The "Green Pass" Controversy in the Italian Twittersphere: a Digital Methods Mapping


In this paper we developed a digital methods mapping of the controversy arises from the adoption of the so-called "Green Pass" in Italy Adopting an "agnostic" approach to our object of study, we used a well-established research design: namely, to collect all the tweets that contain words related to conversations about the green pass in Italy (e.g.: green pass, #greenpass). In this way, the sample collected amounts to 4.307.487 tweets, published between June 15, 2021, and December 15, 2021. To bring out the "voices" of the different actors involved in the controversy we adopted a quali-quantitative approach: on the one hand, by means of computational techniques, we reconstructed the structural relations in which the actors are involved and its evolution over time; on the other hand, by means of content analysis we enriched our map with an interpretation of the discourse surrounding the controversy. Finally, these cartographic results are discussed considering the Italian media system functioning, in order to understand how its conformation may have influenced the public debate concerning the green pass.

DOI Code: 10.1285/i20356609v15i3p549

Keywords: Public Debate; Controversy Mapping; Digital Methods; Covid-19; Green Pass


Anderson, W. (2021), “The model crisis, or how to have critical promiscuity in the time of Covid-19”, Social Studies of Science, 51(2), 167-188.

Bastian, M., Heymann, S. and Jacomy, M. (2009), “Gephi: an open source software for exploring and manipulating networks”, Proceedings of the international AAAI conference on web and social media. 3(1), 361-362.

Blondel, V. D., Guillaume, J. L., Lambiotte, R. and Lefebvre, E. (2008), “Fast unfolding of communities in large networks”, Journal of statistical mechanics: theory and experiment, 2008(10), P10008.

Boccia Artieri, G., Greco, F. and La Rocca, G. (2021), “Lockdown and Breakdown in Italians' Reactions on Twitter during the First Phase of Covid-19”, Partecipazione e conflitto, 14(1), 261-282.

Boydstun, A. E., Bevan, S. and Thomas III, H. F. (2014a), “The importance of attention diversity and how to measure it”, Policy Studies Journal, 42(2), 173-196.

Boydstun, A. E., Hardy, A. and Walgrave, S. (2014b), “Two faces of media attention: Media storm versus non-storm coverage”, Political Communication, 31(4), 509-531.

Bracciale, R., Martella, A. and Visentin, C. (2018), “From super-participants to super-echoed: participation in the 2018 italian electoral Twittersphere”, Partecipazione e conflitto, 11(2), 361-393.

Brusselaers, N. et al. (2022), “Evaluation of science advice during the COVID-19 pandemic in Sweden”, Humanities and Social Sciences Communications, 9(1), 1-17.

Caliandro, A., Anselmi, G. and Sturiale, V. (2020), “Fake news, Covid-19 e Infodemia: un esempio di ricerca sociale in real-time su Twitter”, Mediascapes journal, (15), 174-188.

Castaldo, M., Venturini, T., Frasca, P. and Gargiulo, F. (2022), “Junk news bubbles modelling the rise and fall of attention in online arenas”, New Media & Society, 24(9), 2027-2045.

Cinelli, M. et al. (2021), “The echo chamber effect on social media”, Proceedings of the National Academy of Sciences, 118(9).

Crupi, G. et al. (2022), “Echoes through Time: Evolution of the Italian COVID-19 Vaccination Debate”, Proceedings of the International AAAI Conference on Web and Social Media (Vol. 16, pp. 102-113).

Downs, A. (1972), “Up and down with ecology: The issue-attention cycle”, The public, 28, 38-50.

Flinders, M. (2020), “Gotcha! Coronavirus, Crisis and the Politics of Blame Game”, Political Insight, 11(2), 22-25.

Flinders, M. (2021), “Democracy and the Politics of Coronavirus: Trust, Blame and Understanding”, Parliamentary Affairs, 74(2), 483-502.

Gallè, F. et al. (2021), “Acceptance of COVID-19 Vaccination in the Elderly: A Cross-Sectional Study in Southern Italy”, Vaccine, 9, 12-22.

Gallotti, R. et al. (2020), “Assessing the risks of ‘infodemics’ in response to COVID-19 epidemics”, Nature Human Behaviour, 4(12), 1285-1293.

Goldhaber, M. H. (1997), “The attention economy and the net”, First Monday, 2(4). doi: 10.5210/fm.v2i4.519.

Guidi, S., Romano, A. and Sotis, C. (2021), “Depolarizing the Covid-19 Vaccine Passport”, Yale Law Journal, 3850152.

Hallin, D. and Mancini, P. (2004), Comparing Media Systems. Three Models of Media and Politics. Cambridge: Cambridge University Press.

Hilgartner, S. and Bosk, C. L. (1988), “The rise and fall of social problems: A public arenas model”, American Journal of Sociology, 94(1), 53-78.

Islam, M.S. et al. (2020), “COVID-19-Related Infodemic and Its Impact on Public Health: A Global Social Media Analysis”, The American Journal of Tropical Medicine and Hygiene, 103(4), 1621-1629.

Jacomy, M., Venturini, T., Heymann, S. and Bastian, M. (2014), “ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software”, PloS one, 9(6), e98679.

Kim, E., Shepherd, M. and Clinton, J.D. (2020), “The effect of big-city news on rural America during the COVID-19 pandemic”, PNAS, 117(36), 22009-22014.

