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Do.Re.Mee Seminar

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Roentgen Room 3.B3.SR01 + ZOOM Meetings
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You may follow the seminar online via ZOOM Meetings at the following link: https://unibocconi-it.zoom.us/j/96468907648

 

1st Paper

"Land distribution in pre-industrial Luxembourg: urban and rural areas comparison"

by Sonia Schifano

 

Abstract:

This paper uses the land distribution derived from the Maria Theresa cadastre in Luxembourg from 1766 to compare inequality levels between Luxembourg City and Dudelange, a rural municipality in the south of the country. The Gini coefficients for the two areas show a big difference in inequality. Dudelange, with a Gini coefficient of 0.87, seems to be much more unequal than Luxembourg City that registers a Gini coefficient on land distribution of 0.53. The professions of the declarants disclose two different societies. A high variety of professions associated to declarants from Luxembourg City belongs to commercial activities, while the majority of declarants in Dudelange has a profession related to the cultivation of land. The decomposition of the Gini coefficient by category of land and the difference in the percentages of net revenues associated to the deciles of the distributions reproduce the different socio-economic structures of these two places. Luxembourg City being a commercial centre and Dudelange being a rural feudal village.  

 

Bio:

Sonia obtained a PhD in Economics from the University of Luxembourg in October 2022. She is now a PostDoc working on the PRIN Project "Economic Development in Italy from the Middle Ages to today: a regional perspective". Her research revolves around inequality and intergenerational mobility in historical perspective.

 

2nd Paper

"Pro, Anti or Neutral? The unfolding of Covid-19 Vaccine Hesitancy on Reddit"

by Duilio Balsamo

Abstract:

Vaccines have been essential in containing the spread of infectious diseases, including COVID-19. However, the acceptance of such vaccines often evolves unexpectedly, due in part to online information and misinformation that polarize the public debate, potentially threatening a timely uptake.
To shed light on this issue, this study analyzes the unfolding of the discussion about Covid-19 vaccination on Reddit to understand its temporal evolution, the topics discussed, and the presence of echo chambers. We identified a network of subreddits where such discussions take place and trained machine learning classifiers to categorize over 26 million posts from January 2020 to August 2021 according to their Pro, Anti or Neutral stance about vaccination. 
Our preliminary results show that polarization about vaccination evolved with crucial events, such as vaccine approval and mandates. 
The study also reveals the main topics discussed on Reddit regarding vaccination, which indeed include vaccine mandates and concerns about safety and side effects. Surprisingly, we found that even in seemingly polarized subreddits, we can always find the presence of both opposing stances. This spontaneous and proactive engagement with users of a different stance lowers or eliminates the echo chamber effect, showing the existence of channels of communication which promote discussion that might lead to opinion change and could be targeted to increase vaccine uptake.
Our work provides insights to mitigate the proliferation of disinformation and constitutes the basis for a better understanding of the intricate phenomenon of vaccine hesitancy.
 

Bio:

Duilio Balsamo is a Postdoctoral Researcher at Bocconi University. His research focuses on Computational Social Science, i.e., understanding individual and collective behaviour using techniques such as Machine Learning, Network Science, and Natural Language Processing on digital data and social media.
During his PhD in Modeling and Data Science, he worked on addressing several aspects of the Opioid Epidemic in the US, such as Public Health Monitoring, Pharmacovigilance and Rehabilitation, leveraging Reddit data. He now works on using digital data to understand the behavioural determinants of vaccine uptake/hesitancy and to inform epidemiological models. 

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