Cultural Machines. Unlocking the power of digital methods and computational techniques for understanding socio-cultural processes in digital environments

Since the advent of big data, social scientists tried to ‘unlock’ the cultural power embedded in them, by extracting qualitative ‘thick’ data from huge amount of quantitative digital data (Ford, 2014). This ambitious methodological endeavour has been undertaken by several scholars from different fields in social science, giving birth to numerous innovative research approaches and techniques. See for example the work of STS scholars who adapted the language and methodological array of Actor-Network-Theory to the analysis of big data (Vertesi and Ribes, 2019) – including works on digital mapping of scientific controversies (Venturini, 2010; 2012; Marres, 2015), digital network analysis (Cambrosio et al., 2014; Venturini et al., 2021), or the application of co-word analysis on web content (Venturini and Guido, 2012; Eykens et al., 2021). Other notable contributions have been forthcoming from digital methods (Rogers, 2009), computational approaches (Giglietto, Rossi, Bennato, 2012), interface methods (Marres and Gertliz, 2015), and platform methods (Nieborg et al., 2020) to the exploration and understanding of the huge repositories of qualitative data on social media (Lewis et al., 2013; Niederer, 2016; Rieder et al., 2018). Similarly, various ethnographic approaches have tried to mix ethnographic observation with the use of digital tools for data collection and analysis, such as ethnomining (Aipperspach et al., 2006), trace ethnography (Geiger and Ribes, 2011), ethnography for the Internet (Hine, 2015), computational ethnography (Elish and boyd, 2017), digital methods for ethnography (Caliandro, 2018) – just to name a few.