research disciplines are now attempting to characterise this new manifestation of misogyny. The endeavour to comprehend this phenomenon has resulted in a significant increase in publications from several fields, including Social Sciences, Arts and Humanities, Psychology, and Computer Science. This paper presents a systematic review of multidisciplinary research on misogyny from the years 1990 to 2022, encompassing a total of 2830 articles retrieved from the Scopus database as of December 31, 2022. The literature is thoroughly analysed using three approaches: bibliometric analysis, topic detection, and qualitative analysis of the documents. The findings suggest that the analysis of online misogyny has been the primary driver behind the exponential growth in publications in this field. Additionally, the results of the topic analysis and topic interaction reveal a limited connection between the areas of knowledge that are necessary to fully grasp this complex phenomenon.

Suggested Citation

  • Lara Fontanella & Berta Chulvi & Elisa Ignazzi & Annalina Sarra & Alice Tontodimamma, 2024. "How do we study misogyny in the digital age? A systematic literature review using a computational linguistic approach," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-15, December.
    • Handle: RePEc:pal:palcom:v:11:y:2024:i:1:d:10.1057_s41599-024-02978-7
    DOI: 10.1057/s41599-024-02978-7
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    References listed on IDEAS

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    1. Arho Suominen & Hannes Toivanen, 2016. "Map of science with topic modeling: Comparison of unsupervised learning and human-assigned subject classification," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(10), pages 2464-2476, October.
    2. Aria, Massimo & Cuccurullo, Corrado, 2017. "bibliometrix: An R-tool for comprehensive science mapping analysis," Journal of Informetrics, Elsevier, vol. 11(4), pages 959-975.
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