CFP: TECNOSCIENZA: Italian Journal of Science & Technology Studies

Deadline: February 28, 2022

We are looking for submissions for a special issue of Tecnoscienza: Italian Journal of Science & Technology Studies (indexed in SCOPUS database) on “Cultural Machines: Unlocking the power of digital methods and computational techniques for understanding socio-cultural processes in digital environments” (see CfP below and in attachment).

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

Guest Editors:
Davide Bennato, Università degli Studi di Catania (Italy)
Alessandro Caliandro, Università degli Studi di Pavia (Italy)

Deadline for full paper submissions: February 28, 2022.

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. One of the most effective efforts in this direction has been developed by 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.

Notwithstanding the exceptional advancements in this direction, in our opinion, qualitative analysis of big data and the exploration of cultural processes within them are still underdeveloped (Pedersen 2021). On the one hand, so far one of the main (and probably the best) strategies to extract ‘thick description’ from big data consists in conducting manual analysis (via traditional qualitative techniques, such as qualitative content analysis or ethnographic observation) on small sample of digital data (Caliandro and Gandini 2017), as adopted for example in social media research on public opinion (Dragotto et al. 2020), fandoms (Arvidsson et al. 2016), brand culture (Schöps et al. 2020), micro-celebrity (Marwick and boyd 2011), or platform vernaculars (Gibbs et al. 2015). For how insightful and innovative these studies could be, they are nonetheless difficult to reproduce on a large scale. On the other hand, there exist computational approaches that focus precisely on ‘cultural data’, like cultural analytics (Manovich 2009), which use automated image recognition techniques to explore huge quantities of visual data (Manovich 2017). Such approach considers images as data, meaning that it focuses more on structural characteristic of images (e.g., colours, filters, resolution, etc.), rather than the content per se (Niederer and Colombo 2019).

Source: http://www.tecnoscienza.net/index.php/tsj