“We hate her . . . and you too”: Polarized intersectionality in Italy throughout changing political scenarios

Abstract

This article proposes a joint application of online network analysis and NLP techniques to explore dynamics of “polarized intersectionality”—that is, how (mis)representations of women that develop online and at the intersection of different axes of discrimination entwine with ideological and affective polarization. We look in particular at whether and how digital (mis)representations change together with political scenarios in which political parties, leaders, and partisan communities more in general swing from collaboration to hostility. Our analysis of two Twitter conversations that put women at the center of attention show that changing political scenarios generate different digital conversations which, in turn, reflect patterns of alliances and rivalry. Regardless of these changes, women are invariantly (mis)represented in narratives that are often weaponized against political enemies in ways that foster both ideological and affective political polarization.