Dialoguing with Data and Data Reduction: An Observational, Narrowing-Down Approach to Social Media Network Analysis

Abstract

In this article, we propose an observational, narrowing-down approach to analysing social media networks and developing research design by the joint use of computational algorithms and researchers’ inductive exploration and interpretive explanations. The Brexit referendum on Twitter study is used to illustrate how we applied this approach in practice. In this study, observation helped us combine the strengths of computational statistical analysis and modelling and of inductive inquiries. Computational algorithms and tools including Elasticsearch, Kibana and Gephi provided us with an “ethnographic field” where we were able to inductively observe the relationships among users and to reduce the amount of data down to a level in which we could intuitively understand these relationships. In traditional observational studies, talking to human subjects and observing their interactions in a research site are important to ethnographers. Likewise, it is useful for social science researchers to dialogue with data, observe human relationships embodied in the data and reconstructed by computational tools, and understand these relationships through closely examining a small batch of meaningful data that is extracted from large-scale data. In this case study, adopting the proposed approach, we found the importance of political disagreement leading to a tale of two politicians, in which pro-Brexit users denounced @David_Cameron but legitimised @Nigel_Farage.