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.
Get full access to this article
View all access options for this article.
References
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
