Abstract
AbstractNowadays, 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.
Publisher
Springer Science and Business Media LLC
Reference98 articles.
1. Alghamdi R, Alfalqi K (2015) A survey of topic modeling in text mining. Int J Adv Comput Sci Appl 6(1):147–153
2. Allen A (2021) Feminist perspectives on power. In: Zalta E (ed) The Stanford encyclopedia of philosophy. Metaphysics Research Lab, Stanford University
3. Anzovino M, Fersini E, Rosso P (2018) Automatic identification and classification of misogynistic language on Twitter. In: Silberztein M, Atigui F, Kornyshova E et al (eds) Natural language processing and information systems (NLDB) 2018. Lecture notes in computer science, vol 10859. Springer, Cham, pp. 57–64
4. Aria M, Cuccurullo C (2017) bibliometrix: an R-tool for comprehensive science mapping analysis. J Informetr 11(4):959–975
5. Attanasio G, Pastor E (2020) PoliTeam @ AMI: improving sentence embedding similarity with misogyny lexicons for automatic misogyny identification in Italian tweets. In: Proceedings of the 7th evaluation campaign of natural language processing and speech tools for Italian (EVALITA 2020) (eds Basile V, Croce D, Maro M, Passaro LC) CEUR workshop proceedings, vol 2765, Accademia University Press, Torino