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‘No farmers, no food’: a sentiment analysis of the 2024 farmers’ protests in Italy

Online First
  • Giampiero Mazzocchi,
  • Marco Vassallo,
  • Giuliano Gabrieli,
  • Roberto Henke
DOI: https://doi.org/10.36253/rea-15468

Published 2024-10-14

Keywords

  • farmers’ protests,
  • agriculture,
  • social media,
  • European Union,
  • sentiment analysis,
  • Italy
    • ...More
    Less

How to Cite

Mazzocchi, G., Vassallo, M., Gabrieli, G., & Henke, R. (2024). ‘No farmers, no food’: a sentiment analysis of the 2024 farmers’ protests in Italy. Italian Review of Agricultural Economics (REA). https://doi.org/10.36253/rea-15468

Copyright (c) 2024 Giampiero Mazzocchi, Roberto Henke, Marco Vassallo, Giuliano Gabrieli

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Starting in January 2024, street protests by farmers’ groups spread in several European countries. The demands, which started from rather specific aspects, have broadened to involve economic, environmental and geo-political considerations, calling the already weakened European Green Deal even more into question. Starting from an analysis of the motivations for the protests and the responses provided by national governments and European institutions, the article tracks the main arguments that characterized the motivations of the so-called ‘tractor protests’, through the methodology of Sentiment Analysis applied to the social network of accounts (specifically, X) relating to different categories of subjects interested in the debate. The results indicate a generally positive sentiment, characterised by trust and anticipation, suggesting potential for improving the relationship between society, institutions, and the conditions of farmers. Farmers remain at the centre of the debate, which focused on two key areas: the economic and competitive conditions of agricultural businesses, and the compatibility and economic sustainability of the environmental regulations embedded in European policies. The research revealed that the demands were somewhat fragmented and inconsistent. Nevertheless, the protests, although short-lived, had a significant impact by prompting European institutions to steer environmental and agricultural policies in new directions. Additionally, the research highlights that innovative investigative methods can be effectively applied to examine the interplay between the technicalities of public policies and collective perceptions.

