Discussing Migration on Social Media in Times of Populism

Mass migration is an issue that every government has to address and that influences the public discourse in most societies recently. While there are studies on the degree of populist pervasion of the mass media (Rooduijn 2014), so far there has been little research on the extent to which the online discourse on migration has been permeated by populist narratives (Engesser, Fawzi and Larsson 2017). We want to find out if the Twitter discourse on the Global Compact for Safe, Orderly and Regular Migration (GCM) in Marrakech in December 2018 was predominantly populist and which populist communication strategies were used. We use two indicators to identify the level of populisms in the Twitter debate on migration: the populist permeation of (1) those tweets on the GCM that reach a wider audience (degree of interaction like retweeting and favoriting) and the populist permeation of (2) those tweets carried out by the most relevant actors of the Twitter discourse network on the GCM (centrality of network nodes). Our study will do two steps: First, in order to find out the populist permeation of the discourse, we identify the share of those tweets on the GCM reaching a wider audience that can be classified as populist (the 500 most interactive tweets). And in a second step, we focus on the most relevant actors of the Twitter discourse network on the GCM (the 500 most relevant accounts) and investigate what share of their tweets can be classified as populist. This study sheds light on the degree of populist penetration of the online discourse on migration, about the populist communication strategies and about the most relevant actors in this field. For this analysis, we mined more than one million tweets on the most important hashtags on the GCM: #GlobalCompactMigration and #UNMigrationPact. We queried the Twitter API using the R package streamR (Barberá 2018). The data collection was done from 5 to 19 December 2018, covering the period before the conference and the following days. To classify populist tweets, we use a content analysis coding schema built by Ernst et al. (2017). We then use SNA methodology to identify the central actors of the GCM discourse network, or more precisely we calculate PageRanks as a centrality measure (Page et al. 1998). Bibliography Barberá, Pablo. “Access to Twitter Streaming API via R.” CRAN, 2018. https://cran.r-project.org/web/packages/. Engesser, Sven, Nayla Fawzi, and Anders Olof Larsson. “Populist online communication: Introduction to the special issue.” Information, Communication & Society 20, no. 9 (2017): 1279–92. Ernst, Nicole, Sven Engesser, Florin Büchel, Sina Blassnig, and Frank Esser. “Extreme parties and populism: An analysis of Facebook and Twitter across six countries.” Information, Communication & Society 20, no. 9 (2017): 1347–64. Page, Lawrence, Sergey Brin, Rajeev Motwani, and Terry Winograd. “The PageRank citation ranking: Bringing order to the Web.” Stanford InfoLab Techreport, 1998. http://ilpubs.stanford.edu:8090/422. Rooduijn, Matthijs. “The Mesmerising Message: The Diffusion of Populism in Public Debates in Western European Media.” Political Studies 62, no. 4 (2014): 726–44.