Operationalizing the Concept of Digital Movement of Opinion in the Case of #RefugeesWelcome: An Enhanced Social Network Analysis Methodology

Recent work on digital political engagement has extensively shown that social media platforms enhance political participation and collective action. One key challenge when looking for evidence of citizens’ voice on social network sites is shifting through all the network ‘noise’ and zooming in on the threads that are relevant to a political cause or mobilization. We propose a focus on Twitter hashtags as a tool for tracking political trends as well as the democratic quality of online discourse. Not only is Twitter consistently one of the most influential social media platforms worldwide, it was on Twitter in particular that hashtags first appeared as an ‘organic’ way for Twitter users to make sense of the otherwise asynchronous communication environment of that particular social media platform. A second challenge is to conceptualise the type of digital force that results from citizen s’ voice on social network sites: a type of political mobilization that combines properties of social movements but which is more fluid in structure and temporality. To address this second challenge, we operationalize the concept of Digital Movement of Opinion (DMO) and test it in the case of #RefugeesWelcome, a Twitter hashtag that came to represent an extraordinary wave of empathy that characterized the publics’ reactions in key European hosting countries during the early days of the refugee crisis in 2015. By opting for methodological triangulation in the operationalization of the DMO concept, we addressed a third key challenge in Social Network Analysis research, namely the reliability and inference capacity of SNS data. To this end, we opted for a multi-method approach of analysis, which combined unsupervised quantitative methods (Twitter network and metadata analyses) and more qualitative text-based validations. This multi-method operationalization of the Digital Movement of Opinion concept provides a heuristically useful tool for future research on new forms of digital citizen participation.