SODAS Data Discussion 10 February 2023

Data Discussion
Data Discussion with Natalia Umansky and Jeremy Spater

Copenhagen Center for Social Data Science (SODAS) aspirers to be a resource for all students and researchers at the Faculty of Social Sciences. We therefore invite researchers across the faculty to present ongoing research projects, project applications or just a loose idea that relates to the subject of social data science.

The rules are simple: short research presentations of ten minutes are followed by twenty minutes of debate. No papers will be circulated beforehand, and the presentations cannot be longer than five slides.

Presenter: Natalia Umansky, University of Zurich

TitleSpreading like wildfire: The securitization of the Amazon rainforest fires on Twitter

Abstract: As a tool of political communication and information diffusion, social media has transformed the process of securitization, allowing (in)security messages to spread and scale up rapidly. Focusing on the case of the Amazon rainforest fires in 2019, this article seeks to answer two questions: How does securitization spread in online networks? And who are the actors that contribute to the diffusion of security messages? To explore this puzzle, the study develops a dictionary of query terms and performs a full-archive search to collect tweets posted between June and October 2019 and reconstruct the communication network of over three million users. Drawing from theories of online activism and research on information diffusion in networks, the study uses both the structure of the Twitter network and the dynamics of activity in message exchange to identify four types of users and explore their roles in the spread of the message. The findings shed new light on the ways in which social media facilitates the definition of security problems and provide empirical evidence of the prominent position of influence taken by lay actors in the process of securitization.

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Presenter: Jeremy Spater, Department of Political Science, University of Oslo

Title: Measuring Segregation and Individual Outgroup Exposure: The k-Nearest-Neighbors Metric

Abstract: What is the connection between physical space and interpersonal interaction? This question is of interest for understanding, among other things, how inter-group segregation affects ethnic conflict. We explore this question using geocoded network data and simulations. We develop two metrics – one of individual outgroup exposure, and one of area-level aggregate segregation – based on geocoded individual locations. Drawing on an original network census, we demonstrate that our exposure metric, the k-nearest-neighbors score, accurately reflects social contact, which conditions intergroup relations. Moreover, we find that this exposure metric is a better proxy for social contact than two current measures. Next, we demonstrate that the segregation metric, the mean of the k-nearest-neighbors score, is an accurate measure of aggregate residential segregation. We use a simulation approach to demonstrate that the proposed aggregate segregation metric can more accurately discriminate between segregated and integrated areas than two commonly-used existing metrics. Finally, we apply exponential random graph models (ERGM) to the network data to explore how different dimensions of interpersonal contact -- which we characterize along social, economic, and political dimensions -- vary differently across space. The results have implications for how changing residential patterns are likely to impact prospects for intergroup cooperation along the various dimensions.