Intersectionality on the go:The diffusion of Black feminist knowledge across disciplinary and geographical borders

Kimberlé Crenshaw coined the term “intersectionality” in 1989 as a critique of feminist and critical race scholarship's neglect of—respectively—race and gender. Since then, the concept has been interpreted and reinterpreted to appeal to new disciplinary, geographical, and sociocultural audiences, generating heated debates over its appropriation and continued political significance. Drawing on all 3,807 publications in Scopus that contain the word “intersectionality” in the title, abstract, or keywords, we map the spread of intersectionality in academia through its citations. Network analysis reveals the contours of its diffusion among the 6,098 scholars in our data set, while automated text analysis, manual coding, and the close reading of publications reveal how the application and interpretation of intersectional thinking has evolved over time and space. We find that the diffusion network exhibits communities that are not well demarcated by either discipline or geography. Communities form around one or a few highly referenced scholars who introduce intersectionality to new audiences while reinterpreting it in a way that speaks to their research interests. By examining the microscopic interactions of publications and citations, our complex systems approach is able to identify the macroscopic patterns of a controversial concept's diffusion

Why it is important to consider negative ties when studying polarized debates: A signed network analysis of a Dutch cultural controversy on Twitter

Despite the prevalence of disagreement between users on social media platforms, studies of online debates typically only look at positive online interactions, represented as networks with positive ties. In this paper, we hypothesize that the systematic neglect of conflict that these network analyses induce leads to misleading results on polarized debates. We introduce an approach to bring in negative user-to-user interaction, by analyzing online debates using signed networks with positive and negative ties. We apply this approach to the Dutch Twitter debate on ‘Black Pete’—an annual Dutch celebration with racist characteristics. Using a dataset of 430,000 tweets, we apply natural language processing and machine learning to identify: (i) users’ stance in the debate; and (ii) whether the interaction between users is positive (supportive) or negative (antagonistic). Comparing the resulting signed network with its unsigned counterpart, the retweet network, we find that traditional unsigned approaches distort debates by conflating conflict with indifference, and that the inclusion of negative ties changes and enriches our understanding of coalitions and division within the debate. Our analysis reveals that some groups are attacking each other, while others rather seem to be located in fragmented Twitter spaces. Our approach identifies new network positions of individuals that correspond to roles in the debate, such as leaders and scapegoats. These findings show that representing the polarity of user interactions as signs of ties in networks substantively changes the conclusions drawn from polarized social media activity, which has important implications for various fields studying online debates using network analysis

Hydrographische kaart van het Vriesche Zeegat : met een gedeelte der Vriesche en Groningsche Wadden

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