: CALL FOR PAPERS - Special Issue - 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. 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).

As a matter of fact, it appears to be still quite arduous to take advantage of digital methods and computational techniques to automate the qualitative analysis and cultural interpretation of digital content on a very large scale - despite the attempts being made in this direction (among others Bennato 2021; Cambrosio et al. 2020).

Anyhow, it seems that new methodological possibilities, helpful to fill this gap, are now emerging, especially referring to two current ‘technical’ trends. First, several major digital platforms are starting to release ad hoc tools (seldom based on artificial intelligence) specifically meant to perform qualitative analysis on digital cultural objects as well as support researchers in their interpretation. These include Google Vision API, a machine learning-based image recognition toolkit (Mulfari et al. 2016) that provides modules to automatically analyse images based on: a) the content of the image itself (image-label); b) its specific ‘audiencing’ through references obtained from the web (image-web entities); and c) the sites of image circulation (image-domain) (Omena et al. 2021, 4). Or CrowdTangle – featured by Facebook – that in its dashboard includes a ‘meme search’ function (Fraser 2021). Not to mention GPT-3 (now a product of Microsoft): an artificial intelligence application that is able to autonomously write a text provided a specific question from the user (Schmelzer 2021) (e.g., ‘can I get a poem please?’ Or even better ‘can I get a research report on the topic X?’).

Second, the current processes of platformization of the web (Helmond 2015), and the subsequent platformization of culture (Duffy et al. 2019), put into motion a progressive standardization of web content as well as their production; a condition that (hypothetically) makes the tracing, analysis, and interpretation of cultural content easier and possibly scalable on larger datasets. A sheer example of such standardization of cultural processes and social dynamics within social media platforms is represented by Internet memes (Shifman 2014). Memes are “collections of standardized multimodal texts spreading rapidly across digital networks, which consist in user-created derivatives that stem from an original piece of content” (Caliandro and Anselmi 2021, 5; see also Milner, 2016). Memes are becoming very common and widespread means of interaction and communication among the massive and dispersed publics populating social media. Thanks to memes the dispersed users of social media can ‘simulate’ conversations on disparate topics (e.g., politics or social trends) (Rogers 2019). Furthermore, memes amount to be not only cultural objects or means of communication, but also provide the structural and semantic template that inform other forms of cultural production on social media. Consider for example the memetic logics informing the visual production of brands’ fans on Instagram (Caliandro and Anselmi 2021), or teenagers’ self-presentation strategies on TikTok (Zulli and Zulli 2020). Framed in this way, Internet memes are not mere cultural object to study, but also methodological resources that work as a heuristic to identify, follow, analyze, and interpret processes of platformization of culture in social media (Nieborg et al., 2020). In this sense, memes look very much like Tardian’s monads (Tarde/Clark, 2010), which, in the epistemological elaboration of Bruno Latour, can be considered as single cultural processes on a very large scale.

Given this emerging scenario and new methodological possibilities, we welcome contributors who are willing to address the methodological challenges outlined above. Possible questions to address (but not limited to these):

How to repurpose (commercial) artificial intelligence services and products for socio-anthropological research purposes?

  • How to use computational techniques to develop thick descriptions on big data?
  • Can we use digital methods to (methodologically) follow cultural objects, beyond the medium?
  • How to fruitfully combine ethnography with computational techniques?
  • Methods to address the platformization of cultural production in social media.
  • What is the nexus between platform logics, functioning, affordances, and public opinion?
  • The standardization of citizens and consumers’ behavior brought about by surveillance capitalism.
  • Innovative (quali-quanti) methods to study the Internet memes and meme culture.
  • The Internet memes as methodological heuristics to study cultural processes in digital environments.
  • Memetic practices on TikTok.
  • To what extent can memes be considered as monads?
  • To what extent Tarde’s theory of monads can be useful to study memes and other digital cultural objects populating digital environments as well as frame them as methodological resources?
  • Using sentiment analysis beyond brand reputation (e.g., for tracing, mapping, and understating complex socio-cultural phenomena like affective publics or digital affect cultures).
  • Methodological limits and possibilities of Google Vision API, Meme Spector, CrowdTangle, GPT-3, IBM Watson and other ‘cultural machines’.

Deadline for full paper submissions: February 11th, 2022.

