Fourth International Conference on Communication & Media Studies

Abstract

The persistent buzz of “big data” in reference to vast amounts of unstructured data that can be scraped from the internet (Chaulagain et al., 2017; Cooley et al., 1997; Landers et al., 2016) has presented new options for data analysis in media studies, some of which draw on the quantitative and computational work. Microblog research is an exploding field stemming from computational fields and using big data for complex modelling (De Maoi et al., 2016; Feng et al., 2018; Hasanain & Elsayed, 2017; Yan et al., 2012). A microblog can be defined as a collection of short, character-constrained posts (“microposts”) to the internet (Zappavigna, 2014). The microblogging platform Twitter is known for its radical constraint on message length. Since its arrival in 2007, newer microblogging platforms have shifted priorities towards imagery, while allowing for written texts of extended lengths. Thus, the microblog has expanded to provide even more data for big data scraping. Yet, this added richness affords researchers to analyze microblogs using qualitative methods (Wargo, 2017; Zappavigna, 2016; 2018a; Zhao & Zappavigna, 2018). Following Zappavigna (2018b) and Baker (2006; 2012) in conceptualizing social-media analysis under convergence of corpus linguistics and critical discourse analysis, I discuss my decisions in analyzing a corpus of 1.5 million social media micro-/nanoposts both quantitatively and qualitatively. In navigating barriers to and affordances of mixed-method designs and their practical implications, I encourage debate as to whether broadening the reach of data collection—often praised as technologically and geographically democratized—comes at the expense of fundamental paradigmatic incommensurability.