Krackhardt, D. and Stern, R. N. (1988), “Informal networks and organizational crises: An experimental simulation”, Social psychology quarterly, 123-140.

Kwak, H., Lee, C., Park, H., & Moon, S. (2010), “What is Twitter, a social network or a news media?”, Proceedings of the 19th international conference on the World wide web (pp. 591-600).

Latour, B. (1987), Science in action: How to follow scientists and engineers through society. Cambridge: Harvard university press.

Latour, B. (2007), Reassembling the social: An introduction to actor-network-theory. Oxford: Oxford University Press.

Leng, Y. et al. (2021), “Misinformation During the COVID-19 Outbreak in China: Cultural, Social and Political Entanglements”, IEEE Transactions on Big Data, 7(1), 69-80.

Marres, N. S. (2005), No issue, no public: Democratic deficits after the displacement of politics,

Amsterdam: Ipskamp Printpartners.

Marres, N. and Weltevrede, E. (2013), “Scraping the social? Issues in live social research”, Journal of cultural economy, 6(3), 313-335.

Marres, N. (2015), “Why map issues? On controversy analysis as a digital method”, Science, Technology, & Human Values, 40(5), 655-686.

Marres, N. and Moats, D. (2015), “Mapping controversies with social media: The case for symmetry”, Social Media+ Society, 1(2).

Metaxas, P., Mustafaraj, E., Wong, K., Zeng, L., O'Keefe, M., & Finn, S. (2015), “What do retweets indicate? Results from user survey and meta-review of research”, Proceedings of the international AAAI conference on web and social media (Vol. 9, No. 1, pp. 658-661).

Miconi, A. and Risi, E. (2022), “Framing pandemic news. Una ricerca sulla rappresentazione del Covid-19 nei news media italiani”, Problemi dell'informazione, 47(1), 31-61.

Moccia, G. et al. (2022), “Vaccine Hesitancy and the Green Digital Pass: A Study on Adherence to the Italian COVID-19 Vaccination Campaign”, International Journal of Environmental Research and Public Health, 19(5), 2970.

Munk, A. K. (2019), “Four styles of quali-quantitative analysis: Making sense of the new Nordic food movement on the web”, Nordicom Review, 40(s1), 159-176.

Noack, A. (2009), “Modularity clustering is a force-directed layout”, Physical Review E, 79 (2).

Pilati, F. and Anselmi, G. (2022), “The AstraZeneca affair. A digital methods mapping of the Covid-19 vaccination controversy in the Italian hybrid media system”, Tecnoscienza, 2/22.

Rojas, J. (2020) Coronavirus: Lockdowns drive record growth in Twitter usage, Sky News, 23 July 2020.

Russo, L. and Valbruzzi, M. (2022), “The impact of the pandemic on the Italian party system. The Draghi government and the “new” polarization”, Contemporary Italian Politics, 14(2), 172-190.

Sacco, P. L., Gallotti, R., Pilati, F., & De Domenico, M. (2021), “Emergence of knowledge communities and information centralization during the COVID-19 pandemic”, Social Science & Medicine, 285, 114215.

Saltelli, A. et al. (2020), “Five ways to ensure that models serve society: a manifesto”, Nature, 582.

Sol Hahn, P., Chinn, S. and Soroka, S. (2020), “Politicization and Polarization in COVID-19 News Coverage”, Science Communication, 42 (5), 679-697.

Spitale, G., Biller-Andorno, N. and Germani, F. (2022), “Concerns Around Opposition to the Green Pass in Italy: Social Listening Analysis by using a Mixed Methods Approach”, Journal of Medical Internet Research, 24(2):e34385.

Venturini, T. (2010), “Diving in magma: how to explore controversies with actor-network theory”, Public understanding of science, 19(3), 258-273.

Venturini, T. and Latour, B. (2010), “The Social Fabric: Digital Footprints and Quali-Quantitative Methods”, Proceedings of futur en Seine, 87-101.

Venturini, T. (2012), “Building on faults: how to represent controversies with digital methods”, Public understanding of science, 21(7), 796-812.

Venturini, T. et al. (2014), “Three maps and three misunderstandings: A digital mapping of climate diplomacy”, Big Data & Society, 1(2)

Venturini, T. et al. (2015), “Designing controversies and their publics”, Design Issues, 31(3), 74-87.

Venturini, T. (2019), “From fake to junk news: The data politics of online virality”, in D. Bigo, E. Isin, E. Ruppert (eds.), Data Politics, London: Routledge, pp. 123-144.

Venturini, T., Munk, A. K. and Jacomy, M. (2019), “Actor-Network versus Network Analysis versus Digital Networks: Are We Talking about the Same Networks?”, in J. Vertesi and D. Ribes (eds.), digitalSTS, Princeton, Princeton University Press, pp. 510-524.

Venturini, T., Jacomy, M. and 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.

Venturini, T. and Munk, A. K. (2021), Controversy Mapping: A Field Guide. London: Polity Books.

Weingart, P. (1999), “Scientific expertise and political accountability: paradoxes of science in politics”, Science and public policy, 26(3), 151-161.

Full Text: PDF


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribuzione - Non commerciale - Non opere derivate 3.0 Italia License.