Full papers (in English) with a maximum length of 8,000 words including notes and references should be sent as email attachments to tecnoscienza.specialissue@gmail.com and copied to the guest editors. Authors are encouraged to refer to the Tecnoscienza-Italian Journal of Science and Technology Studies website for instructions on submitting a paper, editing a manuscript, and for more information about the journal (http://www.tecnoscienza.net/index.php/tsj).

Full papers will be subject to a double blind peer review process. Accepted papers are expected to be published by 2022.

Expressions of interest and questions about expectations, requirements, etc. should be directed to the special issue editors:

Davide Bennato, davide.bennato@unict.it;

Alessandro Caliandro, alessandro.caliandro@unipv.it


Aipperspach, R., Rattenbury, T. L., Woodruff, A., Anderson, K., Canny, J. F., & Aoki, P. (2006). Ethno-mining: integrating numbers and words from the ground up. Electrical Engineering and Computer Sciences University of California at Berkeley Tech Report UCB/EECS-2006, 125. Retrieved from https://www2.eecs.berkeley.edu/Pubs/TechRpts/2006/EECS-2006-124.html.
Arvidsson, A., Caliandro, A., Airoldi, M., & Barina, S. (2016). Crowds and value. Italian directioners on Twitter. Information, Communication & Society, 19(7), 921-939.
Bennato, D. (2021). The Digital Traces' Diamond. A Proposal to Put Together a Quantitative Approach, Interpretive Methods, and Computational Tools. Italian Sociological Review, 11 (4S), 207-224. http://dx.doi.org/10.13136/isr.v11i4S.432
Caliandro A. & Gandini A. (2017), Qualitative Research in Digital Environments: A Research Toolkit. Routledge, London.
Caliandro, A. (2018). Digital methods for ethnography: Analytical concepts for ethnographers exploring social media environments. Journal of Contemporary Ethnography, 47(5), 551-578.
Caliandro, A., & Anselmi, G. (2021). Affordances-Based Brand Relations: An Inquire on Memetic Brands on Instagram. Social Media+ Society, 7(2), https://doi.org/10.1177/20563051211021367.
Cambrosio, A., Cointet, J. P., & Abdo, A. H. (2020). Beyond networks: Aligning qualitative and computational science studies. Quantitative Science Studies, 1(3), 1017-1024.
Cambrosio, A., Bourret, P., Rabeharisoa, V., & Callon, M. (2014). Big data and the collective turn in biomedicine. How should we analyze post-genomic practices?. TECNOSCIENZA: Italian Journal of Science & Technology Studies, 5(1), 11-42.
Dragotto, F., Giomi, E., & Melchiorre, S. M. (2020). Putting women back in their place. Reflections on slut-shaming, the case Asia Argento and Twitter in Italy. International Review of Sociology, 30(1), 46-70.
Duffy, B. E., Poell, T., & Nieborg, D. B. (2019). Platform practices in the cultural industries: Creativity, labor, and citizenship. Social Media+ Society, 5(4), https://doi.org/10.1177/2056305119879672.
Eykens, J., Guns, R., & Engels, T. C. (2021). Fine-grained classification of social science journal articles using textual data: A comparison of supervised machine learning approaches. Quantitative Science Studies, 2(1), 89-110.
Ford, H. (2014). Big Data and Small: Collaborations between ethnographers and data scientists. Big Data & Society, 1(2), pp. 1–3. doi: 2053951714544337.
Fraser, L. (2021). What data is CrowdTangle tracking? https://help.crowdtangle.com/en/articles/1140930-what-data-is-crowdtangle-tracking
Geiger, R. S., & Ribes, D. (2011). Trace ethnography: Following coordination through documentary practices. In 2011 44th Hawaii international conference on system sciences (pp. 1-10). IEEE.
Gibbs, M., Meese, J., Arnold, M., Nansen, B., & Carter, M. (2015). # Funeral and Instagram: Death, social media, and platform vernacular. Information, Communication & Society, 18(3), 255-268.
Giglietto, F., Rossi, L., & Bennato, D. (2012). The open laboratory: Limits and possibilities of using Facebook, Twitter, and YouTube as a research data source. Journal of technology in human services, 30(3-4), 145-159.
