Comparative Process-oriented Research Using Social Media and Historical Text

Abstract

This article evaluates whether we can use process-oriented theory to conduct comparative, historical social media research. There is a lack of theoretically informed approaches to studying recently digitized historical text with contemporary social media. This article argues that such perspectives are needed and extends Norbert Elias’ notions of ‘sociogenesis’ and ‘psychogenesis’ into data-driven research. Canonical process-oriented researchers such as Elias used mixed-methods approaches, including visual maps and quantitative surveys. By comparing 17th-century digitized diaries and 5 million digitized books from Google Books with contemporary tweet data, this study provides a successful case of comparing tweets with historical printed text at a big data scale. Moreover, quantitative methods are important to process-oriented methodologies and can be extended to big data empirical sources. An important finding is that there are similarities in the curation of everyday life in elite historical diaries and in more democratic forms of contemporary social media. Although accessibility and volume of content have changed over time from historical text to tweets, we found that there is a marked preference for certain words associated with communal sentiment over the centuries.

The rhetorical work of credibility-building for social scientific big data: Positioning arguments and legitimacy in empirical sociology

“…(Burrows et al, 2017: 191) Here, establishing a link to existing social scientific practice is emphasized as important for credible interpretation. In addition to geodemographic data, this idea was present in connection with, variously, digital administrative data (O'Brien, 2016;O'Brien et al, 2016), Twitter discussion data (Fitzhugh et al, 2014;McKelvey et al, 2014), and Twitter in combination with digitized texts (Murthy, 2017). While some authors described Twitter data as already well-established in the social sciences, it was argued that current uses lack theoretical underpinnings (McKelvey et al, 2014;Murthy, 2017).…”

“…In addition to geodemographic data, this idea was present in connection with, variously, digital administrative data (O'Brien, 2016;O'Brien et al, 2016), Twitter discussion data (Fitzhugh et al, 2014;McKelvey et al, 2014), and Twitter in combination with digitized texts (Murthy, 2017). While some authors described Twitter data as already well-established in the social sciences, it was argued that current uses lack theoretical underpinnings (McKelvey et al, 2014;Murthy, 2017). Well-developed theoretical understanding was considered crucial for the analysis of big data, and purportedly theory-free approaches to pattern discovery were criticized (e.g., Murthy, 2017: 18;O'Brien et al, 2017: 140).…”

“…These examples show that big data research following this strategy can include traditional methods for drawing statistical inferences and describing the data (Burrows et al, 2017;McKelvey et al, 2014;O'Brien, 2016;O'Brien et al, 2016), but also methods such as algorithmic signal detection (Fitzhugh et al, 2017) or keyword searches of Twitter and literary material (Murthy, 2017). The key point here is that the methods should have tried-and-true uses in the fields where they are applied and that one can make them applicable by moulding unfamiliar data in line with established theory.…”