Have we even solved the first 'big data challenge?' Practical issues concerning data collection and visual representation for social media analytics

Abstract

Copyright © 2016 Phillip Brooker, Julie Barnett, Timothy Cribbin and Sanjay Sharma The present chapter explores the technical and computational processes through which social media data is shaped into research findings. The authors make this argument by depicting the effects of two practical issues - API rate limiting in Twitter data collection and the use of spatial mapping algorithms in visualising those data - on resulting analyses. Such issues are not problematic to social media analytic research; rather, they can be used as resources for helping to characterise and understand the data at hand. Hence, the authors work to demonstrate the value in incorporating these reflexive analyses of technical and computational processes into our accounts; to advocate thinking in assemblages as a requirement for making analytic claims with 'big' social media data.