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References

  1. Antošová I., Hazuchová N., Stávková J. (2021). Income situation of agricultural households of EU countries. Agricultural Economics  Czech, 67(4): 121-128. DOI: https://doi.org/10.17221/474/2020-AGRICECON.
  2. Cagliero R., Vassallo M., Pierangeli F., Pupo D’Andrea M.R., Monteleone A., Camaioni B., Tarangioli S. (2023). The Common Agricultural Policy 2023-27. How do Member States implement the new delivery model? Italian Review of Agricultural Economics, 78(1): 49-66. DOI: https://doi.org/10.36253/rea-14318.
  3. Cambria E., Das D., Bandyopadhyay S., Feraco A. (2017). A Practical Guide to Sentiment Analysis. Springer, 2017.
  4. Clark J. (2006). The institutional limits to multifunctional agriculture: subnational governance and regional systems of innovation. Environment and Planning C: Government and Policy, 24: 331-349. DOI: https://doi.org/10.1068/c053.
  5. De Castro P. (2020). The Common Agricultural Policy 2021-2027: a new history for European agriculture. Italian Review of Agriculture Economics (REA), 75(3): 5-12. DOI: https://doi.org/10.13128/rea-12703.
  6. Ferroni F. (2024). Il dialogo strategico sul futuro dei sistemi agroalimentari nell’Unione europea tra sostenibilità economica, ambientale e sociale. AE – Agricoltura Alimentazione Economia Ecologia, n. 1/2024. ISSN 2036-9948.
  7. Finger R., El Benni N. (2021). Farm income in European agriculture: new perspectives on measurement and implications for policy evaluation. European Review of Agricultural Economics, 48(2): 253-265. DOI: https://doi.org/10.1093/erae/jbab011.
  8. Finger R., Fabry A., Kammer M., Candel J., Dalhaus T., Meemken E.M. (2024). Farmer Protests in Europe 2023-2024. EuroChoices. DOI: https://doi.org/10.1111/1746-692X.12452.
  9. Gupta V., Lehal G.S. (2009). A Survey of Text Mining Techniques and Applications. Journal of Emerging Technologies in Web Intelligence, 1(1). DOI: https://doi.org/10.4304/jetwi.1.1.60-76.
  10. Henke R. (eds.). Verso il riconoscimento di un’agricoltura multifunzionale. Teorie, politiche, strumenti. Edizioni Scientifiche Italiane, 2004.
  11. Henke R., Benos T., De Filippis F., Giua M., Pierangeli F., Pupo D’Andrea M.R. (2018). The new Common Agricultural Policy: Ηow do Member States respond to flexibility? Journal of Common Market Studies, 56(2): 403-419. DOI: https://doi.org/10.1111/jcms.12607.
  12. Henke R., Sardone R. (2022). The 7th Italian Agricultural Census: new directions and legacies of the past. Italian Review of Agricultural Economics, 77(3): 67-75. DOI: https://doi.org/10.36253/rea-13972.
  13. Herring R.J. (2015). How is food political? Market, state, and knowledge. The Oxford handbook of food, politics, and society, p. 1-28.
  14. Hoekstra E., Fur H. (2024). Gli Scioperi degli Agricoltori Costringono l’UE a Rivedere gli Obiettivi Climatici per il 2040. GreenMarked, www.greenmarked.it.
  15. Kanter D.R., Musumba M., Wood S.L.R., Palm C., Antle J., Balvanera P., Dale. V.H., Havlik P., Kline K.L., Scholes R.J., Thornton P., Tittonell P., Andelman S. (2018). Evaluating agricultural trade-offs in the age of sustainable development. Agricultural Systems, 163: 73-88. DOI: https://doi.org/10.1016/j.agsy.2016.09.010.
  16. Illia L., Sonpar K., Bauer M.W. (2014). Applying Cooccurrence Text Analysis with ALCESTE to Studies of Impression Management. British Journal of Management, 25: 352-372. DOI: https://doi.org/10.1111/j.1467-8551.2012.00842.x.
  17. Lang T. (1999). Food Policy for the 21st Century: Can It Be Both Radical and Reasonable? In: Mustafa K., MacRae R., Mougeot L.J.A., Welsh J., For Hunger-Proof Cities. International Development Research Centre, Ottawa, Canada. ISBN: 0-88936-882-1.
  18. Liu, B. (2015). Sentiment Analysis: Mining Opinions, Sentiments, and Emotions. Cambridge University Press.
  19. Mandják T., Lavissière A., Hofmann J., Bouchery Y., Lavissière M.C., Faury O., Sohier R. (2019). Port marketing from a multidisciplinary perspective: A systematic literature review and lexicometric analysis. Transport Policy, 84: 50-72. DOI: https://doi.org/10.1016/j.tranpol.2018.11.011.
  20. Matthews A. (2024/a). What is actually happening with agricultural incomes? www.capreform.eu
  21. Matthews A. (2024/b). The origins and dimensions of protests. Intereconomics, 59(2): 83-87. DOI: https://doi.org/10.2478/ie-2024-0018.
  22. Mazzocchi G., Giarè F., Sardone R., Manetti I., Henke R., Giuca S., Borsotto P. (2023). Food (di)lemmas: disentangling the Italian Local Food Policy narratives. Italian Review of Agricultural Economics, 78(3). DOI: https://doi.org/10.36253/rea-14511.
  23. Mohammad S., Turney P. (2013). Crowdsourcing a Word-Emotion Association Lexicon. Computational Intelligence, 29(3): 436-465. DOI: https://doi.org/10.1111/j.1467-8640.2012.00460.x.
  24. Neogi A.S., Garg K.A., Mishra R.K., Dwivedi Y.K. (2021). Sentiment analysis and classification of Indian farmers’ protest using twitter data. International Journal of Information Management Data Insights, 1(2), 100019. DOI: https://doi.org/10.1016/j.jjimei.2021.100019.
  25. Pierangeli F., Henke R., Pupo D’Andrea M.R., Scardera A. (2024). Effetti della PAC per i divari territoriali. 15a Conferenza Nazionale di Statistica: La statistica ufficiale al tempo dell’intelligenza artificiale, 3-4 luglio Roma, ISTAT.
  26. Ratinaud P. (2014). IRAMUTEQ: Interface de R pour les Analyses Multidimensionnelles de Textes et de Questionnaires [Computer software].
  27. R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Vienna, Austria. https://www.R-project.org/.
  28. Singh A., Kalra N., Singh A., Sharma S. (2022). Sentiment analysis of Twitter data during Farmers’ Protest in India through Machine Learning. International Conference on Computer Science and Software Engineering (CSASE), Duhok, Iraq, 2022, pp. 121-126. DOI: https://doi.org/10.1109/CSASE51777.2022.9759767.
  29. Sotte, F. (1997). Per un nuovo patto sociale tra gli agricoltori e la società. QA – La Questione Agraria, 65: 7-15.
  30. Sotte, F. (2023). La politica agricola europea. Storia e analisi. Firenze University Press.
  31. Sree Sresta R.S., Pavan Kumar C.S., Roshini S. (2024). A Comprehensive Study of Farmer’s Protests Through Advanced Sentiment Analysis. International Conference on Inventive Computation Technologies (ICICT), Lalitpur, Nepal, pp. 18-28. DOI: https://doi.org/10.1109/ICICT60155.2024.10545018.
  32. Strohschneider P. (chair) (2024). Strategic Dialogue on the future of EU agriculture - A shared prospect for farming and food in Europe. European Commission, Brussels.
  33. Tiwari D., Nagpal B. (2022). KEAHT: A Knowledge-Enriched Attention-Based Hybrid Transformer Model for Social Sentiment Analysis. New Gener. Comput. 40: 1165-1202. DOI: https://doi.org/10.1007/s00354-022-00182-2.
  34. Van Huylenbroeck G., Vandermeulen V., Mettepenningen E., Verspecht A. (2007). Multifunctionality of Agriculture: A Review of Definitions, Evidence and Instruments. Living Reviews in Landscape Research, 1(3). http://creativecommons.org/licenses/by-nc-nd/2.0/de/.
  35. X Developer Platform (2024). X APIv2 Basic. https://developer.x.com/en/docs/twitter-api.
  36. Younis E.M.G. (2015). Sentiment Analysis and Text Mining for Social Media Microblogs using Open-Source Tools: An Empirical Study. International Journal of Computer Applications, 112(5). DOI: https://doi.org/10.5120/19665-1366.

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