Helmond, A. (2015). The platformization of the web: Making web data platform ready. Social Media+ Society, 1(2), 2056305115603080.
Latour, B. (2010). Tarde’s idea of quantification (pp. 161-178). Routledge.
Latour, B., Jensen, P., Venturini, T., Grauwin, S., & Boullier, D. (2012). ‘The whole is always smaller than its parts’–a digital test of Gabriel Tardes' monads. The British journal of sociology, 63(4), 590-615.
Lewis, S. C., Zamith, R. and Hermida, A. (2013). Content analysis in an era of Big Data: A hybrid approach to computational and manual methods. Journal of Broadcasting & Electronic Media, 57(1), pp. 34–52.
Manovich, L., (2009), How to Follow Global Digital Cultures, or Cultural Analytics for Beginners, http://manovich.net/index.php/projects/how-to-follow-global-digital-cultures
Manovich, L. (2017). Instagram and contemporary image. Manovich.net. http://manovich.net/index.php/projects/instagram-and-contemporary-image.
Marres, N. (2015). Why map issues? On controversy analysis as a digital method. Science, Technology, & Human Values, 40(5), 655-686.
Marres, N. and Gerlitz, C. (2015). Interface methods: renegotiating relations between digital social research, STS and sociology. The Sociological Review, 64(1), pp. 21–46.
Marwick, A., & Boyd, D. (2011). To see and be seen: Celebrity practice on Twitter. Convergence, 17(2), 139-158.
Milner, R. M. (2016). The world made meme: Public conversations and participatory media. MIT Press
Nieborg, D. B., Duffy, B. E., & Poell, T. (2020). Studying platforms and cultural production: Methods, institutions, and practices. Social Media+ Society, 6(3), https://doi.org/10.1177/2056305120943273
Niederer, S. (2016). Networked content analysis: The case of climate change. (Doctoral dissertation, Universiteit van Amsterdam).
Niederer, S., & Colombo, G. (2019). Visual methodologies for networked images: Designing visualizations for collaborative research, cross-platform analysis, and public participation. Diseña, 14, 40–67.
Omena, J. J., Pilipets, E., Gobbo, B., & Chao, J. (2021). The Potentials of Google Vision API-based Networks to Study Natively Digital Images. Diseña, (19), Article.1. https://doi.org/10.7764/disena.19.Article.1
Pedersen, A. (2021). Machine Anthropology, Big Data & Society, https://journals.sagepub.com/page/bds/collections/machineanthropology?M_BT=54541027589991.
Rieder, B., Matamoros-Fernández, A., & Coromina, Ò. (2018). From ranking algorithms to ‘ranking cultures’ Investigating the modulation of visibility in YouTube search results. Convergence, 24(1), 50-68.
Rogers, R. (2009). The End of the Virtual. Amsterdam: Amsterdam University Press.
Rogers, R. (2019). Doing digital methods. SAGE
Schmelzer, R. (2021). GPT-3, https://searchenterpriseai.techtarget.com/definition/GPT-3
Schöps, J. D., Kogler, S., & Hemetsberger, A. (2020). (De-) stabilizing the digitized fashion market on Instagram–dynamics of visual performative assemblages. Consumption, Markets & Culture, 23(2), 195-213.
Shifman, L. (2014). The cultural logic of photo-based meme genres. Journal of Visual Culture, 13(3), 340–358.
Tarde, G. and Clark, T.N.(2010). Gabriel Tarde on communication and social influence: Selected papers. University of Chicago Press.
Venturini, T. (2010). Diving in magma: how to explore controversies with actor-network theory. Public understanding of science, 19(3), 258-273.
Venturini, T. (2012). Building on faults: how to represent controversies with digital methods. Public understanding of science, 21(7), 796-812.
Venturini, T., & Guido, D. (2012). Once upon a text: an ANT tale in text analysis. Sociologica, 6(3), 0-0.
Venturini, T., Jacomy, M., & Jensen, P. (2021). What do we see when we look at networks: Visual network analysis, relational ambiguity, and force-directed layouts. Big Data & Society, 8(1), 20539517211018488.
Vertesi, J., & Ribes, D. (2019). digitalSTS. Princeton University Press.
Zulli, D., & Zulli, D. J. (2020). Extending the Internet meme: Conceptualizing technological mimesis and imitation publics on the TikTok platform. New Media & Society, 1461444820983603.

ISSN: 2038-3460